Springer Tracts in Electrical and Electronics Engineering

Bharat Bhushan
Sudhir Kumar Sharma
Raghvendra Kumar
Ishaani Priyadarshini Editors

5G and
Beyond
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Bharat Bhushan · Sudhir Kumar Sharma ·
Raghvendra Kumar · Ishaani Priyadarshini
Editors

5G and Beyond
Editors
Bharat Bhushan Sudhir Kumar Sharma
Department of Computer Science Department of Computer Science
and Engineering, School of Engineering Institute of Information Technology
and Technology and Management
Sharda University New Delhi, India
Greater Noida, India
Ishaani Priyadarshini
Raghvendra Kumar School of Information
Department of Computer Science University of California, Berkeley
and Engineering Berkeley, CA, USA
GIET University
Gunupur, Odisha, India

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Preface

The fifth-generation (5G) networks continue to emerge through evolved technolo-

gies and new models of cellular operations. The key advances over preceding gener-
ations include multi-interface access, spectrum extensions, network softwarization
powered by Network Function Virtualization (NFV), Software-Defined Networking
(SDN), etc. Recent years have witnessed an unprecedented rise in the use of portable
computing devices such as laptops, tablets, smartphones, and machine-type devices
interconnected via Internet of Things (IoT). The emergence of such innovative
online services and mobile applications poses unprecedented challenges to the 5G
networks. Therefore, there is a need for novel solutions (such as New Radio (NR)
interface in millimeter-wave (mmWave) bands, beamforming, and massive Multiple-
Input Multiple-Output (massive MIMO)) to optimize the use of finite radio spec-
trum resources. Further, the future networks are expected to meet several require-
ments such as increased network capacity, latency, energy efficiency, reliability,
heterogeneity, and Quality-of-Service (QoS) demands.
Owing to the exponential rise in the number of devices being connected to the
Internet, it is anticipated that the wireless data traffic might increase to 10000-fold by
the year 2030. Predictions evidently indicate the skyrocketing demand on data traffic
and applications for machine-type communication such as smart cities, industries,
healthcare monitoring and self-driving vehicles along with traditional human-centric
communications. Such coexistence of such machine-type and human-centric services
will tend to make the next-generation wireless networks more complex and diverse.
In order to provide better support to the IoT applications, numerous challenges such
as advanced signal processing, network resource allocation and network architec-
tures need to be overcome in 5G and beyond. As 5G research is maturing toward
a global standard, there is a need for the development of beyond-5G solutions, that
is, 6G and beyond. 6G might entail technologies that are beyond the scope of 5G,
especially the way in which data is collected, processed, and transmitted within the
wireless network. These next-generation wireless networks must support low latency,
ultra-reliable communication, and intelligently managed IoTs or even future IoT
devices in a highly dynamic real-time environment. Furthermore, the paradigm shift
to machine-oriented communications from people-centric communication makes the

v
vi Preface

future wireless networks even more complex. Therefore, enabling the vision requires
addressing a myriad of practical and theoretical challenges.
This book aims to highlight the coming surge of 5G network-based applications
and predicts that the centralized networks and its current capacity will be incapable
to meet the demands. The main aim of this book is to outline the major benefits
as well as challenges associated with integration of 5G networks with varied appli-
cations. Further, the book aims to gather and investigate the most recent 5G-based
research solutions that handle the security and privacy threats while considering
the resource-constrained wireless devices. The information, applications and recent
advances discussed in this book will serve to be of immense help for practitioners,
database professional, and researchers.

Greater Noida, India Bharat Bhushan

New Delhi, India Sudhir Kumar Sharma
Gunupur, India Raghvendra Kumar
Berkeley, USA Ishaani Priyadarshini
Contents

1 Evolution of Next-Generation Communication Technology . . . . . . . . 1

Riya Sil and Ritwesh Chatterjee
2 Third Industrial Revolution: 5G Wireless Systems, Internet
of Things, and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Anwesha Das, Aninda Chowdhury, and Riya Sil
3 Network Architectures and Protocols for Efficient
Exploitation of Spectrum Resources in 5G . . . . . . . . . . . . . . . . . . . . . . . 45
Kande Archana, V. Kamakshi Prasad, M. Ashok,
and G. R. Anantha Raman
4 Wireless Backhaul Optimization Algorithm in 5G
Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Astha Sharma, Mukesh Soni, Abhaya Nand,
Suryabhan Pratap Singh, and Sumit Kumar
5 Security Attacks and Vulnerability Analysis in Mobile
Wireless Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Ayasha Malik, Bharat Bhushan, Surbhi Bhatia Khan,
Rekha Kashyap, Rajasekhar Chaganti, and Nitin Rakesh
6 Utilities of 5G Communication Technologies for Promoting
Advancement in Agriculture 4.0: Recent Trends, Research
Issues and Review of Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Parijata Majumdar, Diptendu Bhattacharya, and Sanjoy Mitra
7 Security Attacks and Countermeasures in 5G Enabled
Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
A. K. M. Bahalul Haque, Tasfia Nausheen,
Abdullah Al Mahfuj Shaan, and Saydul Akbar Murad

vii
viii Contents

8 Energy Efficiency and Scalability of 5G Networks for IoT

in Mobile Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Smriti Sachan, Rohit Sharma, and Amit Sehgal
9 Security Services for Wireless 5G Internet of Things (IoT)
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Ayasha Malik, Veena Parihar, Bharat Bhushan,
Rajasekhar Chaganti, Surbhi Bhatia, and Parma Nand Astya
10 Securing the IoT-Based Wireless Sensor Networks in 5G
and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
N. Ambika
11 5G and Internet of Things—Integration Trends,
Opportunities, and Future Research Avenues . . . . . . . . . . . . . . . . . . . . 217
A. K. M. Bahalul Haque, Md. Oahiduzzaman Mondol Zihad,
and Md. Rifat Hasan
12 Post-Quantum Cryptographic Schemes for Security
Enhancement in 5G and B5G (Beyond 5G) Cellular Networks . . . . . 247
Saurabh Bhatt, Bharat Bhushan, Tanya Srivastava, and V. S. Anoop
13 Enhanced Energy Efficiency and Scalability in Cellular
Networks for Massive IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Husam Rajab and Tibor Cinkler
Correction to: Wireless Backhaul Optimization Algorithm in 5G
Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C1
Astha Sharma, Mukesh Soni, Abhaya Nand, Suryabhan Pratap Singh,
and Sumit Kumar
Editors and Contributors

About the Editors

Bharat Bhushan is an Assistant Professor of the Department of Computer Science

and Engineering (CSE) at the School of Engineering and Technology, Sharda Univer-
sity, Greater Noida, India. He is an alumnus of Birla Institute of Technology, Mesra,
Ranchi, India. He received his B.Tech. in Computer Science and Engineering in
2012, an M.Tech. in Information Security in 2015, and a Ph.D. in Computer Science
and Engineering in 2021 from Birla Institute of Technology, Mesra, India. He has
published over 80 research papers in various renowned International conferences and
Journals. He has also contributed over 25 book chapters and has edited 11 books. He
is a member of numerous renowned bodies such as IEEE, IAENG, CSTA, SCIEI,
IAE, and UACEE.

Sudhir Kumar Sharma is currently a Professor and Head of the Department of

Computer Science, Institute of Information Technology and Management affiliated
with GGSIPU, New Delhi, India. He has extensive experience of over 21 years
in the field of Computer Science and Engineering. He obtained his Ph.D. degree
in Information Technology in 2013 from USICT, Guru Gobind Singh Indraprastha
University, New Delhi, India. Dr. Sharma received his M.Tech. degree in Computer
Science & Engineering in 1999 from the Guru Jambheshwar University, Hisar, India,
and an M.Sc. degree in Physics from the University of Roorkee (now IIT Roorkee),
Roorkee, in 1997. His research interests are machine learning, data mining, and
security. Dr. Sharma has published over 60 research papers in various prestigious
International Journals and Conferences. He authored and edited 7 books in the fields
of IoT, WSN, blockchain, and cyber-physical systems.

Raghvendra Kumar is an Associate Professor in Computer Science and Engi-

neering Department at GIET University, India. He received B.Tech., M.Tech., and
Ph.D. in Computer Science and Engineering, India, and Postdoc Fellow from the
Institute of Information Technology, Virtual Reality, and Multimedia, Vietnam. He

ix
x Editors and Contributors

has published a number of research papers in international journals and conferences.

He also published 13 chapters in edited books published by renowned publishers.
His research areas are computer networks, data mining, cloud computing and secure
multiparty computations, theory of computer science, and design of algorithms. He
authored and edited 23 computer science books on IoT, data mining, biomedical
engineering, big data, and robotics.

Ishaani Priyadarshini is a lecturer at the School of Information, UC Berkeley, USA,

Ph.D., and Master’s Degree in Cybersecurity from the University of Delaware, USA.
Prior to that, she completed her Bachelor’s degree in Computer Science Engineering
and a Master’s degree in Information Security from Kalinga Institute of Industrial
Technology, India. She has authored several book chapters for reputed publishers and
is also an author of several publications for SCIE-indexed journals. As a certified
reviewer, she conducts peer reviews of research papers for prestigious publishers
and is a part of the Editorial Board for the International Journal of Information
Security and Privacy (IJISP). Her areas of research include cybersecurity, artificial
intelligence, and HCI.

Contributors

Abdullah Al Mahfuj Shaan Department of ECE, North South University, Dhaka,

Bangladesh
N. Ambika Department of Computer Science and Applications, St. Francis College,
Bangalore, India
G. R. Anantha Raman Department of Computer Science and Engineering, Malla
Reddy Institute of Engineering and Technology Maisammaguda, Hyderabad, Telan-
gana, India
V. S. Anoop Smith School of Business, Queen’s University, Kingston, ON, Canada
Kande Archana Research Scholar, Department of Computer Science and Engi-
neering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India;
Assistant Professor, Department of Computer Science and Engineering, Malla Reddy
Institute of Engineering and Technology Maisammaguda, Hyderabad, Telangana,
India
M. Ashok Department of Computer Science and Engineering, Malla Reddy Insti-
tute of Engineering and Technology Maisammaguda, Hyderabad, Telangana, India
Parma Nand Astya Department of Computer Science and Engineering, School of
Engineering and Technology (SET), Sharda University, Greater Noida, India
Editors and Contributors xi

A. K. M. Bahalul Haque LUT School of Engineering Science, LUT University,

Lappeenranta, Finland;
Department of ECE, North South University, Dhaka, Bangladesh
Saurabh Bhatt Department of Computer Science and Engineering, School of
Engineering and Technology, Sharda University, Greater Noida, India
Surbhi Bhatia Khan Department of Data Science, School of Science, Engineering
and Environment, University of Salford, Manchester, United Kingdom
Surbhi Bhatia College of Computer Science and Information Technology, King
Faisal University, Hofuf, Saudi Arabia
Diptendu Bhattacharya Department of Computer Science and Engineering,
National Institute of Technology, Agartala, Agartala, Tripura, India
Bharat Bhushan Department of Computer Science and Engineering, School of
Engineering and Technology, Sharda University, Greater Noida, India
Rajasekhar Chaganti University of Texas, San Antonio, USA
Ritwesh Chatterjee Adamas Tech Consulting, Bangalore, India
Aninda Chowdhury St. Placid’s School and College, Chittagong, Bangladesh
Tibor Cinkler Budapest University of Technology and Economics, Budapest,
Hungary
Anwesha Das Adamas University, Kolkata, India
V. Kamakshi Prasad Department of Computer Science and Engineering, Jawa-
harlal Nehru Technological University, Hyderabad, Telangana, India
Rekha Kashyap Noida Institute of Engineering and Technology (NIET), Greater
Noida, India
Sumit Kumar Indian Institute of Management, Kozhikode, India
Parijata Majumdar Department of Computer Science and Engineering, National
Institute of Technology, Agartala, Agartala, Tripura, India
Ayasha Malik Delhi Technical Campus (DTC), GGSIPU, Greater Noida, India
Sanjoy Mitra Department of Computer Science and Engineering, Tripura Institute
of Technology, Agartala, Agartala, Tripura, India
Saydul Akbar Murad Faculty of Computing, Universiti Malaysia Pahang, Pekan,
Malaysia
Abhaya Nand IIMT College of Management, Greater Noida, India
Tasfia Nausheen Department of ECE, North South University, Dhaka, Bangladesh
Md. Oahiduzzaman Mondol Zihad Department of Electrical and Computer Engi-
neering, North South University, Dhaka, Bangladesh
xii Editors and Contributors

Veena Parihar KIET Group of Institutions Delhi-NCR, Ghaziabad, India

Husam Rajab Budapest University of Technology and Economics, Budapest,
Hungary
Nitin Rakesh Department of Computer Science and Engineering, School of Engi-
neering and Technology, Sharda University, Greater Noida, India
Md. Rifat Hasan Department of Computer Science & Engineering, Fareast Inter-
national University, Dhaka, Bangladesh
Smriti Sachan Department of Electronics and Communication, SRM Institute of
Science and Technology, NCR Campus, Ghaziabad, India
Amit Sehgal School of Engineering and Technology, Sharda University, Greater
Noida, India
Astha Sharma GL Bajaj Institute of Technology and Management, Greater Noida,
India
Rohit Sharma Department of Electronics and Communication, SRM Institute of
Science and Technology, NCR Campus, Ghaziabad, India
Riya Sil Adamas University, Kolkata, India
Suryabhan Pratap Singh Institute of Engineering and Technology, Deen Dayal
Upadhyaya Gorakhpur University, Gorakhpur, India
Mukesh Soni Department of CSE, University Centre for Research & Development
Chandigarh University, Mohali, Punjab, India
Tanya Srivastava Department of Computer Science and Engineering, School of
Engineering and Technology, Sharda University, Greater Noida, India
Chapter 1
Evolution of Next-Generation
Communication Technology

Riya Sil and Ritwesh Chatterjee

Abstract In this technological era of wireless communication, various Internet

devices and Wi-Fi zone play a significant role in the fast growth of data usage.
The communication of people has been revolutionized by mobile communication
systems. The rapid increase in the number of users, higher data rate, and requirement
for higher bandwidth and voluminous data has become a challenge for Internet service
providers. All these requirements are expected to be met by the next-generation
communication network. The evolution of next-generation communication tech-
nology aims to emphasize the user terminal development that will provide access
to various technologies and combine several flows from numerous technologies.
In this paper, the authors have provided a detailed overview of the various wire-
less communication technologies and how it can be enhanced in future. A compar-
ative study of the different generations of communication technologies has been
done which includes 1G which has contented the elementary mobile voice, and 2G
which has familiarized us with capacity and coverage. The quest continued with
3G for high-speed data that in turn provided a true experience to mobile bandwidth
with 4G and finally the next-generation communication technology—5G. The varied
variety of telecommunication services such as advanced mobile services, with the
help of mobile and fixed networks, can be accessed with the help of the Fourth
Generation (4G). The technology in the Fifth Generation (5G) is more advanced,
and intelligent and it interconnects with the entire world. Moreover, in this paper,
the authors have discussed the various issues and challenges faced in the existing
cellular network, the limitation of conventional cellular systems, and the reason for
the need for next-generation communication technology. Also, a detailed study on
5G network communication has been deliberated.

Keywords Wireless communications · Fifth-generation communication · Mobile

broadband · Technology · Networks

R. Sil (B)
Adamas University, Kolkata 700040, India
e-mail: riyasil1802@gmail.com
R. Chatterjee
Adamas Tech Consulting, Bangalore 560070, India

© The Author(s) 2023 1

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_1
2 R. Sil and R. Chatterjee

Introduction

The rapid evolution of architecture, communication services, and technologies has

stimulated due to the demand for new applications, research innovations, and other
foremost possibilities for enhancements at various levels (Rappaport et al. 2014).
These changes are driving a fundamental shift in the process of designing and
delivering infrastructure and services. Communication systems have generally been
envisioned and run as centralized secure utility services, with fewer options for
specialization and customization, especially at the network edge (Holma et al.
2015). Supportive infrastructure is built and delivered consistently, according to
a set of specific designs, resulting in hardened structures that should last for many
years (Stallings 2007). In an environment where service requirements are constantly
changing, this method is somewhat limited. Traditional rigid designs make it difficult
to create, enhance, deploy, and customize services.
The constant increase in wireless user data usage, devices, and the desire for an
experience of improved quality have overall affected the advancement of cellular
network generations. At the end of 2020, there were over 50 billion linked devices
that were using cellular network services, which has resulted in a massive increase
in data traffic in comparison to 2014. On the other hand, the solutions are insuf-
ficient to address the aforementioned challenges (Bangerter et al. 2014). In brief,
the growth of 5G networks is aided by a rise in 3D (i.e., “D”ata, “D”evice, and
“D”ata transfer rate). The fifth-generation cellular networks precisely address and
highlight three main perspectives: (i) user-centric (i.e., deliver device connectivity
24 × 7, continuous communication assistance, and pleasing user experience); (ii)
provider-centric service (provide services that include sensors, intelligent transporta-
tion systems that are connected); (iii) network-operator-centric; and (iv) network-
operator (provide energy-efficient, programmable, low-cost, scalable). As a result,
the three key features described below are expected to appear in 5G networks: (i)
ubiquitous connectivity: A wide range of devices will be able to communicate and
deliver a uniform user experience. In fact, ubiquitous connectivity will allow for a
user-centric viewpoint. (ii) Zero latency: 5G networks should achieve zero latency
or very low latency on the order of one millisecond. Thus, zero latency will make
the service-provider-centric strategy a reality. (iii) Gigabit connection: To achieve
zero latency, a fast gigabit connection for rapid data transmission and reception, on
the gigabits/second to users and machines order, might be employed (Andrews et al.
2014; Khan et al. 2012; Adhikari 2008; Intelligence 2014).
The cellular wireless generation includes a shift in the service’s core foundation,
new frequency bands, backward-compatible, and non-backward transmission tech-
nologies. Since the initial move from the analog (1G) to the analog (2G) network in
1981, new generations (G) have appeared every 10 years, with 3G multimedia capa-
bility following in 2011. Recently, the wireless industry has experienced spectacular
growth. A distinct shift from fixed phones to mobile cellular phones has been noticed
from the beginning of the century. There were more than four times as many mobile
cellular subscribers as landline telephone lines at the end of 2010.
1 Evolution of Next-Generation Communication Technology 3

Manufacturers along with mobile network operators acknowledge the significance

of effective networks and efficient design. As a result, network design and optimiza-
tion services are increasingly becoming popular (Chen and Zhao 2014). 4G networks,
or next-generation mobile networks, are envisioned as a collection of heterogeneous
systems connected by a horizontal IP-centric architecture (3GPP 2015). These new
technologies, together with the aforementioned requirements, provide a number of
roadblocks to 5G development.
Table 1.1 compares the major characteristics and limitations of each generation
of communication technology (Ericsson 2015; Qualcomm Technologies Inc. 2014;
Huawei 2013; NTT Docomo 2015; Nokia Networks 2014).
The scope of this paper includes discussions related to the existing cellular network
and its limitations, 5G networks vision, proposed architectures, advantages, issues
related to implementation, applications, and a detailed discussion on next-generation
network. In Sect. 1.2, authors have discussed the existing cellular network and its
challenges. Section 1.3 explains the conventional cellular systems limitation. In the
next section, i.e., Sect. 1.4, the authors have provided a clear idea about the vision and
mission of 5G network. The architecture of the 5G network has been demonstrated
in Sect. 1.5. Next, Sect. 1.6 explains the applications of next-generation network.
Finally, in Sect. 1.7, the overall conclusion and future scope are discussed.

Table 1.1 Generations of communication technology

Sl. Year Generation Key features Limitations
no
1 1980s 1G • Voice signals Low security
• Analog cellular phones
• NMT, AMPS
2 1990s 2G • Text messages Low internet support
• Voice signal
• Digital signal
• GSM, TDMA, CDMA,
GPRS, EDGE
3 1998s 3G • Voice signals Low high-speed internet support
• Data signals
• Video signals
• Wireless and fixed
internet access
• W-CDMA, UMTS
4 2008s 4G • Higher data rate Does not help with the connected devices
• Interoperability are more than 50 billion
protocol
• Mobile IP
5 2019s 5G • Increased connectivity
• IoT
• Low latency
• Greater transmission
speed
4 R. Sil and R. Chatterjee

Existing Cellular Network and Its Challenges

As per reports related to the statistics of wireless network, review reveals that global
mobile traffic had increased roughly by 70% in 2014. 26% of smartphones (out of
all mobile devices worldwide) account for 88% of total mobile data traffic (Samsung
Electronics Co, 2015). As the number of people using smartphones grow, so does the
amount of mobile video traffic. Video traffic has accounted for over half of all mobile
traffic since 2012. As per reports, the typical mobile user had downloaded 1 terabyte
of data per year in 2020. In today’s 4G LTE cellular systems, supporting this massive
and quick rise in data demand and connection is a huge challenge. To increase capacity
and data rates, the LTE cellular network is pursuing several research and development
options such as MIMO, tiny cells, HetNets, multiple antennas, and coordinated multi-
point transmission. However, this current traffic surge is unlikely to persist in the long
run. As a result, the key problem in mobile broadband communications is to meet
the exponential growth in user and traffic capacity (5G-Infrastructure Public–Private
Partnership 2013; Osseiran et al. 2014; European Commission 2011).

First Generation

The first generation of mobile systems depends on analog transmission for voice
services. NTT or Nippon Telephone and Telegraph in Tokyo, Japan, launched the
world’s foremost cellular system in the year 1979. After 2 years, the cellular period
came in Europe. The Advanced Mobile Phone System (AMPS) was established
in 1982 in the United States. Nordic Mobile Telephones (NMT) and Total Access
Communication Systems (TACS) are the two most widespread analog systems (Kall-
nichev 2001). For AMPS, the Federal Communications Commission (FCC) allocated
a 40-MHz bandwidth in the 800 to 900 MHz frequency range. As a result, for AMPS,
a seven-cell reuse pattern has been established. The Frequency Modulation (FM)
technology is used by AMPS and TACS for radio transmission. Frequency division
multiple access technology is used to multiplex traffic (Lai et al. 2015; Agyapong
et al. 2014; Lara et al. 2014).

Second Generation

Second generation (2G) of mobile system was launched around the end of the
1980s. In contrast to the first-generation (1G) systems, the 2G systems use digital
multiple access technologies which includes TDMA (time division multiple access)
and CDMA (code division multiple access). Thus, 2G systems outperformed first-
generation systems in terms of data services, spectrum effectiveness, and roaming
capabilities (Cho et al. 2014). In the United States, there were three distinct streams of
1 Evolution of Next-Generation Communication Technology 5

development for second-generation digital cellular networks. The first digital system,
the IS-54 (North America TDMA Digital Cellular), launched in 1991, and a second
version IS-136 with expanded services was produced in 1996. Meanwhile, IS-95
(CDMA One) was adopted in 1993 (Arslan et al. 2015). 2G connection is often used
to link GSM (global system for mobile) services. The most general packet radio
service (GPRS) and GSM are commonly used to power 2.5G networks (Checko
et al. 2015; Cvijetic 2014; Chen and Duan 2011).

Third Generation

W-CDMA, CDMA2000, and TD-SCDMA: 3G employs wide brand wireless network

with which clarity is boosted. High-volume data movement was achievable in EDGE,
but the packet transfer over the air interface still operates like a circuit switching
call. The information transfer rate of 3G telecommunication networks is at least
2 Mbps (Liu et al. 2014). As a result, in the circuit switch scenario, some of the
packet connection efficiency is lost. Furthermore, different portions of the world
have varied criteria for establishing networks. As a result, 3G was born. 3G is not
a single standard; it is a collection of standards that can all communicate with one
another. The development has been continued by the Third-Generation Partnership
Project (3GPP), which has defined a mobile system that meets the IMT-2000 standard.
It was known in Europe as UMTS (Universal Terrestrial Mobile System), which is
governed by the ETSI. The ITU-T nomenclature for the third-generation system is
IMT-2000, but the American 3G variation is cdma2000. The air-interface technology
for UMTS is W-CDMA. 3G networks allow network operators to provide a broader
choice of more advanced services to users while increasing network capacity through
increased spectral efficiency. On October 1, 2001, NTT DoCoMo in Japan branded
FOMA launched the first commercial for 3G network that was based on W-CDMA
technology (Banikazemi et al. 2013; Rost et al. 2014; Zhou and Yu 2014).

Fourth Generation

On June 23, 2005, the first successful 4G field testing was performed in Tokyo,
Japan on June 23, 2005. In the downlink, NTT DoCoMo was able to achieve 1 Gbps
real-time packet transmission at a pace of roughly 20 km/h (Zhang et al. 2015).
Base stations emit signaling messages for service subscription to mobile stations
on a regular basis in modern GSM systems. Because of the variations in wireless
technology and access protocols, this procedure becomes more challenging in 4G
heterogeneous systems. Terminal mobility is required in 4G infrastructure to deliver
wireless services at any time and from any location (5G Training and Certification
2014; 5G Forum 2015; Wunder et al. 2014). Mobile clients can travel across wireless
6 R. Sil and R. Chatterjee

network, geographic borders due to terminal mobility. The two most important chal-
lenges in terminal mobility are handoff management and location management. The
system tracks and locates a mobile terminal for prospective integration with loca-
tion management. Location management entails managing all information regarding
roaming terminals, including their initial and current locations, authentication infor-
mation, and so on. When the terminal roams, handoff management, on the other
hand, keeps the lines of communication open (Wunder et al. 2014). For IPv6 wire-
less systems, Mobile IPv6 (MIPv6) is a standardized IP-based mobility protocol
(Pirinen 2014; Boccardi et al. 2014). Each terminal has an IPv6 home address in this
arrangement. After the local network is left by the terminal, the home address turns
into invalid, and thus a new IPv6 address (known as a care-of address) is assigned to
the terminal on the visiting network (Rappaport et al. 2013a, b; Olsson et al. 2013).
The Third-Generation Partnership Project (3GPP) created the fundamentals for
future Long-Term Evolution (LTE) advanced standards. The 3GPP candidate is for
4G designing and optimizing forthcoming radio access methods and further evolution
of the present system. In downlink and uplink transmission, peak spectrum efficiency
targets for LTE advanced systems were, respectively, established at 30 bps/Hz and
15 bps/Hz (Taori and Sridharan 2014).

Fifth Generation

WiMAX, WWWW, RAT: 5G can provide limitless wireless connectivity, bringing an

ideal real-world wireless web—the World-Wide Wireless Web (WWWW). Beyond
the 4G/IMT-advanced standards, 5G refers to the next significant phase of mobile
telecommunication standards. At this time, 5G is not an official word for any explicit
specification or document yet made public by telecommunication corporations or
standardization groups like 3GPP, WiMax Forum, or ITU-R. Each new update will
improve system performance while also introducing new features and application
areas. Home automation, smart transportation, security, and e-books are some of
the other applications that benefit from mobile connection (Pi and Khan 2011a, b;
Korakis et al. 2003; Bae et al. 2014). The Institute of Electrical and Electronics
Engineers (IEEE) has approved a set of wireless broadband standards known as
IEEE 802.16 (IEEE).
The WiMAX Forum industry organization has marketed it under the name
“WiMAX” (from “Worldwide Interoperability for Microwave Access”). The air
interface and related services connected with wireless local loop are standardized
by IEEE 802.16. The utilization of cell phones inside extremely high bandwidth
has altered due to 5G mobile technology. Bluetooth technology and Pico nets have
just been accessible on the market for children’s rocking pleasure. Users may also
connect their 5G technology cell phones to their laptops to have access to high-speed
Internet. Camera, MP3 recording, video player, huge phone capacity, dialing speed,
audio player, and much more are all part of 5G technology. Network design in the
fifth generation consists of a user terminal (which plays an important part in the
1 Evolution of Next-Generation Communication Technology 7

new architecture) and a variety of independent, autonomous radio access technolo-

gies (RAT) (Rajagopal et al. 2011; Feng and Zhang 1998; Roh et al. 2014). The 5G
mobile system is an all-IP interoperability concept for wireless and mobile networks.

Conventional Cellular Systems—Limitations

4G networks are insufficient to serve a large number of low latencies linked devices
and high spectral effectiveness that will be critical in the future. In this part, authors
have discussed over a few key areas where traditional cellular networks fall short,
prompting the development of 5G networks. Heavy data transmission isn’t supported.
Various mobile applications send messages to their servers and occasionally request
a high data transmission speed for a brief period of time (Cardieri and Rappaport
2001). With increased heavy data in the network, such sorts of data transfer may drain
the battery life of (mobile) User Equipment’s (UEs), potentially crashing the core
network. However, in today’s networks, only one sort of signaling/control mechanism
is built for all forms of traffic, resulting in substantial overhead for heavy traffic (Abd
El-atty and Gharsseldien 2013). The processing power of a Base Station (BS) can
only be used by its associated UEs in contemporary cellular networks, and they are
intended to accommodate peak time traffic. When a BS is lightly loaded, however, its
processing power may be dispersed across a vast geographical region. On weekends
or holidays, BSs in residential areas are overloaded while BSs in business areas are
almost empty. However, because practically idle BSs require the same amount of
power as over-subscribed BSs, the network’s overall cost rises (Huq et al. 2013).
A typical cellular network employs two distinct channels: one for transmission
from a UE to a BS, known as uplink (UL), and the other for transmission from a UE to
a BS, known as downlink (DL). A UE being assigned to two separate channels is not
an efficient use of the frequency spectrum. However, if both channels run at the same
frequency, as in a full duplex wireless radio, co-channel interference (interference
between signals utilizing the same frequency) in the UL and DL channels becomes
a big concern in 4G networks (Wang et al. 2013; Hossain et al. 2014; Sanguinetti
et al. 2015).
It also hinders network densification or the deployment of a large number of BSs
in a given region. Heterogeneous wireless networks are not supported. Heteroge-
neous wireless networks (HetNets) are wireless networks that use a variety of access
technologies, such as third generation (3G), fourth generation (4G), wireless local
area networks (WLAN), Bluetooth, and Wi-Fi (Goyal et al. 2021). In 4G, HetNets
are already standardized, but the underlying architecture was not designed to accom-
modate them. Furthermore, existing cellular networks only enable a UE to have a DL
channel, a UL channel must be coupled with a single BS, preventing HetNets from
being fully utilized. For performance improvement in HetNets, a UE might choose
a UL channel and a DL channel from two separate BSs that belong to two different
wireless networks.
8 R. Sil and R. Chatterjee

Vision and Mission—5G

The convergence of growing mm-wave spectrum availability and new application-

specific needs will usher in the next major advancement in wireless communica-
tions, i.e., 5G. Substantial increase in wireless data speeds, bandwidth, coverage,
and connection is expected with 5G wireless communications as well as massive
reductions in round trip latency and energy usage. The following is a high-level
summary of the 5G standardization process. The first standard is projected to be
completed by 2020, according to the report. The Group Special Mobile Association
(GSMA) is working with its partners to shape 5G communication to its full potential
(Nam et al. 2014; Talwar et al. 2014; Arora et al. 2020; Galinina et al. 2014).
The eight major needs of future generation 5G networks have been determined
by combining the many research activities by industry and academia:
(i) Real-world data rates of 110 Gbps: This is about ten times faster than the
theoretical peak transmission rate of 150 Mbps for standard LTE networks.
(ii) 1 ms round trip latency: This is about a tenfold improvement over 4G’s 10 ms
round trip delay.
(iii) High bandwidth per unit area: A large number of linked devices with greater
bandwidths for longer periods of time are required in a particular space.
(iv) Massive number of connected devices: To achieve the IoT goal, new 5G
networks must be able to link thousands of devices.
(v) 99.999% perceived availability: 5G envisions a network that is both practical
and efficient.
(vi) Near-total coverage for “anytime, anyplace” connectivity: 5G wireless
networks must provide comprehensive coverage regardless of the location
of users.
(vii) Near-90% reduction in energy consumption: Green technology development
is already being examined by standard authorities. With 5G wireless’s high
data speeds and huge connection, this will be even more critical.
(viii) Long battery life: In future 5G networks, device power consumption reduction
is critical. Wireless industry, universities, and research groups have begun
working on various parts of 5G wireless networks in response to the eight
needs listed above (Lee et al. 2014).
According to Ericsson, 5G development will begin with existing 4G LTE networks
and backward compatibility. This will aid in the continuation of traditional device
services utilizing the same carrier frequency. Ericsson has also worked with SK
Telecom, the market leader in South Korea, to demonstrate 5G networks at the 2018
Winter Olympics. Qualcomm is working on 4G and 5G at the same time in order to
maximize their potential. The single platform should help save costs and save energy
while enabling a wide range of new services. Huawei is working with international
trade groups, a number of universities, government agencies, and ecosystem partners
to develop critical 5G advancements.
The Docomo network has detected two key trends: (i) global cellular connec-
tivity and (ii) extended real-time rich content delivery. It believes that the key to
1 Evolution of Next-Generation Communication Technology 9

5G implementation is the integration of both the higher and lower frequency bands.
Basic coverage will be provided by the lower frequencies, while high data rates will
be provided by the higher frequencies. Nokia’s 5G wireless realization focuses on
optimizing spectrum utilization, breakthrough advancements in 5G, dense tiny cells,
and better performance. Samsung’s vision for 5G is billions of autonomously linked
heterogeneous gadgets, ushering in the Internet of Things. The European Union has
launched and sponsored two major 5G research projects (Xu et al. 2014).

Architecture—5G

Cellular networks are on the edge of breaching the BS-centric network paradigm.
This is due to the excessive demand of capacity limits and sub-millisecond latency
in conventional wireless spectrum. Due to rise in demand in the wireless sector, the
initial macro-hexagonal coverage was replaced by considerably smaller cell instal-
lations. Researchers are focusing their efforts these days on how to construct user-
centric networking (Shen and Yu 2014). The user is expected to participate in network
storage, content distribution, and processing, rather than being the wireless network’s
final resolution. Future networks are expected to connect a wide range of nodes that
are near to one another. Thus, there would be a lot of co-channel interference in
dense 5G networks. The use of directional (energy focused) and sectorized antennas
rather of the more traditional omni-directional antennas. As a result, the use of Space
Division Multiple Access (SDMA) and effective antenna design is critical. The basis
for 5G systems is planned to be strengthened by decoupling the user and control
planes, as well as seamless interoperability between diverse networks (Bhushan and
Sahoo 2017). The needs for 5G network architecture, modifications in the air inter-
face, and smart antenna design are all discussed in this section. SDN, Cloud-RAN,
and HetNets are among the newer technologies addressed (Hu and Qian 2014).
There are two parts in a mobile communication network that includes (i) core
network and (ii) radio access network (RAN). Services are provided to the users in
core network whereas an RAN links individual devices to their core networks via
radio connections. In comparison to LTE’s EPS (evolved packet system) design, the
key advancement of 5G architecture is the widespread use of virtualization technolo-
gies and cloud to offer a wide range of diverse and adaptable services. Existing mobile
network designs were primarily built to fulfil the needs of voice and Internet services,
which has proven to be inadequately adaptable in 5G, which includes a diverse set of
nodes, interfaces, and services. This becomes one of the driving forces for 5G’s soft-
ware architecture (Wu et al. 2015). Because SDN (software-defined networking) and
NFV (network function virtualization) technologies can support and administer the
underlying physical infrastructure, network services may be virtualized and moved
to the cloud, where central control, processing, and management can be performed.
Compared to previous cellular networks, which use a wide range of proprietary nodes
and specific hardware appliances, the software architecture can lower equipment and
10 R. Sil and R. Chatterjee

deployment costs while increasing administration and evolution flexibility and avail-
ability (Pozar 2005). Furthermore, network slicing allows for the creation of separate
virtual networks dedicated to certain services as needed, such as a vehicle network
service, over a single physical architecture, therefore meeting the diverse needs of
varied services.
These network tasks are virtualized and software based in 5G, thus making the
services that can be easily incorporated into cloud infrastructure. The access network
and the core network are described in more depth below.

C-Ran

Usage of centralized radio access network (C-RAN) in 5G can be done for the radio
access network, by leveraging cloud and virtualization technologies to centralize and
virtualize some base station functions in the cloud, lowering the cost of deployment
and management of the greatly increased and densified base stations. A cloud center
and dispersed locations make up the RAN (Violette et al. 1988). Some RAN non-
real-time functions in the upper layers with low latency requirements, including cell
selection/reselection, intercell handover, and user-plane encryption, might be moved
to the cloud, where the information can be interchanged and resources can be shared.
This RAN cloudification will have an impact on other components of the network.
Many RAN services that were previously implemented in hardware with specific
hardware support, such as IP cores, will be able to implement in a software envi-
ronment in 5G, according to C-RAN. In this instance, it’s critical to ensure their
effectiveness. One example is the development of secrecy and integrity algorithms,
which is one of the reasons why new software-efficient algorithms for 5G use should
be considered.

SBA-Based Core Network

The main network architecture in 5G is Service-Based Architecture (SBA), with

system functionality described as a collection of network functions, such as the
Session Management Function (SMF) and the Access and Mobility Management
Function (AMF). These NFs use standard Service-Based Interfaces (SBI) to deliver
services to other approved NFs. The core network introduces a specific network func-
tion called the NF Repository Function (NRF) to deal with service registration and
discovery, as well as maintain the NF pro-file and accessible NF instances, so that
NFs may discover and access each other. The use of network slicing technology to
construct optimal networks for individual services with varied performance needs is
possible with such a service-based architecture. Subscriber keying materials, such as
long-term keys and home network private keys, are stored in Unified Data Manage-
ment (UDM). It also houses data management capabilities such as the Authentication
1 Evolution of Next-Generation Communication Technology 11

Credential Repository and Processing Function (ARPF), as well as the Subscription

Identifier De-Concealing Function (SIDF). During an authentication, ARPF is in
charge of determining the authentication technique based on the subscriber iden-
tification and specified policy, as well as computing the 5G Home Environment
Authentication Vector (HEAV). The SIDF offers decryption services for a user’s
Subscription Hidden Identifier (SUCI) in order to get the user’s long-term identity
Subscription Permanent Identifier (SUPI) (Kyro et al. 2012).

Next-Generation Network—Applications

The next-generation applications will be emerging in a multiplatform environment.

4G applications are offered on a variety of wireless technologies, including printers,
LTE, e-readers, Wi-Fi, cell phones, digital cameras, laptops, and other devices.
Although 4G apps are anticipated to be expanded and better versions of present
3G services, the capacity of 4G is yet unknown in the mobile industry.

Example of Next-Generation Network Applications

In this section, authors have provided some of the examples of next-generation

network application:
(i) Virtual Presence: This refers to 4G and 5G’s ability to deliver services 24 ×
7 to the users, even while they are off-site.
(ii) Virtual navigation: 4G offers virtual navigation, which allows users to access
a database of various places including buildings, streets, and various other
landmarks in big cities (Jaitly et al. 2017).
(iii) Telemedicine: 4G and 5G will allow for patient monitoring remotely. A user
may obtain video conference without going to hospital for help from a doctor
at any point of time and place.
(iv) Tele-geoprocessing applications: This is a hybrid of GPS (global positioning
system) and GIS (geographical information system) that allows a user to query
the position.
(v) Crisis Management: Natural catastrophes can disrupt communication
networks, which can lead to a crisis.
(vi) Education: 4G provides numerous prospect for people who want to continue
learning throughout their lives (Ranvier et al. 2009). People from various parts
of the world can save money by continuing with their education online.
(vii) Artificial Intelligence: As human life becomes increasingly surrounded
by artificial sensors capable of communicating with mobile phones, more
applications combining artificial intelligence (AI) will emerge.
12 R. Sil and R. Chatterjee

(viii) Traveling: Familiarization to new mobile phone applications, and usage of

smartphones with NFC technology and Bluetooth in the passenger travel
process. Over the next decade, technology is expected to play a leading role
such as experience a location virtually before traveling or to seek inspiration
and exchange information live (Xu et al. 2000; Dillard et al. 2004).
(ix) Security: This layer crosses all layers of the 4G and 5G network architecture,
performing functions such as authentication, encryption, authorization, and
implementation of service policy agreements between vendors.
(x) Economic growth: It is aided by technological advancements that permit
consumers and organizations to take use of content services and high-
value wireless data. 5G networks are projected to support a wide range of
applications and services because of its low latency and fast data transfer
speeds.
(xi) Smart grids: It decentralizes energy distribution and also improves the analysis
of energy consumption (Rappaport et al. 2012). Smart grids would be able
to enhance their efficiency and economic advantages as a result of this. The
5G networks would enable for regular statistical data observation, analyzing
them, and retrieval from far sensors, as well as change the energy distribution
as needed (Vook et al. 2014).
(xii) Automation: In the near future there will be availability of self-driving vehicles
that will be required to connect and communicate in real time. Furthermore,
they would communicate with other devices on the roadways, residences, and
businesses with a need of virtually zero latency. As a result, a linked vehicular
environment would allow for a secure and effective incorporation with other
data systems.
(xiii) Healthcare systems: Medical services can benefit from dependable, secure,
and quick mobile communication, such as regular data transfers from patients’
bodies to the cloud or healthcare facilities (Rajagopal 2012; Anderson and
Rappaport 2004; Collonge et al. 2004). As a result, medical treatments that
are relevant and urgent may be forecasted and supplied to patients extremely
quickly.
(xiv) Industrial applications: 5G networks’ zero-latency capability would allow
robots, mobile devices, sensors, drones, and data collector devices to get a
real-time data without delay, allowing industrial functions to be managed and
operated swiftly while conserving energy (Wang et al. 2014; Sinha et al. 2017;
Jungnickel et al. 2014; Rappaport 1996; Ahmad et al. 2020).

Conclusion and Future Scope

The advancement of next-generation applications has emerged in a multiplatform

environment. Many wireless technologies support 4G application which include LTE
and Wi-Fi. Traditionally, networks and communication services have been built and
supplied as secure resources with limited potential for customization, improvement,
1 Evolution of Next-Generation Communication Technology 13

and specialization. Thus, this approach is not adaptable enough to fulfil a varied
range of new application needs or to get benefit from a slew of new research-based
advances. As a consequence, in some of the selected areas, a new communica-
tion design model is prototyped, developed, and put into production. Other than
providing a permanent infrastructure, this approach views communication resources
as a flexible, programmable environment that can be continually updated to meet
new requirements.
In the mobile sector, Mobile Wireless Communication Technology will be leading
the new phase. Nowadays, offices are at the fingertips or on phones due to the emer-
gence of Personal Data Assistants (PDAs) and mobile phones. There is a huge scope
in the future for 5G technology as it is able to handle most of the modern technologies
and supply clients with excellent handsets. 5G will be providing assistance to the idea
of Super Core, where all network operators are linked through a single core and are
a part of a common infrastructure, independent of their access methods. 4G and 5G
techniques provide lower battery consumption, low probability (more coverage), and
cheaper or no infrastructural implementation costs along with effective user services.
In 5G systems, every mobile phone consists of a permanent “Home IP address” and
“care-of address” that refers to its current location. A packet is sent to home address’s
server when a computer on the Internet wants to communicate with a mobile phone.
It then sends a packet to the real location via the tunnel. Cloud computing is a system
that uses the Internet and a central distant server to keep data and apps up to date.
This central distant service is the content provider in 5G network. Thus, it is due
to cloud computing that consumers and companies are able to use programs and
therefore access their personal files from any computer with an Internet connection
without installing.

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Chapter 2
Third Industrial Revolution: 5G Wireless
Systems, Internet of Things, and Beyond

Anwesha Das, Aninda Chowdhury, and Riya Sil

Abstract Commercial 5G mobile communication installations are currently

ongoing. A variety of reasons, notably rising business and consumer needs as well as
the advent of much more cheap equipment, are driving 5G and IoT growth. Substan-
tial carrier investments in 5G networks, frequency, and infrastructure, as well as
the adoption of international standards, are indeed assisting in driving development
and increasing investor interest in IoT. Today’s modern 5G mobile cellular systems
are emerging beyond current 4G technology, which will remain to fulfill diverse
applications. 5G, which is expected to last a long time, may meet present needs like
intelligent power applications while also forecasting future use cases like self-driving
automobiles. Mobile operators would need to guarantee to ensure its added versatility
simultaneously present as well as future use cases need as companies oversee the
growth of technology. Cautious providers would control their expenditures to assure
customer service as infrastructures migrate to 5G. The majority of 5G use case
scenarios fall into three broad segments: improved mobile broadband (eMBB), enor-
mous IoT, as well as critical communications, within each set of performance, and
bandwidth, including delay needs. While 4G would remain to be utilized for so many
consumers and commercial IoT scenarios, 5G offers IoT features that 4G as well as
other networks do not. This would include 5G’s capacity to accommodate a massive
amount of fixed and portable IoT systems with variable speeds, capacity, and service
level needs. As the Internet of Things develops, the adaptability of 5G would become
increasingly more important for organizations wanting to satisfy the stringent needs
of vital connectivity. Because of 5G’s ultra-reliability as well as reduced latency, self-
driving vehicles, intelligent power infrastructures, better industrial automation, and
some other demanding technologies are becoming a possibility. While 5G increases
Internet bandwidth, cloud services, machine intelligence, as well as cloud technolo-
gies would all assist to manage huge data quantities created by IoT. Additional 5G
advancements, like low latency, and non-public networking, including the core of

A. Das · R. Sil (B)

Adamas University, Kolkata 700126, India
e-mail: riyasil1802@gmail.com
A. Chowdhury
St. Placid’s School and College, Chittagong, Bangladesh

© The Author(s) 2023 19

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_2
20 A. Das et al.

5G, would eventually help realize the goals of an IoT network that is worldwide and
capable of sustaining connectivity that is larger in size.

Keywords Wireless communications · IoT · Fifth-generation communication ·

Mobile broadband · Technology · Networks

Introduction

Fifth-generation (5G) connections become more widely available as an important

driver of the expansion of IoT systems. Today’s modern researchers and professionals
are analogous to Christopher Columbus, who started to establish that the sphere no
longer is analog (Alsamhi et al. 2021). Numerous emerging innovations have resulted
in the emergence of the technological age, but nobody has had a greater influence
than portable technological advances. Personally, such technology has transformed
the everyday lives people connect, but collectively, they dramatically revolutionized
the reality in which the people live by generating a “blue ocean” of a perspective that
is new. A blue ocean, in basic words, seems to be the emergence of a completely fresh
sector or developments in an established sector that modify the bounds of rivalry,
resulting in a marketplace devoid of competition. Traditionally, blue seas have indeed
been industry specific and also the consequence of a particular firm’s breakthroughs,
like Apple, Netflix, Starbucks, Uber, etc. The utilization of mobile technological
advances, on the other hand, has transformed the blue ocean together into a sea of
the IoT. Whenever opportunities emerge, the integration of various innovations has
an impact across all sectors at the very same time.

IoT and Its Devices

The IoT refers to any things (i.e., items) which are linked to the Web and may be
accessed via ubiquitous technologies. The IoT has given rise to plenty of innovative
“intelligent” devices (i.e., Internet enabled). People are presently living amid a smart
transformation wherein numerous items in their daily lives are connected via Internet
(Ali et al. 2019; Goyal et al. 2021a; Chettri and Bera 2019). A few instances of
advanced devices that have resulted in the emergence combining mobile computing
as well as technologies of IoT have been shown in Table 2.1 (Peral-Rosado et al.
2018; Ahmad et al. 2020; French and Shim 2016; Ghendir et al. 2019; Arsh et al.
2021; Hussein et al. 2018).
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 21

Table 2.1 Examples of IoT devices in different sectors

Sl. Residential Fitness devices Attire devices Gadget devices
no devices
1 Smart lock for Monitor for BP Smart watch Smart stoves
door measurement
2 System of Monitor for cholesterol Smart socks Smart AC
hydroponic measurement
3 Smart tank of Monitor for blood glucose Smart shirt Smart washer
propane level measurement for dishes
4 Smart control Smart system for sleeping Insoles that are enabled Smart machine
of sprinkler via Bluetooth for washing
5 Smart security Smart cardio Glasses of technology Smart
for home refrigerator

A 5G and IoT

As humans move closer to 5G possibilities, the simplicity, as well as the efficiency

with which IoT links may be established, would improve, allowing for further tech-
nological improvements (Iannacci 2018; Arora et al. 2020; Islam et al. 2021; Jung-
nickel et al. 2019). Nevertheless, the implications of mobile computing, as well as
IoT, go further than the implementation of novel technical prowess. Humans partici-
pate in the data that is collected by adding multimedia elements to each and all things
humans contact within their ordinary activities. As a consequence, big information
is no longer huge—massive, it is indeed and it’ll only expand as businesses begin
to move forward into the increasing connection between many things and people.
As they continue to evolve opportunities for information analysis, the implications
for data professionals are enormous (Sethi et al. 2021; Knieps 2019; Li et al. 2018).
Businesses of all sizes must answer customer demands for even more connection
among persons, computers, as well as things.
For achieving the objective, the authors have emphasized the implications of 5G
mobile technology on the “Internet of Things (IoT)”. A detailed discussion on the
ubiquitous computing and 5G technology has been discussed taking into consider-
ation the IoT and 5G technology individually. The neediness and the challenges of
wireless 4G networks and requirements, vision (in the context of both research and
industrial perspective), unification of technologies, and technology drivers for the
5G-enabled IoT technologies have been discussed descriptively. Finally, the chal-
lenges of the research and the respective trends for the future have been presented in
the study related to the topic “Internet of Things (IoT) towards 5G Wireless Systems”.
Section “5G and Ubiquitous Computing” provides a clear view of 5G and its ubiq-
uitous computing. Section “History and Present Researches on IoT as Well as 5G”
discusses the history and the current research on IoT and 5G. Section “Requirements
of IoT and Shortcomings of Wireless 4G Network” gives a detailed description of the
requirements of IoT and the various shortcomings of wireless 4G networks (Rathi
22 A. Das et al.

et al. 2020; Mei et al. 2019; Migabo et al. 2020). Section “Needs for IoT that is 5G
Enabled” focuses on the need for 5G-enabled IoT. Section “The Visionof 5G IoT:
Industrial and Research Context” highlights the vision of 5G IoT in an industrial and
research context. Section “Unification of Technologies” gives a detailed overview of
the unification of technologies. Section “Conclusion and Future Scope” concludes
the paper and discusses the future scope of the work.

5G and Ubiquitous Computing

People and corporations have commonly embraced the IoT and the analytics of big
data in today’s modern pervasive computing age, with the next evolution of cellular
technologies, 5G networking, just at the vanguard (Malik and Bhushan 2022; Poncha
et al. 2018). Deloitte Reviews, MIS Quarterly, Proceedings of the ACM, as well as
“Information Systems Research”, among others, have dedicated special issues to IoT,
analytics of big data, including 5G. According to a new Bain & Company analysis,
Europe, as well as the US, would add approximately usd8 trillion to world GDP
through 2020. This section will discuss IoT as well as 5G.

The Internet of Things

Around 1999, British innovator and entrepreneur Kevin Ashton invented the phrase
“Internet of things”. IoT provides improved gadgets and network, including service
connectedness which extends above machine-to-machine interactions (M2M) as well
as encompasses a wide range of interfaces and areas, including activities (Santos et al.
2018; Wijethilaka and Liyanage 2021a, b). For describing IoT, two key concepts may
be used: things and also the Web. An item has to be able to send data or orders to
some other item over a connection to also be IoT capable. Human relationships or
sensing could cause IoT-enabled items to undertake activities, resulting together in
a linked network comprising things having pervasive management. The connection
might be personalized, corporate, or governmental, while the Web has been the
most commonly imagined foundation underlying IoT (Xing 2020). IoT is sometimes
confused with advanced devices, which applies to just about any device having
Internet access. Smart technology consists of devices that really can access the web,
while IoT expands this paradigm to also include things that can be controlled from
anywhere using online services (Yan 2019; Zikria et al. 2018; Agiwal et al. 2019).
A smartphone, for instance, could access the Internet, however, the device should
be used in person. An IoT-enabled device, on the other hand, may be accessed and
controlled from every place and at any time (Fig. 2.1).
Machines could monitor and catalog all items and persons throughout regular
living when they have unique identification (Albreem et al. 2021). Modern smart-
phones, smart watches, smart automobiles, cargo containers, as well as other devices
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 23

Fig. 2.1 Value loop for information

are becoming more linked than ever before. To now, the much more common uses
include home automation, wearable technology, intelligent buildings, smart grids,
linked cars, and even linked health care (Zheng et al. 2020). The Internet of Things
can help you “monitor and tally, watch and recognize, analyze and respond in situa-
tions”. The intelligent supply cycle depicts the enterprise value steps (i.e., generate,
transmit, collect, analyze, as well as react) which must be completed to produce
worth (Aman et al. 2020). Detectors, networking, norms, augmented cognition, and
enhanced behavior are all present at each level. Aside from RFID, items can be tagged
utilizing techniques such as base station communications, QR codes, barcodes, as
well as electronic copyrighting.

A 5G Network

5G would be the next step in the evolution of the mobile world. The US reclaimed
dominance within the mobile market through fourth-generation (4G) installations.
European, Japan, and Korea lead the third-generation (3G) globe in the 2000s. Every
area wants to be the global leader in the 5G network (Sicari et al. 2020). Although
5G is still very much in its initial stages of development, the “International Telecom-
munication Union (ITU)” has started work on the “International Mobile Telecom-
munications (IMT)” spectrum needs for 2020 and even beyond (Sodhro et al. 2020).
The Fig. 2.2 depicts a probable timeline for the advancement of 5G networks (i.e.,
5G study, development, and testing till 2016; 5G standards through mid-2018; 5G
products till the 2020 preceding 5G implementation in 2021). This development of
24 A. Das et al.

Fig. 2.2 Evolution of mobile technology

the 5G technique requires LTE-A, LTE-B, through LTE-C as elements of the “3rd
Generation Partnership Project (3GPP)”.
Even though there is no agreement on 5G, most business experts believe in the sorts
of quality standards (for example, latency, network availability, energy efficiency,
huge “multiple-in multiple-out (MIMO)”, energy usage, linked devices, exposure,
and increased security needs). Verizon intends to have been the first US operator
to provide a 5G network pending implementation (Worlu et al. 2019; Xia et al.
2019; Qiu et al. 2020). Many other nations, including Japan and South Korea, are
making arrangements to provide 5G field trials in the years ahead. As previously
said, the expansion and sustainability of 5G would be dependent on the performance
of the whole informational, communications, and technological (ICT) environment
(Sadique et al. 2018; Ni et al. 2019; Oughton et al. 2018; Liu et al. 2020a). The
whole ICT environment would play an important role in value generation as well as
preservation.

History and Present Researches on IoT as Well as 5G

A variety of wireless systems, including 2G, 3G, or 4G; Bluetooth; Wi-Fi connec-
tivity; and others, have indeed been employed in diverse systems of IoT, wherein
billions of devices would be linked by the wireless system (Khanna and Kaur 2020).
The 2G systems (which presently serve 90% of the global total population) are
intended for speech, the 3G systems (which presently serve 65% of the global total
population) for the phone as well as information, and the 4G networks for cable
broadband services. Although 3G and 4G networks are commonly utilized for IoT,
they are not entirely suited for IoT systems (Khatua et al. 2020). 4G has substantially
improved the capability of mobile networks in terms of providing Web access to
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 25

IoT devices. Since 2012, the “lifelong evolution” (LTE) to 4G connection has been
the quickest and also the most constant variation of 4G when contrasted to rival
technologies like ZigBee, LoRa, WiMAX b, Sigfox, and many others (Husain et al.
2018). As its next networking, 5G networking and standards are anticipated to tackle
issues that 4G networks faced, including more intricate communications, gadget
computing capabilities, among intellects, etc., to meet the demands of intelligent
devices, Industrial 4.0, and so forth.
The graph depicts the progression of mobile networks between 3G- and 5G-
powered IoT. The 5G’s growth would indeed be founded on the basis established
with LTE of 4G, which also would give users the phone, information, and Internet
connectivity (Kadhim et al. 2020). 5G will greatly enhance speed and reliability
to enable dependable and fast connection to upcoming IoT devices. The present
LTE technology of 4G could deliver a rate of transmission of 1 Gbps, although the
connection of 4G can indeed be readily interrupted by Wi-Fi transmissions, struc-
tures, microwaves, and other conclusions (Amin and Hossain 2020; Athanasiadou
et al. 2020; Ahad et al. 2020). 5G connections may give customers greater speeds
over 4G technology, up to ten Gbps, even while providing dependable connectivity
to thousands of devices simultaneously.
The image indicates something in IoT, massive machine-type communication
(MTC) applications in intelligent urban, health systems, as well as other areas neces-
sitate vast connectivity connections, resulting in a large heterogeneity of IoT and
also many implementation issues (Ali et al. 2020). Several M2M communication
techniques have been employed over the last two decades, which include short-
range MTC like “Low Energy Bluetooth (BLE v4.0)”, ZigBee, Wi-Fi, and others,
as well as long-range MTC like “Low-Power wide-area (LPWA)”, Ingenu “random
phase multiple access (RPMA)”, Sigfox, LoRa, and many other (Lu et al. 2018). The
groups of three partnerships project (3GPP) recommended “Enhanced Machine-
Type Communication (EMTC)”, “Extended Coverage-Global System for Mobile
Communications for the IoT (EC GSM-IoT)”, and “Narrowband-IoT (NB-IoT)”
as cellular-based LPWA technologies for such IoT to guarantee M2M capabili-
ties (Mavromoustakis et al. 2016). Available communication technology remains
varied, therefore meeting the needs of IoT applications will be a problem again for
fifth-generation (5G) cellular operators.

Requirements of IoT and Shortcomings of Wireless 4G

Network

Whereas the legacy connectivity is focused on H2H functionality over extended jour-
neys, current interaction is trying to shift into a broader sense M2M console (Poram-
bage et al. 2018; Salam 2020; Ye et al. 2019). The diversity of diversified requirements
represents a problem to co-operative priority scheduling among multiple things, as
well as information sharing and interaction among items extra broadly (Zhang et al.
26 A. Das et al.

2018). As a result, it’s indeed necessary to explore legacy wireless communication

from the standpoint of IoT.
Massive Connectivity—The basic concept of IoT transforms the volume and
variety of linked devices (Zikria et al. 2019; Liu et al. 2020b; Painuly et al. 2020; Khan
and Javaid 2021). Globally, 212 bn smart objects are estimated to also be installed by
2020. LTE wireless networks, on the other hand, were intended for limited “Radio
Resource Control (RRC)” connected consumers. Previously, cables supported the
company’s aim of independent connection (Khanna and Kaur 2019). Even so, the
immense parallelization anticipated inside the IoT scenery can be discussed by wire-
less systems. This technological divide, along with billions of networked diverse
items, would eventually coax disruptive innovation through older systems (Bektas
et al. 2018). Furthermore, the old Wi-Fi access method will struggle with congestion
as well as overloading as a result of numerous demands from massive equipment.
The network efficiency would’ve been degraded if there were a massive amount
of MTC devices conducting concurrent remote access (Boursianis et al. 2022).
Additionally, conventional network computation approaches will fall well short of
extracting the needed information from the vast quantity, speed, as well as diversity
of interconnections.
Long Life Time of Battery—The bulk of smart IoT technology is required to
be battery powered to support wireless communication. Replacing or recharging the
battery may not even be simple or cost-effective (Awoyemi et al. 2020). Furthermore,
IoT-enabled devices are powered by small batteries. As a result, as previously stated,
the requirement for improved battery life is an approaching problem in IoT imple-
mentation that cannot be disregarded. Common M2M travel patterns demonstrate
that now the power needed for the transmission process is often low (Bana et al.
2019). Despite the addition of a power-saving option for MTC communications in
3GPP Release 12 assuring extra battery lifetime in IoT systems remains a daunting
prospect for multiplexing OFDM division-based LTE.
Constraints of Infrequent Congestion as well as Orthogonality-Random-access
synchronization processes are required in 4G LTE channels to confront orthogo-
nality restrictions. Although synchronization maintains senders’ temporal coherence,
orthogonality reduces crosstalk. Regular time–frequency synchronization in short
data packets results in signaling complexity (Hu et al. 2018). In reality, the volume
of information info in sequential “Orthogonal Frequency-Division Multiple Access
(OFDMA)-based” MTC approaches is similar and much less than elevated signaling
bandwidth. Furthermore, in an IoT future, common smart things will have become
huge traffic producers as well as recipients (Pedersen et al. 2018). As a result, future
wireless communication is projected to just be intermittent, providing a significant
problem for service-based IoT design.
Delayed Aware Solutions as well as Delayed Considerate Solutions—In IoT
devices, limiting battery capacity as well as bandwidth constraints promote periodic
communication (Sharma et al. 2020). To a certain degree, delay-tolerant connectivity
is appropriate for certain workloads. Nevertheless, applications such as healthcare
coverage, automated vehicles, as well as monitoring are major priorities and time
sensitive. Additionally, haptic online, which is fast gaining popularity for apps at
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 27

the fingers, is indeed a major driver for low bandwidth broadband Internet (Yang
and Alouini 2019). 4G networks have such a round-trip latency of 10–15 ms (due to
uplink scheduling requests), which is dubious for vital communications, autonomous
cars, as well as other time-sensitive activities.
Narrowband Transmission—Demands for long battery life, low bandwidth M2M
connectivity, especially stochastic flow are incompatible with traditional broadband
wireless connections (Hossein Motlagh et al. 2020). Conventional LTE methods,
which were built for Internet activities, are thus over-engineered for reduced, many-
delay-tolerant applications that are envisioned in the IoT environment. 3GPP has
recently added “narrowband IoT (NB-IoT) in Release 13” specifications (Hui et al.
2020). In contrast to short-range unregistered systems, such as ZigBee, Bluetooth,
and others, NB-IoT technology enables minimal wattage and a vast range of commu-
nication with an available band. It is conceivable to implement NB-IoT with just a
limited bandwidth of around 200 kHz (Kaur 2020). Furthermore, it offers increased
range, higher energy effectiveness enabling larger battery, and reduced complications
with low-cost gadgets. Although conventional LTE networks employ a sub-carrier
of 15 kHz, NB-IoT introduces subcarriers of 3.75 kHz again for uplink architecture
(Aman et al. 2020). Nevertheless, tests have shown that a 3.75 kHz transmitted signal
has certain detrimental consequences on cohabitation only with LTE’s 15 kHz sub-
carrier width. As a result, narrowband functioning is among the important criteria
that have to be investigated further than just low-data workloads as well as adaptable
IoT installation.
Transcend Human Interaction—IoT may be viewed as just a highly distributed
communication network that interfaces well with the physical domain at the system
level (Habibi et al. 2019). Gadgets observe physical processes, thus IoT connectivity
systems include detectors, controllers, meters, utilities, communications, etc. As a
result, a new issue has emerged that links not just persons but also technologies
(Nguyen et al. 2021). Unlike H2H connections, the primary IoT need is the ability
to link a wide range of devices remotely at such a low cost. Moreover, a physical
device connection needs sufficient Internet bandwidth, extended battery life, as well
as enhanced penetration so that the gadgets could access difficult areas. This pursuit
of broad sensing application is projected to get to be a vital impediment to traditional
wireless systems that is human oriented (Shi et al. 2020). The perspectives that
are things oriented imply something other than personal interaction. Furthermore,
as IoT gets more complex, objects and people would connect more frequently and
seamlessly. As a result, the Internet of Things’ need of connecting communications
well with the physical domain cannot indeed be overlooked.
28 A. Das et al.

Needs for IoT that is 5G Enabled

The IoT is transforming daily life by enabling a lot of unique services that run on
ecosystems of intelligent and extremely diverse gadgets (Teli et al. 2018). Numerous
research works have been undertaken in recent years on several tough subjects for
such 5G IoT, as well as the key criteria of IoT encompass:
(i) With increased data speed, future IoT systems like HD streaming content “vir-
tual reality (VR)” or rather “augmented reality (AR)” would demand greater
data rates of roughly 25 Mbps to obtain satisfactory performances.
(ii) High-scalability and perfectly all right systems are required for 5G IoT to
allow fine-grained front-haul networking breakdown through NFV.
(iii) Extremely reduced delay is required in 5G IoT services like haptic Web, AR,
video gaming, and so on.
(iv) With reliability and robustness, 5G IoT necessitates enhanced availability and
transition effectiveness for consumers of IoT devices and applications.
(v) Safety, unlike typical security strategies that safeguard connection and privacy
protection, the upcoming IoT payment service, as well as online wallet
services, create a greater safety approach to increase information security.
(vi) Extended battery life: To handle billions of low-power as well as low-cost IoT
systems in 5G IoT, 5G-powered IoT requires reduced energy technologies.
(vii) Connectivity density, a very large number of sensors would be linked together
during 5G IoT, requiring 5G to facilitate the effective transmission of messages
in a specific time and region.
(viii) Agility, the 5G IoT ought to be capable of handling a large number of device-
to-device connections while being mobile.
The current state of IoT involves posting as well as saving all basic information
generated via IoT systems to the cloud, where it would be analyzed via cloud storage
to derive relevant knowledge via analysis techniques.

The Vision of 5G IOT: Industrial and Research Context

5G IoT’s vision, as well as its goal, is to link a wide variety of devices inside the
same system architecture (Atiqur et al. 2020). Numerous advanced 5G wireless
technologies, such as smart cities, “Internet of vehicle (IoV)”, advanced factories,
and smart farming, including smart health care, contribute to the IoT boom (Farhan
et al. 2018). A few of the major cellular, semiconductors, and network operators
having outstanding research centers are performing laboratory and site experiments
to make a 5G wireless network available by 2030. 5G studies and experimentation are
being conducted at several research centers having world-class lab facilities (Hassan
et al. 2020). The most recent advancements and upgrades in cellular technologies
offer to address the requirement for fast broadband, improved spectrum utilization,
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 29

Table 2.2 Vision of 5G-enabled IoT for various telecommunication industries

Sl. Name of the Vision
no industries
1 Intel Intel is working on a new essential system that will allow 5G HetNets
while also maximizing the effective usage of spectrum resources (Awin
et al. 2019). Intel is developing new technologies, like licensed accessible
access (LAA), to improve speed
2 Samsung Samsung’s ambition is really to link everything on the planet (Chen and
Okada 2020). Samsung anticipates that almost all IoT platform gadgets
will be linked to one another. Effective collaboration is required for the
realization of 5G IoT fields like home automation, smart cities, smart
manufacturing, smart health care, smart farming, transportation, and so
on. Samsung has made significant contributions to the IoT accessible
cloud service, which allows employees to access household equipment.
The remote controller is programmed for Samsung equipment such as the
air conditioner, washer, and refrigerators
3 Nokia Nokia recently announced the creation of a cross-domain framework to
enable 5G technologies. Nokia is focusing on system modernization,
which serves to maintain overall power consumption stability by
decreasing the usage of energy that is not connected directly to data
transfer (He et al. 2021). They are focusing on many big chances to
improve ground station fuel efficiency

long-range connectivity, higher reliability, and also the connectivity of intelligent

devices (Narayanan et al. 2020). IoT together in the 5G context has the potential for
being the most transformative technology in the world of information technology.
As per the studies, 5G wireless communication would be available in several nations
by 2030 (Table 2.2).

Unification of Technologies

Technologies are always progressing toward unity. Whenever the Web became
commercialized within the 1990s, new tools developed, triggering a chain reac-
tion of technological advancement (Goyal et al. 2021b). Items did not have Internet
access in the 1990s. This era’s technology worked individually with one another.
As smartphones and televisions gained Web access in the 2000s, the latest craze of
connected phones emerged. During this period, technology started to shift by incor-
porating functionalities that needed the Internet (Sun et al. 2020). As networking
improved, data speeds rose, as did the capacities of connected phones, giving a boost
to the Web of things when humanity moved from such a theoretical viewpoint to
realize those skills in the 2010s.
Items today do indeed have Internet access, but they can communicate with one
another and share data. Utilizing sensing as well as connected devices, things may
communicate with one another, sharing information and giving new services to the
30 A. Das et al.

Fig. 2.3 Intelligent technologies evolution

public everywhere. As even more devices become IoT enabled, they get closer to
an interlinked future in which all items may interact including all things accessible
by people anyone at any time from any location. This transition to IoT opens up
new opportunities, like social media of smart items which are closely attached. The
Fig. 2.3 depicts the progression of advanced devices from standalone products to IoT
social networking sites.

Enabling Technology Drivers in 5G IoT

5G’s major properties for enabling various real-time multimedia include quick pace,
reduced latency, as well as high throughput. As a result, 5G enablement technologies
must be developed (Kim et al. 2019). Device-to-Device (D2D) communications,
Machine-to-Machine (M2M) interaction, Millimeter Waves, “Quality of Service
(QoS)”, “Network Function Virtualization (NFV)”, Vehicle-to-Everything (V2X),
Full-Duplex, as well as Green Interaction are among the core technology solutions
utilized in 5G technology. 5G system enables data transmission rates of 10–20 Gbps,
which is 100 times faster than 4G technologies, enabling new IoT robotic surgical
capabilities.

Challenges and Trends of Future of the Research

5G has characteristics that can fulfill the criteria for future IoT, but it also created
a new series of exciting research problems on 5G IoT design, trustworthy interac-
tions among gadgets, privacy difficulties, and so on (Pedersen et al. 2018). The 5G
IoT incorporates several techniques and therefore is having a big influence on IoT
systems. In this part, prospective research problems, as well as future developments
in 5G IoT, are discussed.

Challenges

(i) Bands of frequencies—In contrast with 4G LTE, which runs on known band-
widths under 6 GHz, 5G needs frequencies up beyond 300 GHz. Certain
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 31

frequencies, called mm waves, may transport significantly greater bandwidth

and provide a 20-fold improvement in potential bandwidth above LTE.
(ii) Scope and deployments—Although 5G provides considerable increases in
speed and capacity, it’s a much more short scope which would necessitate
infrastructures to a greater extent (Khujamatov et al. 2020). The wavelengths
that are longer in length allow for direction in extreme manner radio signals
that might be focused or directed—which is known as beamforming. The major
difficulty is that, whereas 5G antenna can manage more user activity, they can
only shoot out across short ranges.
(iii) Expenses of construction and acquisition—Establishing a network is costly;
operators would fund it by providing consumers revenues (Qiu et al. 2020).
Much the same as LTE packages had a high capital cost, 5G is ready to
imitate a similar pattern. It’s not simply adding a level to a current network;
it’s establishing the basis want something different.
(iv) Device compatibility—There’s now a lot of information about 5G-enabled
smartphones and various other instruments. However, its accessibility would be
determined by how costly devices are for producing and how rapidly the system
is employed. Various operators in the US, South Korea, including Japan have
indeed commenced 5G projects for trial in various regions, and manufacturers
have stated that suitable mobile phones would indeed be accessible in 2019.
(v) Data security—5G deployment, just like every data-driven innovation, would
face either basic risks of cybersecurity that is of advanced level. Despite the
information that 5G is somewhat covered by the “Authentication and Key
Agreement (AKA)”, which is a method meant to create confidence among
providers, it is presently easy to follow individuals utilizing smartphones or
perhaps even eavesdropping on ongoing phone calls. The obligation, as that is
today, would be on operators and networking conglomerates to offer a virtual
support system for consumers.

Trends of Future

Omdia believes that the confluence of AI as well as edge computing will boost the
importance and influence propositions of IoT. Edge features on equipment within
fields decrease delay, energy consumption, as well as expenses associated with data
transport to the clouds (Liu et al. 2021). This provides a pathway for the analysis of
more complicated types of data. As per “Fortune Business Insights”, the worldwide
IoT industry will be worth $1.1 trillion by 2026. It’s being used for monitoring people,
towns, agriculture, and whatever else one could conceive of for the next years.
32 A. Das et al.

Conclusion and Future Scope

5G offers several advantages not accessible using technological breakthroughs. This

would include 5G’s ability to accommodate an overwhelming amount of fixed and
portable IoT systems with varying speed, capacity, and quality service needs. The
adaptability of 5G would become increasingly more important for companies as the
Internet of Things develops. Important connections would be supported by 5G, which
will have much more stringent performance targets. The ultra-reliability, as well as
reduced latency of 5G, might aid in the realization of self-driving vehicles, intel-
ligent power networks, better industrial automation, as well as other sophisticated
technologies.
5G networks could safely manage the large volume of data generated by IoT
gadgets in addition to serving a very large number of devices including their different
service needs. Cloud technology, machine intelligence, as well as cloud technologies
would also aid in the management of IoT large datasets. 5G is indeed a worldwide
technology that is being deployed following 3GPP international specifications. It was
created to assure IoT compatibility, and it is still evolving through continual improve-
ments to agreed-upon criteria. Expanding on the connectivity for IoT provided by
4G, Release 15 through 16 of something like the 3GPP standards will give additional
assistance for IoT devices using 5G capabilities such as ultra-reliability as well as
reduced latency. Further 5G advancements, like low latency, non-public networking,
including 5G core, are believed to assist in attaining the goals of a worldwide IoT
network able to support large connectivity with varying movement and accessible
needs.
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 33

Appendix 1: Wireless Technology Features

(Source https://i.pinimg.com/originals/9f/a1/dd/9fa1dd19560fe8b1d86bcb6db3c
f31c1.jpg)
34 A. Das et al.

Appendix 2: Information Speed Increment

(Source https://www.thalesgroup.com/sites/default/files/gemalto/Image002.jpg)
2 Third Industrial Revolution: 5G Wireless Systems, Internet of Things … 35

Appendix 3: 5G Network Architecture

(Source https://www.rfwireless-world.com/images/5G-network-architecture.jpg)
36 A. Das et al.

Appendix 4: 5G Tower Signals

(Source https://c8.alamy.com/zooms/9/92ef2e93fe594b4c906052e36f966231/
2bde4b7.jpg)

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Chapter 3
Network Architectures and Protocols
for Efficient Exploitation of Spectrum
Resources in 5G

Kande Archana, V. Kamakshi Prasad, M. Ashok, and G. R. Anantha Raman

Abstract With the emergence of 5G technology with its enhanced architecture,

there are unprecedented possibilities in terms of Quality of Service (QoS), data rate,
latency, and capacity. This chapter throws light on different aspects of 5G tech-
nology and associated technological innovations. It has support for user diversity,
devise diversity, and other dimensions. It focuses on the architecture of 5G tech-
nology, its underlying mechanisms, support for Device-to-Device (D2D) communi-
cation, Multiple-Input Multiple-Output (MIMO) enhancements, advanced interfer-
ence management, enhanced utility of ultra-dense networks, spectrum sharing, and
cloud technologies associated with 5G networks. It also proposes a methodology
with spectrum broker with underlying components with delay-aware and energy-
efficient approach to leverage 5G base stations leading to the reduction of energy
consumption. The concept of queueing delays is considered while proposing the
scheme for delay-sensitive communications with energy efficiency. The spectrum
broker has mechanisms to achieve this. Simulation study with the proposed scheme
shows the energy efficiency of the proposed scheme when compared with the state
of the art. This chapter not only provides the required know-how on different tech-
nologies but also provides the simulation study that may trigger further investigation
into the resource management in 5G networks.

K. Archana (B)
Research Scholar, Department of Computer Science and Engineering, Jawaharlal Nehru
Technological University, Hyderabad, Telangana 500057, India
e-mail: kande.archana@gmail.com
Assistant Professor, Department of Computer Science and Engineering, Malla Reddy Institute of
Engineering and Technology Maisammaguda, Hyderabad, Telangana 500100, India
V. Kamakshi Prasad
Department of Computer Science and Engineering, Jawaharlal Nehru Technological University,
Hyderabad, Telangana 500057, India
e-mail: kamakshiprasad@jntuh.ac.in
M. Ashok · G. R. Anantha Raman
Department of Computer Science and Engineering, Malla Reddy Institute of Engineering and
Technology Maisammaguda, Hyderabad, Telangana 500100, India

© The Author(s) 2023 45

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_3
46 K. Archana et al.

Keywords D2D · 5G radio access networks · 5G mobile cognitive radio ·

Networks · Spectrum broker · 5G mobile communication system

Introduction

Enabler Technologies

Mobile technology and communications have undergone a sea change in the recent
past. Mobile technology has been evolving at a rapid pace, and mobile devices,
since the current millennium, have witnessed many features including gaming, GPS
navigation, messaging, and so on. The mobiles are even able to participate in cloud
computing in the form of Mobile Cloud Computing (MCC). Largely computer tech-
nology also depends on small hand-held smart devices. Tablet computers asso-
ciated with mobile computing have become popular phenomenon. Mobile tech-
nology plays a vital role in digital infrastructure used for computing. The 5G
is fifth-generation technology which is the successor of 4G. The 5G technology
provides wider frequency bands besides increased spectral bandwidth. It is better
than its predecessor in terms of peak bit rate, spectral efficiency, increased device
connectivity, concurrency, speed, low battery consumption, assured connectivity
in all geographical regions, increased device support, low-cost infrastructure, and
reliability.
As presented in Fig. 3.1, the enabler technologies of 5G have their role in making
it useful in real-world applications. The technologies are associated with network
management, massive MIMO, millimeter wave, heterogeneous networks, conver-
gence of access and backhaul, massive machine-type communication, communi-
cation with low latency, ultra-lean design, spectrum sharing, and flexibility. The
sections below in this book provide more details on the architecture of 5G tech-
nology, its underlying mechanisms, support for Device-to-Device (D2D) communi-
cation, Multiple-Input Multiple-Output (MIMO) enhancements, advanced interfer-
ence management, enhanced utility of ultra-dense networks, spectrum sharing, and
cloud technologies associated with 5G networks. It also proposes a methodology with
spectrum broker with underlying components with delay aware and energy-efficient
approach to leverage 5G base stations leading to reduction of energy consump-
tion. The concept of queueing delays is considered while proposing the scheme for
delay-sensitive communications with energy efficiency.
The 5G network is based on various design considerations as explored in
Agyapong et al. (2014), Marsch et al. (2016), and Gupta and Jha (2015). It has
inherent support considerations for the use cases of IoT (Khalfi et al. 2017). There is
provision to have better utilization of spectrum with 5G in an energy-efficient manner
(Mavromoustakis et al. 2015). The notion of Software-defined radios (SDRs) with
5G technology can improve its controlling (Lin et al. 2015). With 5G networks,
spectrum harvesting and energy are to be integrated for better results (Liu et al.
3 Network Architectures and Protocols for Efficient Exploitation … 47

Fig. 3.1 5G enabler technologies

2015). There are some techniques discussed in Zhang et al. (2017) and Yang et al.
(2016) for advanced spectrum sharing in 5G technology. Content-centric orientation
and spectrum sharing are explored for 5G networks (Gur 2019). With 5G network
in place, spectrum sharing among secondary users is investigated in Papageorgiou
et al. (2020). SDN architecture for such networks is studied in Akyildiz et al. (2015).
The contributions of this paper are as follows:
1. Investigation is made into architectures of 5G technology, MIMO usage in 5G,
and D2D communication in 5G.
2. Focused on interference management, spectrum sharing, and ultra-dense
networks with 5G technology.
3. Other areas explored with 5G include cloud computing technologies, design of
energy efficient, and cooperative schemes with an algorithm and empirical study.
The remainder of the paper is structured as follows. Section 3.2 covers the archi-
tecture details of 5G. Section 3.3 throws light on the MIMO technology in 5G.
Section 3.4 explores the D2D communication in 5G. Section 3.5 deals with the inter-
ference management for 5G. Section 3.6 covers the spectrum sharing with cognitive
radio in 5G. Section 3.7 discusses the ultra-dense networks in multi-radio access
technology association in 5G. Section 3.8 throws light on the cloud technologies
48 K. Archana et al.

for 5G. Section 3.9 covers the proposed methodology for the design of energy-
efficient delay-aware cooperative scheme for 5G. Section 3.10 provides challenges
and directions for future work. Section 3.11 concludes the paper.

Architecture of 5G

The 5G technology is the new global standard for mobile networks. It has the potential
to have connectivity to more devices and machines with unprecedented possibilities.
It is aimed at delivering multi-Gbps data speed with reliability, low latency, and
massive capabilities. There are many important use cases of 5G. One important use
case is the 5G network can be used for broad spectrum due to its reliability and high
speed. It enables mission-critical communications and supports IoT technology. The
5G technology offers more flexibility and supports devices that need large-scale
connectivity in future. It leverages mobile broadband, augmented reality, and virtual
reality. The 5G technology bestows numerous benefits such as unprecedented speed,
reliability, supporting future connectivity, and so on.
The 5G technology is equipped with advanced architecture with improved termi-
nals and network elements. It also enables service providers to adopt it with advanced
technologies to render value-added services to their customers. The upgradeability
depends on cognitive radio technology with its features like location, temperature,
and weather. Transceiver is made up of cognitive radio technology to enhance the
operating efficiency. The technology can also distinguish the subtle environmental
changes in its location and provide response to ensure high-quality and uninterrupted
service.
As presented in Fig. 3.2, the 5G architecture is designed based on IP and the
model is meant for both mobile and wireless networks. The 5G system consists
of user terminal and is associated with many autonomous and independent radio
access technologies. Each technology is treated as an IP link in the eyes of the
Internet world. The IP-based approach is to have full control and routing IP packets
in different application scenarios that involve sessions over the Internet between
servers and client applications. It has flexibility provided to user in making decisions
pertaining to routing of packets.
As presented in Fig. 3.3, the policy router layer facilitates communication between
applications and servers. It has provision for different kinds of communications over
IP model. The 5G technology has a master core to have flexible convergence point
for other technologies.
As presented in Fig. 3.4, the master core technology associated with 5G has provi-
sion to have convergence point for different technologies such as photonic routers,
beam transceivers, and nanotechnologies. It has support for concurrent approaches
considering either 5G network mode or IP network mode. It is capable of control-
ling technologies and supports 5G-based deployments. It offers more flexibility,
efficiency, power, and less complicated phenomena. There are many researchers
proposed different architectures based on 5G. For instance, Integrated Access and
3 Network Architectures and Protocols for Efficient Exploitation … 49

Fig. 3.2 Shows architectural overview of 5G

Backhaul (IAB) architecture is proposed in Ranjan et al. (2022) for exploiting 5G

technology. The 5G mobile architecture is discussed in Tudzarov and Janevski (2011),
while the key enabling technologies of the 5G are discussed in Idowu-Bismark et al.
(2019). Evolution of wireless technologies including 5G is explored in Gupta and
Jha 2015 architectural overview of 5G technology.
Arora et al. (2020) explored the evolution of 5G network in terms of Internet of
Things (IoT) technology. They discussed the importance of 5G networks in order
to solve the problems of IoT in the area of speed and convergence. Ghosh et al.
(2019) also focused on the evolution of 5G technology. It has covered time-sensitive
communication, integrated access, and backhaul. Ahmad et al. (2020b) discussed
the evolution of IoT in order to use 5G technology. They found that 5G brings
several benefits to IoT use cases. Goyal et al. (2021) explored about architecture
and underlying aspects of a green 6G network. Arsh et al. (2021) explored design
requirements and further developments needed to have IoT that makes use of IoT.
Chetri and Bera (2019) studied the possibilities of using 5G in IoT use cases in order
to reap its benefits in terms of speed and fulfillment of the user needs.
50 K. Archana et al.

Fig. 3.3 Shows communication between client applications and server

Fig. 3.4 Master core technology associated with 5G

Multiple-Input Multiple-Output Technology in 5G

Antenna Array for MIMO in 5G

In communications with wireless technology, MIMO is an improvement over its

predecessor models for improving efficiency. MIMO enables a configuration that
supports receiving and sending multiple signals concurrently. In fact, it is an improved
and smart antenna technology that leverages performance in wireless communica-
tions. It is suitable for 5G technologies to reap its benefits. For 5G terminal, Abdullah
3 Network Architectures and Protocols for Efficient Exploitation … 51

Fig. 3.5 Antenna array for MIMO in 5G

et al. (2019b) designed multiple antennas in order to realize MIMO technology. It is

presented in Fig. 3.5.
It is designed for 5G mobile terminal that can operate at 2.6/3.5 GHz. Single
antenna is used in traditional communication systems. It has many issues such as
obstructions in the propagation line. It is overcome with MOMO technology and
that can be used to exploit 5G benefits. MIMO technology has many applications
like home networks, mobile communication networks, wireless local area networks
(WLANs), and digital television (DTV). More on the MIMO technology usage, its
future possibilities are found in Sharawi (2017). For 5G mobile terminals, the MIMO
system is explored in Abdullah et al. 2019a.

Device-to-Device Communication in 5G

D2D communication refers to the communication that is directly between two devices
without having conventional infrastructure such as access point. There are technolo-
gies like Wi-Fi Direct and Bluetooth that support D2D communication as explored in
Shen (2015). Cellular networks have no support for direct communication between
devices. Among different possibilities with 5G networks, D2D is one of the kinds
52 K. Archana et al.

of communication expected. With the advanced standard of Long-Term Evolution

(LTE), D2D is expected to be realized in 5G cellular networks. With 5G cellular
networks, mmWave technology is becoming popular. In such networks also D2D is
expected. This technology is known for high propagation loss and short wavelengths
and inability to penetrate into solids. These conditions provide suitability to realize
D2D communications.
As presented in Fig. 3.6, there is a visual representation showing the approach
in which cellular communication and D2D communication takes place. For service
providers, D2D communication has been not viable. However, with the emergence of
5G, the subject of D2D became important once again. There are many use cases for
D2D such as local data services, coverage extension, and Machine to Machine (M2M)
communication. With respect to efficient spectrum usage, secondary users associated
with Cognitive Radio Network (CRN) can exploit D2D communication that has a
potential to get rid of interference to primary users of the spectrum. D2D is also
expected to complement MIMO-enabled networks and HetNets with high data rates
and efficiency. There is possibility of noise resilience and enhanced system capability
with MIMO and D2D technologies. System capacity is significantly enhanced with
D2D with MIMO devices.
As presented in Fig. 3.7, there is direct communication between two mobile
devices. It is possible with “operator controlled link establishment.” In this kind of
D2D communication, both devices can exchange information without the involve-
ment of base station. However, they are to be supported by the operator in order to
have link established. As discussed in Adnan and Ahmad Zukarnain (2020), there are

Fig. 3.6 Illustrates

device-to-device
communication
3 Network Architectures and Protocols for Efficient Exploitation … 53

Fig. 3.7 Overview of D2D communication

many challenges associated with D2D communication. The challenges are in selec-
tion, mode, control, power, security, privacy, management, interference, discovery,
and device.

Interference Management for 5G

Multi-tier 5G Architecture

Interference is one of the issues in 4G technology. Due to density in cell deploy-

ment, it caused interference. Especially co-channel interference is the main problem
with traditional cellular networks. As discussed in Nam et al. (2014) interference
management can be done with many techniques such as advanced receiver and joint
scheduling. They proposed two kinds of interference management techniques with
5G. They are categorized into UE-side and network-side management of interfer-
ence. In Hossain et al. (2014), it is opined that multi-tier architecture of 5G has
mechanisms to have advanced interference management. 5G also promotes D2D
communication, Quality of Service (QoS), and energy efficiency in spectrum usage.
It is made possible with multi-tier architecture of 5G technology as presented in
Fig. 3.8.
The 5G technology with its multi-tier architecture enables tier-based and traffic-
based priorities. It has provision for energy efficiency, latency, and reliability. It
54 K. Archana et al.

Fig. 3.8 Multi-tier 5G architecture

also minimizes interference in the system. There are certain key challenges associ-
ated with interference management. The challenges include heterogeneity, balancing
traffic load and coverage, restrictions of public and private usage, and management
of priorities. Interference management in 5G is explored with system model and
analysis can be found in Sanguinetti et al. (2015).

Spectrum Sharing with Cognitive Radio in 5G

Spectrum sharing is an important aspect of wireless communications. With respect

to CRNs, there are users like primary users and secondary users. Wireless network
usage is rapidly increasing due to its advantages and conveniences. However, design
of a wireless communication system is challenging as it needs to consider many
factors such as spectrum utilization efficiency, high Quality of Service (QoS), larger
channel capacity, and higher data rate in order to meet the requirements of wireless
users. Cognitive Radio (CR) is the technology innovation in which wireless commu-
nications are more flexible. It enables users to modify transmitting and receiving
parameters without causing interference to primary and secondary users in order to
achieve more efficient performance in wireless communications. There are two types
of users known as Primary Users (PUs) and Secondary Users (SUs) based on the
priority in the assigned radio frequency spectrum. Under a specified policy, both the
users can coexist in the system. The licensed users of the band are known as PUs.
And PUs can least it to SUs in such a way that SUs use the spectrum without causing
harmful interference to PUs in either underlay or overlay approach. Spectrum sensing
is an important function of CR system in an overlay fashion. It is meant for finding
the presence of PUs in the given frequency band. Cooperative spectrum sensing
3 Network Architectures and Protocols for Efficient Exploitation … 55

(CSS) is “a solution to enhance the detection performance, in which secondary users

collaborate with each other to sense the spectrum to find the spectrum holes.” With
5G technology, spectrum sharing has improved possibilities while serving different
enabling networks. The concept of dynamic spectrum sharing is thoroughly discussed
in Ahmad et al. (2020a), while how full spectrum sharing takes place with different
approaches is found in Feng and Bing (2016). In Briones-Reyes et al. (2020), a
Markov process is discussed in detail to have spectrum sharing efficiently for 5G
systems with a mathematical model.

Ultra-dense Networks in Multi-radio Access Technology

Association in 5G

Ultra-Dense Networks (UDNs) are the networks where there are more cells present
than the users. Such networks are found to be useful to handle explosive data traffic
in 5G networks in future.

UDN Moving Cells with 5G+ Network

It has heterogeneous cells even that can be moving in 5G environments. Instead of

static RANs, it is possible to have dynamic and moving RANs in future with the
emergence of 5G technology. This concept is explored with different possibilities in
the world beyond 5G capabilities in Andreev et al. (2019).
Figure 3.9 illustrates the possibilities of moving ultra-dense networks with 5G+
design. There are different unprecedented possibilities with moving cells of UDN
with 5G networks. The possibilities include predictive handover, multi-connectivity,
dynamic blockage, fleet coordination, swarm formation control, on-the-fly associ-
ation, space–time methodology, proactive cell discovery, dual mobility, computa-
tion offloading, and dynamic association. As discussed in Habbal et al. (2015), it
is possible to have a selection of context-aware radio access technology when 5G
networks are used with UDNs. They opined that extreme densification has improved
capabilities with 5G technology.

Cloud Technologies for 5G

Cloud computing has enabled many services that can be rendered in on-demand
fashion. With the emergence of 5G technology, it is possible to have related services.
As explored in Sabella et al. (2015), it is possible to have cloud technologies that
56 K. Archana et al.

Fig. 3.9 UDN moving cells

with 5G+ network

provide benefits associated with 5G. They proposed an architecture known as Cloud-
RAN that provides RAN services in pay per used fashion. In other words, they
proposed a novel service known as RAN as a Service (RANaaS) that consists of the
required cloud-based infrastructure to render RAN services. The proposed virtualiza-
tion infrastructure is associated with the RAN service. Similarly, in Rost et al. (2014),
cloud technologies are leveraged to support flexibility in 5G RANs. Provision of RAN
services is provided through cloud offering based on the runtime needs of systems.
RAN services are centralized and appropriate software functionality is provided.
As discussed in Al-Falahy and Alani (2017), 5G technology-related services can be
rendered with cloud services. Different 5G enabler technologies aforementioned can
be used in cloud in order to provide scalable, on-demand, and location-transparent
services.

Methodology

The Methodology of the Proposed Scheme

This section provides a methodology for the design of energy-efficient delay-aware

cooperative scheme for 5G. The proposed scheme is energy-efficient and delay-aware
that is used to ensure that the 5G technology usage is made with energy efficiency.
The proposed scheme is built based on the architecture shown in Fig. 3.10. It has an
important component known as spectrum broker. Spectrum broker is responsible to
exploit TV White Spaces (TVWS) by managing the process associated with energy
consumption. TVWS consists of spectrum available to support the design of energy-
efficient systems. The proposed methodology works based on queueing delays.
3 Network Architectures and Protocols for Efficient Exploitation … 57

Spectrum Broker
5G Base
Radio Resource Management Station

TV White
Energy Energy
Spaces
Controller Module
Respiratory

Spectrum Band

Fig. 3.10 The methodology of the proposed scheme

As presented in Fig. 3.10, the proposed methodology has spectrum broker which
takes care of energy efficiency by coordinating other components. The concept
of queueing delays is considered while proposing the scheme for delay-sensitive
communications with energy efficiency. The spectrum broker has mechanisms to
achieve this. The broker coordinates with energy module in order to ensure energy-
efficient communications. Simulation study with the proposed scheme shows the
energy efficiency of the proposed scheme when compared with the state of the art.

Spectrum Band

The 5G technology offers diversified spectrum bands that meet different require-
ments. For instance, a low-band spectrum with less than 1 GHz is desirable to many
customers through it can provide latency incrementally.

5G Base Station

The 5G base station is a very powerful device that can help in supporting latency and
also connectivity beyond the existing standards.
58 K. Archana et al.

Energy Module

Energy module is responsible to help energy controller in order to achieve energy-

efficient approaches with 5G technology.

Energy Controller

Energy controller works in tandem with energy module and radio resource manage-
ment module for achieving energy efficiency.

Algorithm 1 Pseudocode of the proposed scheme

1. Start
2. Set 5G base stations
3. Activate radio resource management
4. While(true)
5. For each space in tv white space repository
6. Compute energy consumption and delay
7. Save it to map
8. End For
9. Find energy efficient spaces with least delay
10. Notify energy controller
11. Energy controller assists energy module
12. Energy module assists base station
13. Results in energy efficiency
14. End while
15. End

The proposed scheme is delay-aware and energy efficient. It continuously moni-

tors TVWS in order to have opportunities to help 5G base stations to have
energy-efficient functionality.
As presented in Fig. 3.11, the energy efficiency of the proposed system is compared
with the scheme of Mekikis et al. (2014). The proposed scheme is found to be
energy-efficient.
3 Network Architectures and Protocols for Efficient Exploitation … 59

ENERGY EFFICIENCY COMPARISON

10
Delay per request (s)
8

0
10 20 30 40 50 60 70 80 90 100 110 120 130 140
Energy (Kbit/J)

The Scheme in [22] Proposed Scheme

Fig. 3.11 Performance comparison in terms of energy efficiency

Challenges and Future Research Directions

Exploration of spectrum resources in 5G has its advantages in order to have better

optimization of resource usage. Though 5G technology is found to be a game changer,
it has certain challenges that are to be understood and addressed. The first challenge is
to have 5G infrastructure or network development which needs small-cell technology
in order to increase network capacity. The second challenge is that of the cost involved
in the 5G infrastructure. Microcell costs are more and the cost is going to play its role
in making obstructions in its adoption. The third problem is that 5G technology has
issues with backhaul and coordination with current cellular technology. The fourth
problem is that there is a challenge related to the provision of necessary bandwidth
with wave spectrum in the presence of trees, buildings, and other objects acting as
obstacles. The fifth challenge is that 5G has certain security challenges in terms of
vulnerabilities in the existing infrastructure and new infrastructure. In the presence
of these challenges, it is essential to have further research to acquire ways and means
to handle these challenges.

Conclusion

This chapter has focused on different aspects of 5G technology and its impact on
various communication technologies. It presents the architecture of 5G technology,
its underlying mechanisms, support for D2D communication, MIMO enhancements,
advanced interference management, enhanced utility of ultra-dense networks, spec-
trum sharing, and cloud technologies associated with 5G networks. It also proposes
60 K. Archana et al.

a methodology with spectrum broker with underlying components with delay-aware

and energy-efficient approach to leverage 5G base stations leading to reduction of
energy consumption. The concept of queueing delays is considered while proposing
the scheme for delay-sensitive communications with energy efficiency. The spectrum
broker has mechanisms to achieve this. Simulation study with the proposed scheme
shows the energy efficiency of the proposed scheme when compared with the state
of the art.

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Chapter 4
Wireless Backhaul Optimization
Algorithm in 5G Communication

Astha Sharma, Mukesh Soni, Abhaya Nand, Suryabhan Pratap Singh,

and Sumit Kumar

Abstract A wireless backhaul optimization approach using a delay jitter is

suggested to handle the wireless backhaul issue for 5G dynamic heterogeneous situa-
tions. First, the delay and delay jitter issues in 5G dynamic heterogeneous situations
are carefully evaluated, optimization indicators are defined, and the fundamental
backhaul model is further built. Then, considering the optimization action needs,
include delay constraints to create better model 1; examine network overload, relax
channel number allocation variables to construct improved model 2, and present a
matching hierarchical method for a quick solution. The simulation results reveal that
the suggested approach has improved delay jitter performance compared with three
kinds of current wireless backhaul optimization algorithms.

Keywords 5G communication · Wireless network · Heterogeneous scenarios ·

Hierarchical algorithm · Backhaul algorithm

The original version of this chapter was revised: The name of the author Astha Sharma and the
affiliation have been updated. The correction to this chapter is available at
https://doi.org/10.1007/978-981-99-3668-7_14

A. Sharma
GL Bajaj Institute of Technology and Management, Greater Noida, India
M. Soni (B)
Department of CSE, University Centre for Research & Development Chandigarh University,
Mohali, Punjab, India
e-mail: soni.mukesh15@gmail.com
A. Nand
IIMT College of Management, Greater Noida, India
S. P. Singh
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University,
Gorakhpur, India
S. Kumar
Indian Institute of Management, Kozhikode, India
e-mail: sumit01phdpt@iimk.ac.in

© The Author(s) 2023, corrected publication 2023 63

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_4
64 A. Sharma et al.

Introduction

With the rapid development of science and technology and the continuous improve-
ment of people’s living standards, various types of mobile terminal equipment and
mobile Internet services are widely used. It is estimated that by 2030, the number of
mobile terminals will be close to 100 billion, and mobile service traffic will increase
by nearly 20,000 times (Ge et al. 2019; Wang et al. May 2016). It will be difficult
for the existing communication system to meet the access requirements of massive
terminals and services in the future. In view of this, the fifth generation mobile
communication system (5G) (Zhang et al. 2020; Madapatha et al. 2021) emerges
as the times require. On the other hand, with the gradual rise of various interactive
multimedia services (such as video conferencing and online games, etc.), delay and
delay jitter have increasingly become the most important QoS indicators, and become
the key to user service experience (Chaudhry et al. 2020a; Ahmad 2015). The current
research mainly focuses on the analysis of the delay characteristics of data packets,
and there are relatively few studies on delay jitter. Therefore, it is imperative to study
the delay jitter for the 5G environment.
The control of service delay and delay jitter is mainly implemented at the network
link layer with the packet as the granularity, and the forwarding scheduling and queue
management of the data packets are carried out through the network nodes. At present,
the results of studying delay jitter performance with packet as granularity are rare.
References (Rezaabad et al. 2018; Hore et al. 2021) have explored the network delay
jitter characteristics. Reference (Chaudhry et al. 2020b) uses delay mean square error
to approximate the delay jitter for multi-hop wireless Mesh networks and obtains the
delay jitter by solving the end-to-end delay distribution. It should be noted that most
of the literature on delay jitter research is based on single-point systems, such as
only studying the delay jitter of wireless access network or wired core network (Tran
and Le 2018; Pham et al. 2019). However, the 5G network must be a complex and
diverse heterogeneous network, and the backhaul network is an important part. The
unified consideration of the access network and the backhaul network can more truly
describe the future 5G network.
Therefore, in the assessment of the existing problems, this paper suggests an
optimal channel resource allocation algorithm with delay jitter as the optimization
index for the 5G hybrid backhaul scenario. Considering the dynamic characteristics
of the channel, the delay jitter problem is comprehensively analyzed, the delay jitter
index is obtained, and then various backhaul optimization models are constructed.
Finally, the hierarchical algorithm is used to solve the problem quickly.
The rest of the chapter is organized as follows: section “Network Scenarios
and System Assumptions” covers the 5G network scenario, section “Analysis of
Delay Jitter Indicators” includes the analysis of delay jitter indicators, section “Opti-
mization Model Establishments” includes the proposed model description, section
“Improved Model Solutions” includes the improved model, section “Simulation
Analysis” includes the simulation detail and result analysis, and at last, section
“Conclusions” includes the conclusion and future work.
4 Wireless Backhaul Optimization Algorithm in 5G Communication 65

Network Scenarios and System Assumptions

As shown in Fig. 4.1, for a two-layer heterogeneous network scenario, the upper
layer is a Backhaul Aggregator Node (BAN). The lower layer is multiple Small-Cell
Base stations (SCBS) covered by the BAN. Dedicated optical fibers link the BAN
to the rest of the network, and millimeter waves are used for communication with
SCBS or end customers (Iradukunda et al. 2021).
While SCBS uses frequency bands below 6 GHz (Liu et al. 2020) to communicate
with users, here, the BAN combines the functions of an aggregator and base station
access. Considering the hybrid backhaul scenario, there are two backhaul methods
to choose from: the first one, the user can attach to the BAN via a one-hop wireless
connection, and the BAN will handle the backup via an Ethernet cable; the second
one, the user can access the SCBS to which he belongs, and then owned by.
The SCBS accesses the BAN and the core network through a two-hop wireless
link.
Define SCBS set SC = {C 1 , C 2 ,…, C 1 ,…, C L }, define the user set covered by
cell C 1 as UE1 = {UE11 , UE12 ,…, UEln ,…, UElNl }, where UEln represents the nth
user, N l is the number of users in the cell SC1 . It is assumed that all SCBSs have a
common ∩ channel. Now, the seamless coverage of the entire network is disjoint, that
is, UEi UEj = ∅ (i /= j). Therefore, the set of users is UE = {UE1 , UE2 ,…, UE1 ,…,
ΣL
UEL }, then the number of users N = l=1 Nl .
The access selection vector is defined as

Fig. 4.1 5G two-layer heterogeneous network scenario

66 A. Sharma et al.

A = {a11 , a12 , . . . , a1N2 , a21 , a22 , . . . , a2N2 , . . . , a L1 , a L2 , . . . , a L N L }

Among them, aln = 1 (n ≤ N l ) means that user n in cell C 1 accesses the BAN,
and the BAN performs the backhaul; aln = 0 (n ≤ N l ) means that the user n in cell
C 1 accesses the C l , and the C 1 performs the backhaul.
The channel allocation matrix that defines SCBS is
⎡ ⎤
b11,1 b11,2 . . . b11,M1
⎢ b12,1 b12,2 . . . b12,M1 ⎥
⎢ ⎥
⎢ .. . ⎥
⎢ . .. b .. ⎥
⎢ ln,m .
⎥
⎢ ⎥
⎢ b1N1 ,1 b1N1 ,2 . . . b1N1 ,M1 ⎥
⎢ ⎥
⎢ ⎥
⎢ b21,1 b21,2 . . . b21,M1 ⎥
⎢ ⎥
B=⎢ b22,1 b22,2 . . . b22,M1 ⎥
⎢ ⎥
⎢ .. . ⎥
⎢
⎢ . .. . . . ... ⎥
⎥
⎢ ⎥
⎢ b1N2 ,1 b2N2 ,2 . . . b2N2 ,M1 ⎥
⎢ ⎥
⎢ .. . ⎥
⎢ . .. . . . ... ⎥
⎣ ⎦
b L N L ,1 b L N L ,2 . . . b L N L ,M1

Among them, bln,m = 1 shows that the bandwidth W m is allocated

{ to the user}
UEln , and bln,m = 0 suggests that it is not given. Here, W = W1 , W2 , . . . , W M1
denotes the channel vector that SCBS can allocate, and M 1 is the total number of
SCBS channels.
The BAN channel distribution vector is defined as below, where M 2 is the total
number of channels in BAN

C = {c11 , c12 , . . . , c1N1 , . . . , a11 , a12 , . . . , cln , . . . , cl Nl , . . . , c L1 , c L2 , . . . , c L N L }

The number of BAN channels assigned to UEln is denoted by cln and cln 1(l,n).
It’s important to remember that users require a certain quantity of BAN capacity
whether they use the BAN backhaul or the SCBS backhaul. cln displays the number
of channels assigned to the Cl BAN link in the UEln Cl BAN route for UEln packet
transmission if aln is set to 1; otherwise, cln displays the number of channels assigned
to the UEln BAN link for UEln data packet transmission if aln is set to 0.
The paper’s method has been based on the following principles:
(1) Assuming that the channels are discrete, the BAN can assign different tracks to
any SCBS or a particular user. The SCBS can also set other numbers of media
to a specific user.
(2) For the service uplink transmission scenario, study the delay jitter index based
on packet granularity.
4 Wireless Backhaul Optimization Algorithm in 5G Communication 67

(3) To simplify interference analysis, we will presume that the BAN and SCBS
channels have the same total bandwidth, and the BAN’s total bandwidth should
be larger than the SCBS’s total bandwidth.
(4) If the link’s transmission rate is higher than its channel capacity, wireless packet
loss will occur; each SCBS has an unlimited wait buffer to prevent packet loss
from accumulating (or overcrowding); and if the link experiences packet loss,
the transmission mechanism will resume to transmit the packet again.

Analysis of Delay Jitter Indicators

In the wireless communication environment, delay jitter is a physical quantity that

measures the delay change experienced by data packets during network transmis-
sion—packet delay jitter. Therefore, to analyze the delay jitter problem, it is neces-
sary to examine the delay problem first. Propagation delay and transmission delay
are components of link delay. Propagation delays are affected by a number of factors,
including the distance over which a signal must travel and the pace at which electro-
magnetic waves travel. (i.e., the speed of light). Due to the short distances over which
5G signals must travel, transmission delays are negligible. However, data fragment
transfer causes a delay, which is known as transmission delay. Therefore, the analysis
of the service delay and delay jitter system is analyzed separately below.

Delay Analysis

1
In this scenario, there are three types of wireless links for UEln : the link lln for UEln
2
to access BAN, the link for UEln to access SCl lln , and the link for SCl to access
3
BAN correspondingly lln . All link channels are assumed to be subject to small-scale
1 3
Rayleigh fading. For links lln and lln , since the same track is not repeatedly allocated
in the BAN, there is no interference in the BAN;
Both use millimeter wave communication, and the distance between BANs is
relatively long. The above analysis denotes the signal-to-interference noise ratio of
i
the link lln i
as SINRln . If SINRln i
≥ SINRth , the transmission is considered successful,
i i
and the bit error rate BERln of the link lln is calculated.
Channel error correction coding is linked to the data loss rate. It is believed that
the capacity of all packages is PL (PL ≥ 3). According to this report, the data package
has three mistakes, or bits, and the box is presumed gone. If there is a loss of packets
and the data has to be reissued, the packet loss rate is
PL P L−1
i
P E R ln = 1 − (1 − B E R ln
i
) − C 1P L (1 − B E R ln
i
)
P L−2 2
i
B E R ln − C 2P L (1 − B E R ln
i
) (B E R ln
i
) (4.1)
68 A. Sharma et al.

Cθϑ represents the number of combinations to select ϑ elements from θ elements.

i
Link lln is derived from the packet loss rate average transmission delay for
( ) ( ) ( )
i RT
τlni = 1 − P E R lni
T + P E R ln
i
1 − P E R ln
i
2T + · · · + P E R ln
( ) ( )
i RT +1
1 − P E R ln
i
(RT + 1)T + P E R ln (RT + 1)T (4.2)

T represents a transmission delay, while RT is the greatest number of retransmis-

sions.
The latency study of the UE → BAN route is straightforward once we move
beyond the connection level. With a one-hop wireless link, the latency introduced
by the cable central network is negligible and not worth considering. Therefore, for
the UE → SC → BAN path, the time for Packet1, Packet2, Packet3, …, Packet
k to reach SC1 isτln2 , 2τln2 , 3τln2 , ···, k τln2 in sequence. If τln2 ≥τln3 , there is no queue
congestion at SCl , so the time to reach BAN is τln2 +τln3 , 2 τln2 +τln3 , 3 τln2 +τln3 ,· · · , k
τln2 +τln3 . If τln2 <τln3 , there is a queuing delay in SCl , so the queuing delay in SCl is 0 in
sequence, τln3 −τln2 ,2(τln3 − τln2 ),· · · , (k − 1) (τln3 − τln2 ), the time for each data packet to
arrive at the BAN is is τln2 + τln3 , τln2 + 2τ ln 3
, τln2 + 3τln3 ,· · · , τln2 + kτln3 . Combining the
above two situations, the path delay of UE → SC → BAN can be analyzed from the
perspective of the expected average; the packet arrival delay interval is max{τln2 ,τln3 },
and the initial delay is τln2 +τln3 .

Delay Jitter Analysis

i
Further, the delay jitter of the link lln can be calculated as
( ) ( )
i = 1 − P E R i (T − τ i )2 + P E R i 1 − P E R i (2T − τ i )2 + · · · + (P E R i ) RT
σln ln ln ln ln ln ln
( ) 2 RT +1 2
i [(RT + 1)T − τ i ] + (P E R i )
1 − P E R ln [(RT + 1)T − τlni ] (4.3)
ln ln

Among them, τlni represents the mean transmission delay of the link lln i i
, P E R ln
represents the packet loss rate, and T represents the one-time transmission delay.
Furthermore, Eq. (4.3) describes the fluctuation degree of the transmission delay
i
of the link lln relative to the average delay in the form of mathematical variance
(Chaudhry et al. 2020b).
Spreading from the link to the path, for the transmission of the first data packet,
since there is no waiting delay, each component link can be regarded as independent
of each other, so the initial delay jitter of the path is the sum of the delay jitter of
each component link. For the transmission of subsequent data packets, there are two
situations of queuing and non-queuing at intermediate nodes. The packet retrans-
mission mechanism makes the calculation of waiting delay more complicated. At
this time, each component link can be regarded as interrelated, so the path. The
delay jitter should be less than the sum of the delay jitters of each component link.
4 Wireless Backhaul Optimization Algorithm in 5G Communication 69

For simplicity, this paper uses the maximum delay jitter of each component link to
approximate the delay jitter of the entire path.
Analyzing the latency and disturbance on the UE → BAN route (where the BAN
immediately backhauls UEln ) is straightforward. The cable network’s backbone can
be reached via a single wireless step. It assumes that the wired connection has no
delay jitter, the path wireless. The initial delay jitter and the average delay jitter of
the incoming side are both σlni . For the UE → SC → BAN path (i.e., the backhaul
path where UEln accesses BAN via SC1 ), its initial delay jitter is σln2 + σln3 , and the
average delay jitter of subsequent packets is max (σln2 , σln3 ).

Optimization Model Establishments

Basic Backhaul Model

The optimization goal can be written as, based on the delay jitter study above:
{ Σ Σ
[X ∗ , Y ∗ , Z ∗ ] = arg min U = (1 − aln )
∀aln ,bln,m ,cln ∀l ∀l
[ ( )] }
r. max σln2 , σln3 + aln · σln1 (4.4)

Among them, U represents the optimization objective function of the basic back-
haul model, which can be regarded as the sum of the average delay jitter of all user
backhaul paths, X*, Y *, and Z* represent the optimal solutions of X, Y, and Z, respec-
tively, and r ≥ 1 is the initial delay jitter compensation factor to reflect the effect of
the initial delay jitter. In particular, when max(σln2 , σln3 ), the average delay jitter of
the two types of paths is the same. In this case, UE → BAN with a more negligible
initial delay jitter will be selected as the optimal path.
Then, the basic optimization model can be established:
{Σ Σ [ ( )] {
arg min (1 − aln ) r. max σln2 , σln1 + aln , σln1 (4.5)
∀aln ,bln,m ,cln ∀l ∀l

( )
Σ
M1
s. t. min bln,m , aln = 0, ∀1, n (4.6)
m=1

Σ
Nl
bln,m ≤ 1, ∀l, m (4.7)
n=1
Σ
cln ≤ M2 (4.8)
∀l,n

aln ∈ {0, 1}, ∀l, n (4.9)

70 A. Sharma et al.

bln,m ∈ {0, 1}, ∀l, n, m (4.10)

cln ≥ 1, ∀l, n (4.11)

Based on Eq. (4.6), UEln uses either the SCBS backhaul (for which the SCBS
allocates multiple bandwidths) or the BAN backhaul (for which the SCBS does not
assign a channel). According to Eq. (4.7), in order to prevent intra-cell crosstalk,
each track within a given cell is only shared by a single user. In Eq. (4.8), M2 is the
highest number of channels that can be provided by BAN, so the number of channels
allotted by BAN must be less than or equal to M2. Equations (4.9) and (4.10) define
the value space of aln and bln,m . Finally, Eq. (4.11) indicates that the BAN bandwidth
needs to be allocated no matter how the user is backhauled.

Improved Model 1

The basic model only optimizes the delay jitter and ignores the optimization of a
service delay, so the delay interval constraint of the backhaul path of user UEln is
added:
[ ( )]
(1 − aln ) r. max σln2 , σln3 + aln · τln1 ≤ εln , ∀l, n (4.12)

Among them, εln represents the maximum delay constraint to ensure the basic
delay requirements of the business. Therefore, an improved model 1 is constructed:
{Σ Σ [ ( )] {
arg min (1 − aln ) r. max σln2 , σln3 + aln · σln1 (4.13)
∀aln ,bln,m ,cln ∀l ∀l

( )
Σ
M1
s.t. min bln,m , aln = 0, ∀1, n (4.14)
m=1

Σ
N1
bln,m ≤ 1, ∀l, m (4.15)
n=1
Σ
cln ≤ M2 (4.16)
∀l,n

[ ( 2 3 )]
(1 − aln ) r. max σln, σln, + aln . τln1 ≤ εln , ∀l, n (4.17)

aln ∈ {0, 1}, ∀l, n (4.18)

4 Wireless Backhaul Optimization Algorithm in 5G Communication 71

bln,m ∈ {0, 1}, ∀l, n, m (4.19)

cln ≥ 1, ∀l, n (4.20)

Improved Model 2

When the number of users exceeds the number of available channels on the network
(that is, the network is overloaded), the above model will face an unsolvable problem,
namely:

Σ
L
N= Nl > M 2 (4.21)
l=1

Σ
At this time, even if cln = 1(∀l,n), there is still ∀1,n cln = N > M2 .
Since there is insufficient BAN route to satisfy all demands, the admittance control
system must be triggered, disappointing some users. As for which users are dismissed
and how resources are allocated to admit users, an improvement model 2 needs to
be established:
{ ( Σ ){
arg min U + w M2 − cln (4.22)
∀l,n

( )
Σ
M1
s.t. min bln,m , aln = 0, ∀1, n (4.23)
m=1

Σ
Nl
bln,m ≤ 1, ∀l, m (4.24)
n=1
Σ
cln ≤ M2 (4.25)
∀l,n

[ ( )]
(1 − aln ) r. max σln2 , σln3 + aln . τln1 ≤ εln , ∀l, n (4.26)

aln ∈ {0, 1}, ∀l, n (4.27)

bln,m ∈ {0, 1}, ∀l, n, m (4.28)

cln ≥ 0, ∀l, n (4.29)

72 A. Sharma et al.

where ω is the Σadjustment factor, if ω is small, U is inclined to be optimized; if ω

is large, M2 − ∀l,n cln is inclined to be optimized to allocate resources to users as
much as possible under the premise of ensuring a solution. Also, if cln = 0, the user
request is denied.

Improved Model Solutions

Modified Model 1 and Modified Model 2 are fundamentally integer programming.

To solve the above models quickly, according to their mathematical characteristics,
corresponding solving algorithms are designed based on stratification and branch
and bound.

Improved Model 1 Solution

Modified Model 1 consists of the Boolean vector X, the Boolean matrix Solution Y,
and the number vector Z. This leads to the optimization problem being decomposed
into its component parts. First, the initialization process is given as follows:
Initialization:
1. Generate X = zeros(1, N), Y = zeros(N, M 1 ),
2. Z = ones(1, N)
3. Input: W, C, L, N l , M 1
4. for(bln ∈ (
Y, W m ∈ W ) ) Σ
Σl−1 ∗
5. if m = l=1 N l + n modM 1 ||(( l−1
l=1 Nl + n)/M1 ∈ N )
6. then bln,m ← 1 end if
7. end for
8. forΣW m ∈ W, C l ∈ C
Nl
9. if n=1 bln,m ≥ 2
10. then bln,m, = find(bln,m = 1); bln,m, ← 0; aln ← 1 end if
11. end for
12. Output: Initial solution X,Y,Z
In the first layer, solve for the vector X. Vector X according to Eq. (4.18) to
construct multiple subproblems. Each iteration can obtain an optimal solution to the
current optimization problem. Among them, f f ln represents the value of the formula
on the left side of Eq. (4.17), and f f 0 represents its minimum value. In the algorithm,
f 0 and { fln } represent the objective function value of the current feasible solution.
The first layer of explanation that is, Algorithm 1, is as follows:
4 Wireless Backhaul Optimization Algorithm in 5G Communication 73

Algorithm 1 Algorithm for Computing X Vector

Input: X, Y, Z, M2 , R, RT, PL
Calculate f 0 ;
1. for aln ∈ X
2. if aln = 0 then
3. aln ← 1; bln,m, = find(bln,m = 1); bln,m, ← 0;
4. Calculate f f ln , f ln ;
5. if ( f f ln ≤ εln )&&( f ln < f 0 ) then
6. f 0 ← f ln ;
7. else bln,m, ← 1; aln ← 0; end if
8. end if
9. end for
10. Output: optimized value X*Y, f 0
Matrix Y must be solved in the second layer. Using the first layer’s answer, the
remaining channels in the cell have been allocated so as to fulfill the algorithm (17).
Users who access SCBS get more channels, which will reduce the packet loss rate
accordingly. The second layer of the solution that is, Algorithm 2, is as follows:

Algorithm 2 Algorithm for Computing Y Matrix

Input: X*, Y, Z, W, f 0 , N l , M 2 , εln , R, RT, PL
1. for aln ∈ X
2. if aln = 0 then
3. bln,m, = find(bln,m = 1);
4. for W m ∈ W Σ Nl
5. if (W m /= W m, )&&( n,=1 bln, m = 0) then
6. bln,m ← 1; bln,m, ← 0; Compute f m ;
7. if f m < f 0 then f 0 ← f m ;
8. else bln,m ← 0, bln,m, ← 1 end if
9. end if
10. end for
11. end if
12. end for
13. for aln ∈ X
14. if aln = 0 then
15. while f f ln > εln
16. bln,m, = find(bln,m = 1);
17. for W m ∈ W Σ Nl
18. if (W m /= W m, )&&( n,=1 bln, m = 0) then
19. bln,m ← 1; Calculate f f ln,m ; bln,m ← 0 end if
20. end for
21. f f ln,m ∗ = arg min( f f ln,m ); bln,m* ← 1;
22. end while
23. end if
74 A. Sharma et al.

24. end for

25. for W m ∈ W, aln ∈ X
26. if aln = 0 then
27. Set B_temp = find(bln,mΣ = 1);
Nl
28. if (bln,m ∈ / B_temp)&&( n,=1 bln, m = 0) then
29. bln,m ← 1; Calculate f ln,m , ffln,m ; bln,m ← 0; end if
30. end if
31. [ f ln,m*] = arg min( f ln,m );
32. if ( ff ln,m* ≤ εln )&&( f ln,m* < f 0 ) then
33. f 0 = f ln,m*, bln,m* ← 1 end if
34. end for
35. Output: optimized value of Y *
In this step, solve the matrix Z. After the first and second layers are solved, the
current optimization problem becomes a pure integer programming problem. The
remaining channels of the BAN are allocated, and one remaining channel is allocated
for each traversal. The third layer of the solution that is, Algorithm 3 is as follows:

Algorithm 3 Algorithm for Computing Z Vector

Input: X*, Y*, Z, f 0 , L, N l , M 2 , ε ln , r2 , RT, PL
Σ L Σ Nl
1. while l=1 n=1 cln < M2
2. for cln ∈ Z
3. Z , ← Z; cln = cln + 1; compute f ln , ff ln ; Z ← Z , ;
4. end for
5. [ f l*n* ] = arg min( f ln );
6. if ( ff l*n* ≤ εln )&&( f l*n* < f 0 ) then
7. f 0 ← f l*n* ; cl*n* = cl*n* + 1 end if
8. end while
9. Output: optimized value of Z*
The above method for finding a solution can be seen to break down the initial issue
into a set of three progressively more difficult challenges iteratively solved according
to the characteristics of the solution space. The related problem’s optimal solution
can be obtained using the branch and bound method, and the iterative solution can
gradually converge to the optimal solution of the original problem.

Improved Model 2 Solution

Based on the solution technique of the improved model 1, based on the mathemat-
ical characteristics of the enhanced model 2, a three-layer solution technique of the
improved model 2 is proposed:
In the first layer, solve the matrix X. Relax (25) and set ω = 0. The solution
method is the same as algorithm 1 of the enhanced model 1.
4 Wireless Backhaul Optimization Algorithm in 5G Communication 75

In the second layer, solve matrix Y. The solution process is the same as algorithm 2
of the enhanced model 1.
In the third level, the vector Z must be resolved. It is required to reevaluate the
limits of Eq. (4.25) based on the answers found in the first and second layers and to
conduct N−M2 repetitions. At each step, the person with the largest delay fluctuation
in the present optimization outcome is dropped. Algorithm 4 describes the method
used to find the answer.

Algorithm 4 Algorithm for Solving Z Vector

Input: X*, Y*, Z, L, N l , M 1 , M 2 , ω, ε ln , r, RT, PL
Σ L Σ Nl Σ L Σ Nl Σ M 1
1. While l=1 n=1 aln + l=1 n=1 m=1 bln,m > M2 do
2. for aln ∈ X
3. if aln = 1 then aln ← 0;
4. Algorithm 1; Algorithm 2;
5. compute f ln ; aln ← 1;
6. else for bln,m ∈ Y
7. Set B_temp = find(bln,m = 1);
8. for bln,m ∈ B_temp then bln,m ← 0 end for
9. Algorithm 1; Algorithm 2; Calculate f ln ;
10. for bln,m ∈ B_temp then bln,m ← 0 end for
11. end for
12. end if
13. end for
14. [ f l*n* ] = arg max( f ln ); al*n* ← 0;
15. bl*n*, 1 ← 0, bl*n* ,2 ← 0, …, bl*n* ,M1 ← 0;
16. end while
17. for aln ∈ X Σ M1
18. if (aln = 0)&&( m=1 bln,m = 0) then cln ← 0 end if
19. end for
20. Output: optimized solution Z*

Simulation Analysis

The BAN is positioned in the middle of a 500 m * 500 m, and all four are placed
evenly at regular intervals. To ensure complete coverage, the SCBS has a typical
contact radius of 175 m. Each of the N customers corresponds to one of the N
SCBSs, and the N transmission channels are modeled as Rayleigh fading channels.
Furthermore, the network’s performance will be directly affected by the value of
the number of retransmissions RT: a large RT will increase the transmission delay
but decrease the packet loss rate, while a small RT will decrease the transmission
delay but increase the packet loss rate. To find a happy medium between transmission
lag and packet loss in a real-world system, try RT = 5. Table 4.1 shows additional
simulation parameters.
76 A. Sharma et al.

Table 4.1 Parameters used in simulation

Parameter Parameter Parameter value
α Free space transfer coefficient 3
n BAN
0 Thermal noise of BAN/(dBm · Hz−1 ) −174
n SCBS
0 Thermal noise of SCBS/(dBm · Hz−1 ) −174
ΔBAN Channel bandwidth allocated by BAN/MHz 20
ΔSC Channel bandwidth allocated by SCBS/MHz 20
M2 Number of channels that can be given by BAN 250
PUE User’s transmit power/dBm 7
PSC Transmit power of SCBS/dBm 27
H Packet sending rate /(Mb · s−1 ) 100
PL Packet size/bit 512
r Initial delay compensation factor 1.1
Ω Regulator 1000

In this paper, we propose the Improved Model 1-Based Solved Algorithm

(IM1SA) and the Improved Model 2-Based Solved Algorithm (IM2SA) as two
versions of a model-based solved algorithm for wireless backhaul optimization, and
we model their performance using three distinct algorithms to evaluate their effec-
tiveness. WBOASBS (Gu et al. 2018) is the wireless backhaul optimization algo-
rithm for single backhaul scenarios, and it belongs to the first class of algorithms.
This approach was developed for a particular kind of communication scenario. (that
is, all users access the BAN through SCBS for backhaul). The second category of
method is the Wireless Backhaul Optimization method for Static Channel Scenarios
(WBOASCS) (Zhang et al. 2020). To optimize channel allocation, the third type
of algorithm, WBOABIDJ, uses an average delay jitter index that does not take
into account channel dynamics. This algorithm is considered for hybrid backhaul
scenarios (which include two types of backhaul methods). All three categories of
algorithms are very similar to the one presented in this article, and each one is
developed with the help of MATLAB. Delay jitter is used as an efficiency metric to
evaluate the aforementioned methods. Jitter in this context refers to the time it takes
to implement the outcomes of channel distribution derived from various methods
into a working network. The typical user delay fluctuation number is determined.
In the situation of minimal service demand, Fig. 4.2 compares the delay jitter of
IM1SA, the other three kinds of algorithms, and the number of users (the number of
users is less than M2). Increasing the number of users N causes the delay jitter of the
four distinct methods to rise steadily, as shown below for varying values of M1; when
the number of users is small, the network load is low, and the network performance
is at a better level (that is, the link packet loss rate is standard), at this time, the delay
jitter of the same algorithm under different M1 is very close; when the number of
users is large, the network load is high, and the network performance deteriorates
(that is, the link packet loss rate is high), At this time, the higher M1, the smaller
4 Wireless Backhaul Optimization Algorithm in 5G Communication 77

180
160
140
120
IM1SA M1=100
Delay

100
80 WBOASBS M1=150
60 WBOASCS M1=150
40
WBOABIDJ M1=150
20
0
60 80 100 120 140 160 180 200
Traffic Load

Fig. 4.2 Comparison of delay and jitter of four types of algorithms (low business load)

the delay jitter of the same algorithm; in any case, the delay jitter performance of
IM1SA is always optimal (Table 4.2).
The modeling findings for the light traffic burden scenario are not shown in
Fig. 4.2. Once the number of users rises above M2, all four kinds of algorithms
will be unable to optimize the system; in other words, they will be useless in an
overloaded system. As a result, IM2SA is proposed in this article. In the event of a
heavy service demand, the delay fluctuation of IM2SA varies as shown in Fig. 4.3.
(that is, the number of users is more significant than M2). As the number of users
on IM2SA grows, the delay fluctuation will only vary within a narrow range. The
IM2SA delay fluctuation decreases as the number of users, N, increases. Admission
management and channel allocation can be efficiently executed by IM2SA.
In the case of IM2SA, the delay jitter performance based on IM2SA is always
maintained at a certain level without deterioration (Tables 4.3 and 4.4).
Figure 4.4 shows the different results of the network parameter of IM2SA with
the number of users in the case of a high service load (that is, the number of users is
more significant than M2). It can be seen that the packet delivery ratio, end-to-end
delay, and load of IM2SA fluctuates within a specific range with the increase in the
number of users N. Given the number of users, the delay jitter of IM2SA shows a
downward trend with the rise in M1. IM2SA can effectively carry out admission
control and allocate channels reasonably.

Table 4.2 Comparison of delay and jitter of four types of algorithms (low business load)
60 80 100 120 140 160 180 200
IM1SA M1 = 100 20 30 40 56 59 62 64 66
WBOASBS M1 = 150 40 55 60 78 89 100 102 99
WBOASCS M1 = 150 50 60 80 92 105 125 127 130
WBOABIDJ M1 = 150 60 70 90 110 130 145 150 155
78 A. Sharma et al.

140

120

100

80
Dealy

IM2SA M1 = 100
60
IM2SA M1 = 125
40 IM2SA M1 = 150

20

0
220 340 360 380 400 420 440 460
Traffic Load

Fig. 4.3 IM2SA delay jitter results (high traffic load)

Table 4.3 IM2SA delay jitter results (high traffic load)

220 340 360 380 400 420 440 460
IM2SA M1 = 100 91 93 95 88 90 96 94 91
IM2SA M1 = 125 102 105 104 100 102 106 104 103
IM2SA M1 = 150 112 115 113 111 112 118 114 113

Table 4.4 IM2SA evaluation

Network scenario Packet delivery End-to-end delay Load
over network parameter
ratio (PDR)
IM1SA M1 = 78 56 58
100
WBOASBS M1 72 68 62
= 150
WBOASCS M1 68 72 74
= 150
WBOABIDJ M1 62 75 80
= 150

Conclusions

For 5G dynamic diverse situations, this article suggests an optimization method for
wireless backhaul that takes delay uncertainty into account. Backhaul optimization
metrics are created, and a fundamental backhaul model is built, based on a methodical
4 Wireless Backhaul Optimization Algorithm in 5G Communication 79

80
70
60
50
Rate

40 PDR
30 End to end delay
20
Load
10
0
IM1SA WBOASBS WBOASCS WBOABIDJ
M1=100 M1=150 M1=150 M1=150
Network Senario

Fig. 4.4 IM2SA evaluation over network parameter

study of service delay and delay instability in active diverse techniques. Additionally,
an optimized model 1 and an optimized model 2 are developed from the vantage points
of delay optimization and network overflow, respectively, and a hierarchy method is
suggested to handle them efficiently. This article uses a program to demonstrate the
efficacy of the method.

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Chapter 5
Security Attacks and Vulnerability
Analysis in Mobile Wireless Networking

Ayasha Malik, Bharat Bhushan, Surbhi Bhatia Khan, Rekha Kashyap,

Rajasekhar Chaganti, and Nitin Rakesh

Abstract Security of data is very important while providing communication either

by the wired or wireless medium. It is a very challenging issue in the world and the
wireless mobile network makes it more challenging. In a wireless mobile network,
there is a cluster of self-contained, self-organized networks that form a temporarily
multi-hop peer-to-peer radio network, lacking any use of the pre-determined orga-
nization. As these networks are mobile and wireless connection links are used
to connect these networks through each other, many of the times these kinds of
networks are accomplished of self-manage, self-define, and self-configure. Due to
their dynamic nature, wireless mobile networks/systems do not have a fixed infras-
tructure and, due to this, it is more vulnerable to many types of hostile attacks.
Different kinds of security attacks that are present in wireless mobile networks are
stated in the paper with their spotting and precaution techniques. Furthermore, the
paper deliberates on the various types of mobile networks along with their numerous
challenges and issues. Moreover, the paper defines the need and goals of security in
wireless mobile networks as well as many security attacks along with their detection
or prevention methods.

Keywords Wireless mobile networks · Attacks · Security · Sender · Receiver ·

Cryptography · Detection · Prevention · Vulnerability · Node · Transmission · Data

A. Malik
Delhi Technical Campus (DTC), GGSIPU, Greater Noida, India
B. Bhushan (B) · N. Rakesh
Department of Computer Science and Engineering, School of Engineering and Technology,
Sharda University, Greater Noida, India
e-mail: bharat_bhushan1989@yahoo.com
S. Bhatia Khan
Department of Data Science, School of Science, Engineering and Environment, University of
Salford, Manchester, United Kingdom
R. Kashyap
Noida Institute of Engineering and Technology (NIET), Greater Noida, India
R. Chaganti
University of Texas, San Antonio, USA

© The Author(s) 2023 81

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_5
82 A. Malik et al.

Introduction

For several years now, there has been a clear example seen in daily life as the transfer-
ence from the fixed network to wireless mobile networks for easiness in communica-
tion as there is no need for a licensed frequency band to act and the wireless mobile
network does not require any investment in infrastructure as it can able to form a
dynamic structure (Lohachab and Jangra 2019). These properties have an impor-
tant role to make them appealing for some commercial implementation in various
fields and most important in the military field. As there are many good things in
a wireless mobile network, there is another side too that says, in wireless mobile
network many problems occur; among them, network security is the most important
concern (Nurlan et al. 2022). Mobile technology is rapidly growing, wireless mobile
networks have shown in many forms such as laptops, PDAs, etc. There is a very
high chance for attackers, as in a wireless mobile network, a node can be operated as
a source, destination, and router (intermediate node). Communication in a wireless
mobile network is done through messages, a network can send the data to its adja-
cent network via messages. And these networks do not contain any information about
any other nodes/networks, whether the network is prone to attack or safe. They do
not know each other (Bhushan and Sahoo 2017, 2018). Securing a wireless mobile
network is tough because there are many reasons such as no boundaries, attack from
an unfriendly node into the network, no facility of central management, the power
supply is limited, extension ability, no protection of channels, changes in topology,
etc.
The wireless mobile network usually has small devices which are more memory-
constrained and more susceptible to failures. Although energy is a scarce resource
for both kinds of networks, these networks have tighter requirements on network
lifetime, and recharging or replacing the node’s batteries is much less of an option
(Cuka et al. 2018). The basic purpose is focused on providing distributed computing
and information gathering. Wireless mobile networks are used in environments like
forests, mountains, rivers, etc. In order to be counterproductive and try to predict
natural calamities such as forest fires, quakes, floods, cloudbursts, etc. (Bhushan and
Sahoo 2020a; Han et al. 2019). Wireless mobile networks can be used for moni-
toring outgoing services, equipment, and nodes. It can also be used in surveillance of
battlefield, atomic, biotic, and chemical attack detection. Wireless mobile networks
can be used in health applications for telemonitoring of human physiological data, in
telecare medicine information systems, for drug administration in hospitals, and for
tracking and monitoring patients and doctors inside a hospital (Kibria et al. 2018).
Figure 5.1 shows the transmission procedure of data in the form of packets in the
wireless mobile network.
Wireless mobile systems are susceptible to safety outbreaks due to the broad-
cast behavior of the communication medium and the sensitive nature of collected
information. Security effects by some parameters of wireless mobile networks that
must be addressed are resource limitations, processing limitation, limited memory
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 83

Fig. 5.1 Transmission procedure

and storage space, power limitation, etc. (Bhushan and Sahoo 2019a). Microcon-
trollers in nodes of wireless mobile networks range between 4 and 400 machine
instructions per second which implement communication functions but are not
sufficient to support security mechanisms. A small memory of nodes necessitates
limiting the code size of security algorithms named encryption, decryption, verifi-
cation, etc., (Bhushan and Sahoo 2019b) that employed in security algorithms need
more processing, i.e., power consumption. And also more energy is mandatory to
convey the safety-related data or overhead. Connectionless routing implies unreli-
able exchanges. Due to channel errors and congestion, packets may get damaged,
resulting in lost or missing packets. Packets broadcasted on radio links may collide
causing loss of information (Moin et al. 2021). The multi-hop routing in wireless
mobile network nodes can lead to greater latency and makes it difficult to achieve
synchronization. So, this causes a problem in detecting and reporting the events on
time (Liu et al. 2020). Remote management makes it difficult to notice the physical
interfering and physical caring concerns. Maybe a disseminated system without the
management of central point makes network organization difficult (Zhao et al. 2019).
Furthermore, the key inspiration of this study is as follows.
. The work discusses the background as well as different types of wireless
mobile networks and the need of securing data in the network through wireless
transmission/connections.
. The work deliberates the various challenges and issues of wireless mobile network,
which comes during the transference of data.
. The work highlights the security goals and categories of attacks, and also discussed
how to protect the wireless mobile network from attacks in detail.
84 A. Malik et al.

. The work explores some recently proposed data related to networking and
elaborates some methods to prevent an attack on a system.
. The work redefines the inspiration for protecting data with Java to form a securing
application for Securing data.
The remainder of the paper is planned as follows, section “Types of Wire-
less Mobile Network” defines the different types of wireless mobile networks that
construct on the basis of wireless connection. Furthermore, section “Challenges and
Issues in Wireless Mobile Network” discusses the various challenges and issues that
occur during the formation of wireless mobile networks or when the transmission
takes place. Additionally, section “Security Goals of Wireless Mobile Network”
elaborates the goals of security, where confidentiality, availability, authentication,
integrity, and non-repudiation have been discussed. Moreover, section “Classifica-
tion of Security Attacks” describes the classification of security attacks for securing
applications or systems or wireless mobile networks. This section also defines some
kinds of attacks and illustrates how the attacks can affect the wireless mobile network.
Furthermore, this section also deliberates the information about various detection and
prevention mechanisms for protecting the network from different kinds of attacks.
Lastly, section “Conclusion” brings the paper to a conclusion.

Types of Wireless Mobile Network

The wireless links are used for connection between the devices by using the medium
such as microwaves, communiqué satellites, radio waves, spread spectrum technolo-
gies, free-space optical transmissions, or numerous technologies that are used in
mobile networks (Lyu et al. 2019). The different types of wireless mobile networks
are shown in Fig. 5.2.

Wireless PAN

The wireless Personal Area Networks (PAN) connect all the network and end-to-end
devices to a fairly small region, usually accessible to a person. For illustration, Blue-
tooth radio, as well as undistinguishable infrared rays, delivers wireless PAN headset
connected to a portable computer. ZigBee also supports Wireless PAN applications
that include sensors and many related devices (Awais et al. 2020).
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 85

Fig. 5.2 Types of wireless

network

Wireless LAN

The wireless Local Area Network (LAN) joins two or more nodes as well as network
devices to a short distance using a wireless dissemination technique, typically giving
network access points over the internet. The use of spread-spectrum or wireless
transmission technology could permit the users to navigate within the limited area and
stay associated with the system. Immovable wireless technology uses point-to-point
associations among computers or networks in two remote positions, usually using a
devoted microwave or another ray converted into a line of sight. It is frequently used
in capitals to attach the systems in two or more buildings without fixing a wireless
connection (Fortino et al. 2018).

Manet

The wireless Mobile Ad hoc Network (MANET) is a wireless system that connects
the node or devices based on the structure of mesh topology. Every device transmits
the data packets instead of knowing other devices/nodes and every node forms a route.
86 A. Malik et al.

Ad hoc networks can “support themselves”, and automatically relocate to a depleted

environment. Numerous network layer protocols are required to employ MANET,
such as vector sequence tracking, associativity-based route, dynamic source route,
and much more (Ayele et al. 2018).

Wireless MAN

The wireless Metropolitan Area Network (MAN) is a type of wireless network

that links numerous wireless LANs to provide connectivity between two types of
networks and covers the networks in a range of around thousands of kilometers or
area covering two cities, for example, WiMAX (Malik et al. 2020).

Wireless WAN

The wireless Wide Area Network (WAN) often covers huge regions, like nearby
villages and cities, or cities and suburbs. These systems can be used to join branch
offices to companies or work as a public internet access system, for example, internet
is a type of WAN, which connects people all over the world that says WAN is able
to create or maintain the world largest networks easily. Wireless links among access
points are generally point-to-point microwaves using parabolic vessels at 2.4 GHz
band, at the place of omnidirectional horns, which are used with minor networks.
The standard system consists of basic hubs, routers, gateways, access points, and
relay wireless bridges. Another configuration system has spaces where each access
point acts as a relay as well (Elhattab et al. 2017).

Mobile Network

It is a radio system dispersed over the world called cells, each of which is supplied
with at least one immovable transceiver, known as a mobile site or base station. In
this type of network, all cell uses a diverse group of radio frequencies across adjacent
cells to escape any disruption. When they are put together, these cells provide radio
broadcasts throughout the country (Tzanakaki and Anastasopoulos 2019).

Gan

The Global Area Network (GAN) is a network that is used to support mobile phones
with a certain number of wireless LANs, satellite-covered environments, etc. It is
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 87

a kind of network in which different networks or devices or transmission mediums

are interconnected to cover an unlimited geographical space. The biggest challenge
in mobile communication is the transfer of user communications from one location
to another. The IEEE 802 project involves a series of groundless LANs (Zhao et al.
2021).

Aerospace Network

Aerospace networks are the networks that are used to communicate between space-
craft, usually in areas close to the universe. It has been giving instructional sustenance,
software resolutions, and media invention facilities to both scholastic and commer-
cial clients. An example of this type of network is the NASA space network (Liang
et al. 2019).

Challenges and Issues in Wireless Mobile Network

In today’s world, providing a reliable and dependable mode of communication, espe-

cially in emergencies or applications is one of the important research concerns and
challenges. Some important issues and challenges in a wireless mobile network are
buffer management, node discovery, forwarding of the message, security of network
and data, etc., which are briefly explained below and also mentioned in Fig. 5.3.

Information Management

Wireless mobile network is a type of environment where most of the focus is on the
delivery of information to achieve this most of the routing protocols use flooding-
based mechanisms. This type of protocol has a habit to load the network by trans-
ferring a huge amount of information into the network. So, to handle these issues,
the authors provide various other types of protocols that are based on the forwarding
approach rather than the flooding approach (Ding et al. 2018).

Endless Communication

In wireless mobile networks, communication between two devices provides the basis
for interaction. The communication issue is exacerbated by an absence of prior
information about the position, time, and required bandwidth. Route agreements that
use the context, profile, or history of mobile users as well as all connected devices
88 A. Malik et al.

Fig. 5.3 Challenges in wireless mobile network

should be examined for use on mobile networks. It will be necessary to upgrade

the middleware methods so that the mask can be delayed and hide the complexity
of the flexible methods in the operating systems. The information obtained must be
analyzed to archive, refine, and disseminate as the storage capacity and bandwidth
are restricted (Shnaiwer et al. 2019).

Delay Tolerance

Effective use of Delay Tolerant Network (DTN)’s applications has been proven very
useful for wireless mobile networks. Tolerance delay plays a significant role in the
mobile computer as all individuals did not want to wait or waste their single minute
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 89

hence, it is very essential to provide smooth communication without any kind of

delay (Liu 2021).

Heterogeneity

Possibly, many types of nodes may come together automatically such as cell phones,
PDs, laptops digital notebooks, sensors, cameras, and RFID devices. These devices
can be maintained by a variety of communication abilities and radio signals. The
interplay of communication between these pairs of different devices is the main
experiment (Samanta and Misra 2018).

Contextual Awareness

It is an important key to searching/finding a secure system. Most of the content

is important for people who are directly close to the source, creating a temporary,
local public where they want to share. This needs of inaugurating strong and reli-
able momentary relationships among people and equipment. Content knowledge and
profiles of devices, individuals, and applications as well as repository development
strategies are needed to effectively manage the content repository. In order to share
the information on social media, researchers have projected social networking sites.
A social archive is a logical compilation of a device for each device that stores infor-
mation that is useful to members of its social network. Given that participants are
predictable to meet regularly, and data stored in a public repository can be used effec-
tively by more members, temporary community retention can meaningfully improve
system performance (Lee and Ke 2018; Kibria et al. 2018).

Buffer Management

The most important part of devices is storage or buffer space in themselves. On

a mobile or movable computer, devices store the information of another person in
their repository that should be carefully monitored by removing undesirable data and
protecting the data usable from applications on the network’s device, such as those
devices by which our peers are expected to encounter next. The content repository
can be processed through other applications, content, or other methods (Wadii et al.
2019).
90 A. Malik et al.

Power Features

Power is another important feature of the portable device, where most devices are
powered by a battery. Power management is a separate issue in terms of stowage
and bandwidth management. Improved data transfer on a wireless optical connector
causes more power, while local data storage may incur significant energy costs in
memory control (Sinha et al. 2017).

Privacy and Security

Finding security and trust between anonymous nodes in this type of network is a
challenge. However, social networking structures provide the basis for improving
trust and providing protection through the use of “communities” of similar devices
within themselves, physically or mentally. The idea of using social networking infras-
tructures to improve network security is not a novelty. Actually, the works cover a
few suggestions based on the use of social networks to combat email spam and to
protect the networks against various kind of attacks. Conversely, the use of social
networking is the complete separation of networks is a new and challenging task
as, in these surroundings, security resolutions based on a central server or trusted
online specialists cannot be achieved. In this case, the natural direction of the pursuit
of exploitation of electronic social networks and the relationship between trust and
safety is deeply ingrained in human relationships (Petrov et al. 2018).

Security Goals of Wireless Mobile Network

Wireless network is more feasible as compared to a wired one but it is very essential to
offer safe and secure communication or connection between the users. There are five
security goals that needed to be accomplished to conserve smooth communication
in a wireless mobile network as shown in Fig. 5.4.

Fig. 5.4 Goals of security

5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 91

. Confidentiality—It refers to the protection of data sent by a device so that it

becomes unreadable by an unsanctioned person or access point. Because wireless
networks are open, all networks are within direct transmission range, making
data retrieval simple so it is very important to keep the data confidential from the
unauthorized user or device.
. Availability—The “activeness of communication” is most essential in the network,
network services should always be available when they are needed. The avail-
ability means the data, the transmission medium, as well as the node, will be
available or reachable in the network for communication or connection if they are
not busy in another network (Edirisinghe et al. 2021).
. Authentication—It identifies that a network or a client is genuine or not fake
and prevents parody so that any devices carrying the virus with it cannot easily
connect with the genuine network or perform illegal actions. As the fake node has
the identity of a genuine network or device to access the sensitive information
easily, it is much needed to authenticate the user.
. Integrity—Integrity relates to the fact that the sender’s message should reach the
receiver intact, with no changes or deletions. That means when the data packet
which is sent by the sender to the receiver through an insecure and open trans-
mission medium should be same without any alteration or change of a single bit
in the data packet.
. Non-repudiation—It gave the assurances to the sender that a sent message cannot
deny after sending that the message or that the message was received by the
intended recipient will not be deniable. It’s used to separate and identify infected
access points. If Network X receives an infected message from Network Y,
Network X should be able to blame Network Y and inform the other networks
about it using non-repudiation (Fernando et al. 2019).

Classification of Security Attacks

The transmission medium of a wireless network is broadcast in nature. Due to this,

wireless network is very sensitive to different kinds of security threats. In a wireless
network, security attacks can be categorized as follows.
. Passive versus active attacks—In passive attacks, the data travels over the network
without any disrupting operations applied to transmission. Although in an active
attack, information disruption, alteration, deletion, construction, etc., can disturb
the normal functioning of the wireless network (Lin et al. 2020).
. Internal versus external attacks—Internal attacks are performed inside the
network by compromised networks that lie inside the network, while external
attacks are performed by those networks that do not lie inside the network.
. Stealthy versus non-stealthy attacks—The attacker tries to hide his/her identity/
actions and operate quietly to disturb the network. In non-stealthy attacks, the
attacker doesn’t hide his/her action/identity (Guan and Ge 2018).
92 A. Malik et al.

Table 5.1 Security attacks on different layers

Layers (Loreti and Bracciale Attacks (Loreti and Bracciale 2020)
2020)
Physical layer Eavesdropping, Interference
Datalink layer Disturbing frames of MAC, Monitoring
Network layer Location revelation, Rushing attack, Blackhole attack,
Wormhole attack, Byzantine
Transport layer Flooding, Hijacking
Application layer Data exploitation, Cross-site scripting, Denial

. Cryptographic versus non-cryptographic attacks—Digital signature attacks,

hash collision attacks, and many more are kinds of attacks that lie under the
category of cryptographic attacks. Flooding attacks, blackhole attack, etc., are
those attack that lies under the category of non-cryptographic attacks (Yang and
Wen 2021).
. Attacks on different layers—Table 5.1 shows attacks, which are classified based
on networking layers of the internet model. There are some attacks, which come
under various layers like impersonation, replay, man-in-the-middle, etc., as shown
below.

Most Prevalent Attacks in Wireless Systems

There is various kind of security attacks, which are performed by the attacker to gain
access and admittance in the network and harm the network as well as data. In this
subsection, various security attacks are classified or stated that how they perform
malicious behavior.

Denial of Service (DoS) Attack

In this attack, the attacker familiarizes himself/herself with many fake or bogus data
packets in the system to affect the system conflict in the wireless server. Sometimes,
the infected system may pretend as a busy network and deny communicating with
others (Ashfaq et al. 2019). In this attack, many bogus requests or other kind of
requests floods over the system or server to keep the network busy and to make
them not able to perform any genuine task. These impact network accessibility,
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 93

furthermore, the detection, as well as prevention techniques of this attack are as

follows.
. Strengthening their security status: This includes strengthening all internet-based
resources to prevent compromising, installing and maintaining anti-virus soft-
ware, setting up security walls designed to guard the network against DoS attacks,
and following strict safety procedures to observe and control undesirable traffic
(Okamura et al. 2019).

Flooding Attack

The purpose of an infected network is to deplete the resources in the network like
consuming the power of the battery of the networks by flooding unnecessary requests.
It is also termed a resource consumption attack or bogus information attack (Nundloll
et al. 2020). Moreover, the prevention technique for flooding attacks is stated.
. Blacklist the infected network—Every network has a threshold value in a network
that is priory defined. If the network sends the RREQ request more than its
threshold value, then that network gets blacklisted from the network and any
request that comes from the blacklist network is simply dropped by another
network (Bhushan and Sahoo 2020b).

Jamming

The main purpose of this attack is to prevent sending and reception of legitimate
packets from source to destination. Sometimes, it can be performed to capture the
way and gain access. In this attack, unnecessary request and response messages
are flooded to jam the routes so that the functionality of the network decreases.
At last, all the possible routes between networks in the network get destroyed and
no communication is done, it is also called an SYN flood attack (Liu and Labeau
2021). Moreover, some of the detection and prevention techniques of this attack are
as follows.
. Anti-Jamming reinforcement system—It is used to see if there’s any jamming
going on. To lessen the jamming effects, it provides rate adaptation and power
control measures such as ARES (A software package that allows the file to be
immediately downloaded into the system) (Tsiota et al. 2019).
. Uncoordinated Direct Sequence Spread Spectrum (UDSSS)—The receiver has
a certificate of the sender’s public key in this broadcast situation, but they don’t
exchange the secret key. As a result, the receiver will be able to verify the request
(Zhang et al. 2020).
94 A. Malik et al.

. Steiner Triple System and the Traversal Design (STS &TD)—These two
approaches, STS and TD, are proposed to provide jamming prevention (Gautam
et al. 2019).

Intervention

Radio communication can be obstructed by the invader to harm or injure the data so
that it cannot reach the receiver. It happens when the user is able to access a little
solid information about the network without direct access to it. The purpose of this
unintentional attack is to combine the information on a single level of security in
order to determine the truth that should be protected at the highest level of security
(Malik and Gupta 2019).

Sleep Deprivation Attack

For the extra consumption of battery of networks, the sleep deprivation attack is
done. In this, networks are enforced to continue wakeful by the invader to reduce the
battery life and to shut down the networks. This attack is the most hazardous type
of attack at this stage, as the malicious node makes requests to the nodes only to
keep the victims awake. The victim’s nodes are therefore reserved for the network
wakeful and not able to complete energy-based tasks (Bhushan et al. 2017).

Blackhole Attack

In this attack, an illusion is created by the infected network that it has the shortest
way from sender to receiver. Once it is done, then all the packets coming to the
infected network get to fall. If more than one infected network work in combination
and try to suffer the whole network, then it is called a collaborative blackhole attack
or packet dropping attack (Malik and Gautam 2019). Moreover, the detection and
prevention techniques that are used to protect the network from blackhole attack and
cooperative blackhole attack are as follows.
. Prevention of a Cooperative Blackhole Attack (PCBHA)—The concept of
PCBHA is to use fidelity level in networks. Initially, each network has a defaulting
fidelity level, and after distribution of an RREQ, a source network waits to
receive return RREPs after the neighbor networks, only that neighbor network
gets selected which has an advanced reliability level and surpasses the threshold
value, for passing the data packets. It is mandatory for the destination network to
return an ACK message after receiving data packets. When the source network
receives an ACK message from the destination network, it adds 1 to the fidelity
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 95

level of the neighboring network. If no ACK response is received by the source

network from the destination network, then 1 is subtracted from the fidelity level
of the neighbor network which shows a possibility of a blackhole node in the
network on the followed route (Heo et al. 2018).
. Protection based on cryptography—To protect blackhole and collaborative black-
hole attacks, some cryptographic techniques are also used like fingerprint (hash
or hash MAC), digital signature, and other protections (Zhou et al. 2020).
. Protocol modification—To protect blackhole and collaborative blackhole attacks,
some modifications to the protocol can also be done like cross-check, degree of
trust, and data routing information table (Nishanth and Mujeeb 2021).
. Redundant route method—To prevent blackhole attack, not only one but also
many routes are found from sender to receiver. The minimum number of valid
routes from sender to receiver, in any case, is three as per (Kafaie et al. 2018).

Rushing Attack

In this attack, the source network sends RREQ to the destination network via some
networks in between. Concurrently, another RREQ is sent to the same destination
network by the attacker’s network. If the neighbor network of that destination network
gets the attacker network’s request first, then that infected route is selected (route
having an infected network). After that, the original request which is conducted from
the source network is received by the neighbor network and will be discarded. As
a result, the communication between the source network and destination network
is only done via the infected network or attacker network (Sivanesh et al. 2019).
Moreover, the detection technique which is used to detect the rushing attack in the
network is stated as follows.
. Secure neighbor detection—It confirms that a neighboring network falls in a
maximum communication scale by introducing a delegation message which is
based on the sign based on some routing table’s entries (Zhou et al. 2020).

Sybil Attack

In this attack, a network is taking over the whole network and then claims numerous
individualities. Generally, it disturbs the accessibility but, on the second hand, it also
impacts the rest of the goals of security. Sybil attack is a type of computer network
attack where an attacker overrides the reputation of the system by creating a large
number of fake identities and using them to gain unparalleled influence (Baza et al.
2022). The detection and prevention techniques for this attack are stated below.
. Trusted certification—Every system in the network has a single identity certi-
fication which is given by the centralized authority that cannot be alterable or
96 A. Malik et al.

multiple. It is a kind of certification which is a legal document given by central-

ized authority to certify the individuality of both the trust and action of the trustee
(Avoussoukpo et al. 2021).
. Trusted devices—In this approach, a network card is used to provide authenticity
of entities and it is mandatory for all of the entities to have a card in a network, it
is a kind of certification which is a legal document given by centralized authority
(Yao et al. 2019).

Sinkhole Attack

This attack is done inside a system. An invader accommodates a network inside the
whole network and inaugurates an attack like packet drop, fake routing update, and
modification. To detect and prevent sinkhole attacks, a mechanism is developed that
considers the operation of the AODV protocol as well as the behavior of sinkhole
attacks. The mechanism is divided into four phases—the initialization phase, storage
phase, Iivestigation, and resumption phase (Malik et al. 2022).

Gray Hole Attack

It is a superior case of the blackhole attack. It is very similar to the blackhole attack.
The only variance is blackhole attack drops all the data packets while the gray hole
attack can or cannot drop the data packets. It does not have a fixed behavior. It
often switches its state from infected to normal networks and vice versa (Khan et al.
2021). To protect system from this attack, we can use all the discussed detection
and prevention techniques of blackhole attacks as working of both attacks are very
similar.

Byzantine Attack

In this attack creating routing loops, forwarding packets through non-existing paths,
or dropping attacks are performed in a byzantine attack by an infected network or
group of infected networks that work in collusion (Taggu and Marchang 2019).

Jellyfish Attack

The motive of this attack is to make unwanted delays while data packets are being
sent. It introduces pauses in forwarding the data packets producing high end-to-end
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 97

delays. It is known as a Jellyfish attack or GTS attack and Timing attack (Deepika and
Saxena 2018). The various detection and prevention techniques for jellyfish attack
are as follows.
. 2ACK—The 2ACK basic technique is based on the idea that a specific two-hop
acknowledgment called 2ACK to send by the destination network to the source
network just to point out that the data packet was received successfully by the
destination network.
. Credit-based systems—In that approach token or credit is used by the network, the
moment it begins to send its packet in order to encourage successful transmissions
(Thapar and Sharma 2020).
. Reputation-based scheme—In this system, single networks are capable to detect
misbehaving networks (such as CONFIDANT) (Yang et al. 2021).

Wormhole Attack

The data packets are caught by the invader from one place and are tunneled to another
place to disorder the routing. Sometimes this attack may also affect the accessibility
of the network. If the tunneling mechanism is not applied properly, all the packets may
be dropped by the attacker (Prasse and Mieghem 2020). Furthermore, the detection
and prevention techniques for wormhole attack are stated.
. Clustering—The whole network is partitioned into small clusters (group of
networks) containing a cluster head. In a cluster, the number of members
(networks) is priory-defined. A cluster head is a leader of the cluster by has the
power to transmit the information to the entire membership. There is no commu-
nication link between members, it is only done via cluster head (Yoshino et al.
2018).
. Packet leash—Two types of leashes are used to detect and prevent wormhole
attacks. The first one is a geographical leash, and another is a temporal leash. In
a geographical leash, the network sends its location and transmission time before
sending the data packet. When the receiver receives the data packets, it calculates
the traversal time of packets just to match the information which is sent by the
sender. RTT (Round trip time) and time of flight are some methods that come
under the geographical leash to detect the attack. On another side, in temporal
leashes, the packet is sent with a sending timestamp added by the sender, and the
traveling distance of that packet is calculated by the receiver (Gul 2021)
. Other techniques—Some other techniques can also be used to prevent this attack
such as DS, Network monitoring, and GPS-based wormhole combating technique
(directional antenna) (Thanuja et al. 2018).
98 A. Malik et al.

Eavesdropping

It is the blocking and casting of an eye over data and conversations by the attacker. It
disturbs the privacy of the network. It is also termed traffic analysis or sniffing attack.
It is theft of data as it is transmitted to a network via a node, smartphone, or another
connected device. It uses the opportunity of an unsecured network connection to
access data as it is sent or received by its user. It occurs when cybercriminals steal
information sent or received by a user through an insecure network. Additionally,
by the usage of strong encryption techniques, we can able to mitigate this attack and
can protect the system/network (Li et al. 2019).

Disclosure of Information Attack

In this type of attack, the attacker reveals information related to the network
topology, confidential data, geographic position of networks, or ideal paths to actual
networks in the network. It is also known as an Information leakage attack, it occurs
when a website accidentally discloses the sensitive data to its users. Dependent on
the framework, websites may reward all kinds of data/information for a potential
invader, including data of other users, such as usernames or financial information. It
occurs when the request does not adequately protect sensitive information that may
eventually be disclosed to the parties who should not have access to it (Li et al. 2021).

Man-In-The-Middle Attack

The attacker sits between the sender and recipient and observes information, while
transmission and theft of the essential data/information under this attack. This attack
is a common name where the perpetrator puts himself in a conversation between the
user and the request listener or pretends to be one of the parties, making it seem
like they are exchanging common information. This attack also helps the vicious
attacker, without any type of participant you see until it is too late, to break into
another person’s targeted data and should not be sent at all (Khatod and Manolova
2020).

Replay Attack

It is a passive attack, in this attack, the attacker stored a message or data packet of a
network and used the stored message for further communication by controlling and
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 99

resending them later to access the network and perform impersonate actions (Malik
2019).

ACK Attack

Under this attack, a fake acknowledgment is sent by the hacker to the receiver, inter-
mediate node as well sender to eavesdrop on the network. When a request is sent by
the sender then an attacker takes advantage of this to send a fake acknowledgment
as a response at the place of receiver or intermediate nodes. Once the fake acknowl-
edgment is received by the sender which is exactly the same as the acknowledgment
of the actual receiver hence the sender gets trapped and sends the original data as a
response to the fake received acknowledgment (Boche et al. 2021).

Spoofing Attack

When an infected network/node misprints its identification to a genuine node as

multiple nodes, resulting in topology changes, delays, change in data, illegal actions,
and data losses. An infected system may pretend to be a valid network member after
retaining the same IP address of a network member. These are known as spoofing
attacks, IP spoofing attacks, and session hijacking attacks (Wu et al. 2020).

Link Spoofing Attack

In This Attack- when a fake path to non-existent network/s is built or a fake updating
in the routing table is performed, then routing protocol is directly affected. It’s also
called a Link spoofing attack, a fabrication attack, or a Global Positioning System
(GPS) attack (Huan and Kim 2021). Hence, the detection technique that is used to
protect the network from a link spoofing attack is stated.
. Location information-based detection—Each network has GPS and a timestamp
attached with it by this technique. GPS works with cryptographic methods. In the
network, each network must announce its current and actual location information
with the help of GPS to other networks so that every network that is present in the
network becomes to know the location details of other networks in the network.
The distance between two networks that pretended to be neighbors can be verified
and false links may be turned down (Wang et al. 2019).
100 A. Malik et al.

Spear-Phishing Attack

Spear-phishing is also known as email spoofing, in this, the attacker forces the victim
to open his or her email to acquire access and retrieve important information. It is a
malicious email attack directed at an organization or individual, seeking unauthorized
access to sensitive information. It is a direct attempt to steal sensitive information
such as account information or financial information from the victim, usually for
malicious reasons. This is achieved by obtaining personal information from the victim
such as friends, hometown, places they frequently visit, and what they have recently
purchased online (Swarnalatha et al. 2021).

Repudiation Attack

Repudiation assault refers to the denial in taking part in communication activity that is
the node affected by repudiation attack will continuously deny to make a connection
or take part in sharing the data packets by showing them busy (Zhang et al. 2021).
The two techniques proposed in the literature for protecting the network against
repudiation attacks are—Create Secure Audit Trails (CSAT) and Digital Signatures
(DS) (Luo et al. 2018).

Infected Code Injection

Viruses, worms, logic bombs, spyware, adware, and Trojan horses are examples of
harmful programming that can target both the operating system and user application
as well as the network. It’s also known as a malware attack (Bhardwaj et al. 2021).
The detection or prevention technique for this attack is as follows.
. Static code analysis—It is the most effective method of preventing harmful
malware from infecting business systems. Nowadays, leading scanners can rapidly
expose infected code such as anti-debugging techniques, steady information, data
leakage, time bombs, rootkits, etc. (Liu et al. 2019).

Colluding Mis-Relay Attack

In a colluding mis-relay attack, instead of a single attacker, a group of attackers

works together quietly to modify data packets or drop-sending packages to disrupt
the network’s normal operation. When the attackers drop packets, it has an impact on
the network’s availability. To detect this attack, an acknowledgment-based approach
is used at the receiver’s end as well as the sender’s end (Abdalzaher et al. 2019).
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 101

Selective-Forwarding Attack

The attacker gets all data packets originating from the source and then forwards some
of the data packets to the destination node that select randomly, while the remaining
data packets are stolen by the attacker so that a malicious action can be performed
(Gonzalez and Jung 2019).

Database Hack Attack

In a network, all the activities or data stored in the database must be properly config-
ured but when it is configured appropriately. It can, however, be hacked if it is
configured incorrectly (Gonzalez and Jung 2019). Moreover, table 5.2 presents a
tabular summary of the whole paper.

Conclusion

The paper discussed and presented the various security issues present in mobile or
wireless networks which disturb the normal functions of the network. The mobile
nature of the networks makes them even more vulnerable to security attacks like DoS
attack, Blackhole attack, Jamming, Flooding attack Sybil attack, Gray hole attack,
IP spoofing attack, Rushing attack, Sleep deprivation attack, Wormhole attacks, etc.
The paper discussed the various categories of wireless mobile networks, different
challenges of wireless mobile networks, security goals, and classification of attacks
into different categories on various measures. At last, the paper presented some
detection and prevention of attacks as proposed by different researchers. The paper
presents a comprehensive survey of the attacks on wireless mobile networks and
purposed solutions. In future work, we will design a technique that can able to
secure the network from different kinds of attacks.
Table 5.2 Security attacks in wireless mobile network
102

Attack (Loreti and Bracciale Type (Loreti and Bracciale 2020; Action (Loreti and Bracciale Detection and prevention Affects (Loreti and Bracciale
2020; Ashfaq et al. 2019; Ashfaq et al. 2019; Okamura 2020; Ashfaq et al. 2019; mechanisms (Loreti and 2020; Ashfaq et al. 2019;
Okamura et al. 2019; Nundloll et al. 2019; Nundloll et al. 2020; Okamura et al. 2019; Nundloll Bracciale 2020; Ashfaq et al. Okamura et al. 2019; Nundloll
et al. 2020; Bhushan and Sahoo Bhushan and Sahoo 2020b; Liu et al. 2020; Bhushan and Sahoo 2019; Okamura et al. 2019; et al. 2020; Bhushan and Sahoo
2020b; Liu and Labeau 2021; and Labeau 2021; Tsiota et al. 2020b; Liu and Labeau 2021; Nundloll et al. 2020; Bhushan 2020b; Liu and Labeau 2021;
Tsiota et al. 2019; Zhang et al. 2019; Zhang et al. 2020, 2021; Tsiota et al. 2019; Zhang et al. and Sahoo 2020b; Liu and Tsiota et al. 2019; Zhang et al.
2020, 2021; Gautam et al. 2019; Gautam et al. 2019; Malik and 2020, 2021; Gautam et al. 2019; Labeau 2021; Tsiota et al. 2019; 2020, 2021; Gautam et al. 2019;
Malik and Gupta 2019; Bhushan Gupta 2019; Bhushan et al. 2017; Malik and Gupta 2019; Bhushan Zhang et al. 2020, 2021; Gautam Malik and Gupta 2019; Bhushan
et al. 2017; Malik and Gautam Malik and Gautam 2019; Heo et al. 2017; Malik and Gautam et al. 2019; Malik and Gupta et al. 2017; Malik and Gautam
2019; Heo et al. 2018; Zhou et al. et al. 2018; Zhou et al. 2020; 2019; Heo et al. 2018; Zhou et al. 2019; Bhushan et al. 2017; Malik 2019; Heo et al. 2018; Zhou et al.
2020; Nishanth and Mujeeb Nishanth and Mujeeb 2021; 2020; Nishanth and Mujeeb and Gautam 2019; Heo et al. 2020; Nishanth and Mujeeb
2021; Kafaie et al. 2018; Kafaie et al. 2018; Sivanesh et al. 2021; Kafaie et al. 2018; 2018; Zhou et al. 2020; Nishanth 2021; Kafaie et al. 2018;
Sivanesh et al. 2019; Zhou et al. 2019; Zhou et al. 2020; Baza, Sivanesh et al. 2019; Zhou et al. and Mujeeb 2021; Kafaie et al. Sivanesh et al. 2019; Zhou et al.
2020; Baza, et al. 2022; et al. 2022; Avoussoukpo et al. 2020; Baza, et al. 2022; 2018; Sivanesh et al. 2019; Zhou 2020; Baza, et al. 2022;
Avoussoukpo et al. 2021; Yao, 2021; Yao, et al. 2019; Malik Avoussoukpo et al. 2021; Yao, et al. 2020; Baza, et al. 2022; Avoussoukpo et al. 2021; Yao,
et al. 2019; Malik et al. 2022; et al. 2022; Khan et al. 2021; et al. 2019; Malik et al. 2022; Avoussoukpo et al. 2021; Yao, et al. 2019; Malik et al. 2022;
Khan et al. 2021; Taggu and Taggu and Marchang 2019; Khan et al. 2021; Taggu and et al. 2019; Malik et al. 2022; Khan et al. 2021; Taggu and
Marchang 2019; Deepika and Deepika and Saxena 2018; Marchang 2019; Deepika and Khan et al. 2021; Taggu and Marchang 2019; Deepika and
Saxena 2018; Thapar and Sharma Thapar and Sharma 2020; Yang Saxena 2018; Thapar and Sharma Marchang 2019; Deepika and Saxena 2018; Thapar and Sharma
2020; Yang et al. 2021; Prasse et al. 2021; Prasse and Mieghem 2020; Yang et al. 2021; Prasse Saxena 2018; Thapar and Sharma 2020; Yang et al. 2021; Prasse
and Mieghem 2020; Yoshino 2020; Yoshino et al. 2018; Gul and Mieghem 2020; Yoshino 2020; Yang et al. 2021; Prasse and Mieghem 2020; Yoshino
et al. 2018; Gul 2021; Thanuja 2021; Thanuja et al. 2018; Li et al. 2018; Gul 2021; Thanuja and Mieghem 2020; Yoshino et al. 2018; Gul 2021; Thanuja
et al. 2018; Li et al. 2019, 2021; et al. 2019, 2021; Khatod and et al. 2018; Li et al. 2019, 2021; et al. 2018; Gul 2021; Thanuja et al. 2018; Li et al. 2019, 2021;
Khatod and Manolova 2020; Manolova 2020; Malik 2019; Khatod and Manolova 2020; et al. 2018; Li et al. 2019, 2021; Khatod and Manolova 2020;
Malik 2019; Boche et al. 2021; Boche et al. 2021; Wu et al. 2020; Malik 2019; Boche et al. 2021; Khatod and Manolova 2020; Malik 2019; Boche et al. 2021;
Wu et al. 2020; Huan and Kim Huan and Kim 2021; Wang et al. Wu et al. 2020; Huan and Kim Malik 2019; Boche et al. 2021; Wu et al. 2020; Huan and Kim
2021; Wang et al. 2019; 2019; Swarnalatha et al. 2021; 2021; Wang et al. 2019; Wu et al. 2020; Huan and Kim 2021; Wang et al. 2019;
Swarnalatha et al. 2021; Luo Luo et al. 2018; Bhardwaj et al. Swarnalatha et al. 2021; Luo 2021; Wang et al. 2019; Swarnalatha et al. 2021; Luo
et al. 2018; Bhardwaj et al. 2021; 2021; Liu et al. 2019; Abdalzaher et al. 2018; Bhardwaj et al. 2021; Swarnalatha et al. 2021; Luo et al. 2018; Bhardwaj et al. 2021;
Liu et al. 2019; Abdalzaher et al. et al. 2019; Gonzalez and Jung Liu et al. 2019; Abdalzaher et al. et al. 2018; Bhardwaj et al. 2021; Liu et al. 2019; Abdalzaher et al.
2019; Gonzalez and Jung 2019) 2019) 2019; Gonzalez and Jung 2019) Liu et al. 2019; Abdalzaher et al. 2019; Gonzalez and Jung 2019)
2019; Gonzalez and Jung 2019)
(continued)
A. Malik et al.
Table 5.2 (continued)
Sinkhole attack Active attack The attacker sends a fake routing SAR All security goals
detail to collect all network
traffic. The data packages may
also be modified by the attacker
Gray hole attack Active attack Providing a sham request and Ignore the infected network Availability
then dropping (may or may not)
the packets
Wormhole Attack Passive attack/Active attack Infected networks use a Clustering, Packet least, Digital Confidentiality
high-speed link to connect to the signature (Round trip time),
source and act as the genuine Directional antenna
neighbors
Jamming Active attack Block genuine packets from ARES, UDSSS, UFH Availability
being sent or received
Blackhole attack and Active Attack The attacker creates routes upon PCBHA, Cryptography-based Availability
collaborative blackholes attack receipt of the route request and protection, Protocol modification,
the data packet received are not and Redundant route method
sent to the network
Traffic analysis Passive attack The attacker closely monitors the Strong encoding methods Confidentiality
route and transmission of data
packets. To capture important
personal information
5 Security Attacks and Vulnerability Analysis in Mobile Wireless …

Flooding attack Active attack Flood the network with needless Blacklist the infected network Availability
requests and comeback messages,
consuming network resources
(continued)
103
Table 5.2 (continued)
104

Rushing attack Active attack When an invader (attacker) Secure neighbor detection Availability
receives a route request data
package, it delivers it out to the
entire network until genuine
networks
Link spoofing attack Active attack To modify the routing, placed Location information-based Authenticity
route to non-existent detection
Colluding mis-relay attack Active attack Some modifications were applied Acknowledgment based approach Confidentiality and availability
on packets and dropped the
packets to destroy the normal
function of the network
Eavesdropping Passive attack Find the confidential information Strong encryption mechanisms Confidentiality
by sniffing the data packets
Jellyfish attack Active attack Introduces unnecessary delay Credit-based systems, 2ACK, Availability
during delivery of packets Reputation-based scheme
Denial of service Active attack The attacker acts as a busy Enticement means based on Availability
network, denying or dropping repudiation
packet forwarding
Infected code Attacks Active attack Infected code is inserted into data SCA Integrity
or over a network
Sybil attack Active attack More than one individuality is Trusted certification, trusted Availability
generated by the attacker for a devices, received signal strength
single network
Repudiation Attack Passive attack Refusal of sharing DS and CSAT Non-repudiation
A. Malik et al.
5 Security Attacks and Vulnerability Analysis in Mobile Wireless … 105

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Chapter 6
Utilities of 5G Communication
Technologies for Promoting
Advancement in Agriculture 4.0: Recent
Trends, Research Issues and Review
of Literature

Parijata Majumdar, Diptendu Bhattacharya, and Sanjoy Mitra

Abstract The ultrafast 5G network will play a significant role in the farming industry
over the upcoming couple of years, serving to boost crop yield and quality while
requiring minimal labour. Farmers will be more informed to make smart decisions
regarding irrigation by using smart and precision farming. The introduction of 5G
will significantly alter the farming characteristics and agriculture practices in this
era of Agriculture 4.0. 5G network’s IoT-based cloud computing service offers smart
farming solutions that are both flexible and resourceful. This will permit the seamless
operation of various unmanned agricultural devices during ploughing, sowing seed
and managing phases of crop farming, resulting in secure, dependable, environment-
friendly and energy-efficient operations, as well as the creation of unmanned farms.
This paper examines the need for and role of smart and precision farming in the
agricultural sector incorporating 5G applications in precision farming in the present
era of Agriculture 4.0, such as real-time monitoring, data analytics, cloud reposito-
ries, virtual consultation and predictive maintenance and also discusses upcoming
opportunities. 5G-based IoT solutions focusing towards Ultra-Reliable Low Latency
Communication (URLLC) like automated control and self-driven vehicles to support
rapid response times and higher dependability will diminish communication delays
in time-sensitive agriculture applications and non-public networks to allocate part of
frequency spectrum on demand, network slicing alternatives are also discussed here.

Keywords Smart agriculture · 5G · Precision farming · IoT · URLLC ·

Non-public networks · Agriculture 4.0

P. Majumdar · D. Bhattacharya
Department of Computer Science and Engineering, National Institute of Technology, Agartala,
Jirania, Agartala, Tripura 799046, India
S. Mitra (B)
Department of Computer Science and Engineering, Tripura Institute of Technology, Agartala,
Narsingarh, Agartala, Tripura 799009, India
e-mail: mail.smitra@gmail.com

© The Author(s) 2023 111

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_6
112 P. Majumdar et al.

Introduction

Agriculture is most countries’ principal factor of income and plays a critical role
in their economic development. Different styles of agriculture are performed all
over the world, with the goal of healthy food production to feed the world’s popu-
lation. Agriculture is a developing country’s primary source of revenue. Modern
farming began around the eighteenth century, during the British Agricultural Revo-
lution, when numerous improvements to farming were accomplished in a little time,
resulting in a productive increase in yield and more efficient ways. Food production
must be raised swiftly to keep up with the rapid growth of the global population.
Traditional farming practices result in irregular output, resource overuse and trash
creation that is unchecked. Farmers will require more advanced technology to meet
these demands, which will allow them to produce more while requiring manually
less labour. This is when automation enters the picture. In this context, the intro-
duction of 5G communications provides a potentially disruptive factor. In terms of
communication, 5G’s enhanced data rate, less end-to-end latency and wider coverage
have the ability to meet even the ever-increasing demand of IoT end users (Heidari
et al. 2021). Its ability to accommodate a massive number of devices allows for the
creation of a truly global Internet of Things. Furthermore, as it focuses on the inte-
gration of access methods, 5G could serve as a unified interconnection framework,
allowing “things” to connect to the Internet seamlessly. The purpose of this study is
to examine in depth the potential of 5G for the Internet of Things for reaping full
benefits of smart farming. With machine-to-machine services, however, the adop-
tion of 5G will assist speed up the complete procedure. The real-time data transfer
capabilities of 5G can aid in the efficient operation of these technologies, allowing
for quick, reliable, data-driven and real-time decision-making. Applications of 5G
in agriculture include AI-enhanced machinery, drone sprayers, accurate harvest esti-
mation, effective irrigation and livestock tracking as well as management (Tang et al.
2021).

Challenges Faced by Existing Network Technologies

The 4G networking paradigm faces significant restrictions that may restrain the
technology from reaching its full prospective in the agriculture industry. One of the
most significant limits is the operating area. Remote locations are not covered by
existing wireless networks. Due to variable data rates, resource allotment, handoff
and channel state, issues between heterogeneous networks facilitating QoS (Tong
et al. 2019) networks poses a considerable challenge. The large number of antennae
and transmitters causes poor battery life in mobile nodes. Because many current agri-
cultural gadgets, such as drones and agribots are powered by battery, these cannot
be incorporated in remote crop fields for long periods of time. Several devices are
continuously rising in number, requiring greater intelligence, scalability, processing
6 Utilities of 5G Communication Technologies for Promoting … 113

capacity, secure communication, etc., to conduct profound computational opera-

tions. Ultralow latency along with high connection is essential for IoT devices to
deliver quick performance and low prices. Because it only permits IP-based packet
switching connectivity, the existing 4G network (LTE) cannot provide such function-
alities (Martin et al. 2011). Transitioning to next-generation 5G will address these
limitations of previous generations.

Motivation of the Work

Farmers can expect the following benefits in the near future as a result of 5G’s
accessible capabilities like faster communication where 5G will offer a data speed
of up to 10 Gbps, which is 100 times faster than its predecessor, 4G. Real-time
communication between stakeholders will be facilitated by faster speeds and much
lower latency. Machine-to-machine data transfer will facilitate direct information
transfer between 5G-enabled equipment without the need for human intervention that
can improve agricultural processes’ speed and efficiency. 5G will reduce the costs
where farm owners can significantly boost revenue by requiring less agri-inputs,
labour and other resources. It’s possible that 5G will take longer to fully expand
out and cover all distant locations. When it happens, though, this new agricultural
technology will cut labour requirements while bringing automation. The 5G network
minimizes the per unit time for data transfer, supports larger data rate, ensure secure
and dependable connections required for efficiency of time-sensitive applications
like irrigation scheduling which is dependent on real-time prediction of droughts
and floods.

Existing Problems and Contribution

As 5G coverage spreads over the world, it will provide extensive coverage for
numerous new applications. The 5G energy network uses LTE for both machines
(LTE-M) and narrowband IoT technologies (Heidari et al. 2021). To meet service
requirements, the 5G will form an extensively dense network. The high density
creates a mobility organization difficulty and causes larger energy utilization. Energy
harvesting methods help to deploy a large number of wireless sensors in both urban
and rural locations. Intelligence is also added that will result in substantial energy
savings during transmission. The ideal parameter settings to minimize energy loss
can be attained via advanced analytics of network data (Tang et al. 2021). Further-
more, rather than the traditional reactive approach to energy management, a proactive
method may be established. For a stable 5G future, we studied energy management
and harvesting solutions for IoT devices. Based on energy harvesting schemes, IoT
devices will be power conserving and management strategies at the circuit, system
and device levels that will be implemented in the near future.
114 P. Majumdar et al.

Scope of 5G-Enabled Networks in AgriIoT

5G provides a diverse set of capabilities to fulfil the needs of eMBB, mMTC and
URLLC services. The notion of “network slicing” allows for the operation of many
dedicated networks on a single platform. The flexibility of 5G specifications to dedi-
cate a dedicated slice of the network for certain application areas will also enable new
distant and mobile IoT applications, unlike earlier generations of mobile networks.
In 5G, network slicing allows for various connection segments to be used to imple-
ment one or more use cases. In 5G, network slicing allows for various connection
segments to be used to implement one or more use cases. The 5G network will be
connected by a large number of IoT nodes. This will make ultra-reliable or ultra-
low rate of time consumed for data transfer and communications possible (GSMA
2019). Edge computing and Artificial Intelligence at the edge, incorporating 5G,
will perform novel augmented reality (AR), time-critical industrial IoT applications,
virtual reality (VR), etc. The VR eye tracking interphase, that shows the user’s focal
point and supplies images of high resolution at the point of focal plane, is an appli-
cation area of VR-IoT. To save energy, reduced resolution is used elsewhere. 5G can
provide accommodation to millions of 5G devices in a square kilometres because
of its capability for “large machine type communication.” 5G technology is ideally
suited to meet the reduced timing requirement to perform data transfer and depend-
ability needs of crucial IoT equipments. The ability to deliver services for important
and dependable systems, like agriculture monitoring based on real-time weather
parameter sensing is critical to 5G with cellular networks. URLLC is a primary char-
acteristic of 5G and one of its main foundation stones. URLLC IoT is utilized to
better control traffic and prevent congestion while providing users with early warn-
ings (Foerster et al. 2020). The majority of 5G IoT devices will be power-driven
entirely by batteries. So, extending the network lifetime of IoT devices requires an
energy-conserving plan like modifying the frequency of sensing and data collection.
Low-cost ubiquitous computing is a major IoT enabler to ensure energy efficiency.
The size of unit computing has shrunk over the last five decades, and this, together
with new 5G network technologies like massive MIMO and millimetre-wave trans-
mission, can help IoT realize its full potential. The reduction in accessible energy is
the shortcoming of ubiquitous computing. Over time, the size of a single processing
unit has shrunk dramatically. In the meantime, battery and energy storage technolo-
gies are progressing very slowly (Sen et al. 2018). As a result, the IoT nodes have a
limited quantity of energy available. The sensors have a battery size which is modest.
Because battery life is typically significantly shorter than electronic lifespan, devel-
oping an energy harvesting-based system that can achieve net-zero energy for sensor
nodes will be a superior strategy.
6 Utilities of 5G Communication Technologies for Promoting … 115

Energy Consumption Management Methods in 5G Networks

Energy Harvesting technologies will be critical in extending network lifetime by

providing a controlled manner of recharging batteries. It is a potential advance-
ment which does not lessen the power requirement of devices but rather makes it
easier to convert to self-powering in the event of a power outage. The 5G energy
harvesting system can be divided into energy sources, energy conversion methods,
energy harvesting phases, harvesting models, etc. Transducers can convert energy
harvesting sources to usable electrical power that performs power conversion and
energy storage capacity, such as supercapacitors or batteries, to store converted
power. There are a variety of ambient energy sources available in the environment
(Lee et al. 2018) like Solar, electromagnetic, mechanical vibrations or kinetic energy
sources, thermal etc. are all examples of renewable energy sources. In general, elec-
tromagnetic radiation can be used to generate energy non-radiatively and radiative
methods. The non-radiative method has a higher competence but less range, but the
radiative method has an inferior efficiency but a comparatively better range. As a
result, in the 5G and beyond future, effective utilization of these approaches is crit-
ical. Solar energy has already been shown to be a practicable source of significant
power generation. Generators convert the mechanical energy into useful electrical
power from there. Using a thermoelectric generator, energy is quickly extracted using
thermal energy harvesting. Kinetic energy sources could be a critical enabler in the
development of 5G-enabled components. With its speedy connectivity, 5G wireless
capabilities may open up new options for agriculture-related data analytics (Yuan
et al. 2020). If a generic power management framework can serve all 5G IoT devices
with energy harvesting capabilities, the field’s extendibility is limited in which intel-
ligent power management is required. The ability to stop a system while it is not in
use is arguably the most efficient way to save energy. Sensors, power management,
communication transceivers and energy harvesters make up the sensing nodes of IoT
devices. The most practical way for reducing the system’s energy usage is to use
the sleep mode to turn the BS on and off. This guarantees that the captured energy
is used efficiently. Because their subsystems stay active in the idle state, 5G IoT
devices may then also exhaust a large amount of energy when they are not trans-
mitting data/sensing. Meanwhile, one of the aspects of next-generation networks is
energy proportionality with traffic. Each BS sleep level is defined by a transition
latency threshold. The sum of the part’s reactivation and deactivation latencies is the
subcomponent transition rate of data transferred per unit time. When the BS is in
sleep state, a quick activation time is used to keep subcomponents with a long tran-
sition rate of data transfer per unit time always active. In most current systems, this
strategy is employed to preserve QoS. Utilizing the IoT device hardware capability,
the network’s flexibility and the measurement periodicity acquired by the device, the
sleep state can achieve noteworthy energy savings. The shortest BS sleep period is
in sleep mode one (Debaillie et al. 2015). A BS that is in sleep mode 1 is still active
and can receive data. When a BS is not transmitting data, it automatically switches to
this sleep state. Sleep mode two denotes a middle-state sleep situation in which more
116 P. Majumdar et al.

subparts are deactivated, and it equates to 1 ms. Finally, the BS is in standby mode in
sleep mode four, which lasts for a minimum of 1 s. In sleep mode 4, the BS is disabled,
although it can be reactivated. The data transfer of IoT components can be scheduled
with the four separate BS sleep modes to ensure that the energy harvested is used
while still meeting QoS. For the 5G frame structure, five distinct numerologies have
been established. Because of its high power density and efficiency, Lithium batteries
can provide a long battery life. Massive IoT applications necessitate miniaturized
and autonomous devices, restraining power management and energy storage ability.
Non-rechargeable batteries will also be limited in their usage as a key energy source
for vast IoT applications due to frequent replacement, environmental consequences
and a shortage of energy sources. Another battery technology is solid-state thin film,
which has a high energy concentration but low power density. Because of properties
like bendability and manufacture in IC packages, these batteries enable significant
size and cost reduction. Nowadays, supercapacitors are used in place of rechargeable
batteries as it has an unconstrained charge–discharge cycle (Somov and Giaffreda
2015). Because batteries can be moulded into a variety of shapes and sizes, they
remain a viable option for large-scale IoT deployments that require extremely low
power consumption and a 10-year lifespan. Integrating rechargeable batteries with
energy-conserving approaches is crucial to extend the lifespan of the 5G-enabled
devices by recharging the batteries.

Application Areas of 5G in Smart Farming

Data Aggregation

For centralized data aggregation in large farming operations, 5G technology holds a

lot of potential. To aggregate data from micro-monitored crop management systems,
a large corporate farm may construct a private 5G network. These systems incorporate
soil moisture sensor density that is hundreds of times greater than what is currently
supported by available technologies. This network could allow for a more efficient
real-time monitoring system, complete with triggers for limiting irrigation and other
crop support systems (Xu et al. 2017).

Predictive Analytics

Large industrial farms can better utilize predictive analytics thanks to 5G technology,
which enables data aggregation. Analytics software develops models and forecasts
based on past and current data on circumstances (e.g. soil moisture and pesticide
use) to assist farmers in making decisions. Analytics will become more exact as 5G
6 Utilities of 5G Communication Technologies for Promoting … 117

enables denser real-time data, maximizing farm production and efficiency (Sevgican
et al. 2020).

Drone Operations

Drones are increasingly being used by farmers to check their crops. Drones are less
expensive than driving tractors through fields, and they provide more precise data on
crop damage and other aspects. Drones will be able to collect higher-quality video
data and transmit it faster thanks to 5G’s high-bandwidth technology. These high-
speed data transfer capabilities will allow for the development of AI drone technology
and real-time reports (Tang et al. 2021).

Animal Tracking and Real-Time Monitoring

Animal monitoring sensors will most likely remain connected through Wi-Fi, Blue-
tooth or LTE LPWAN Until Rel 17 increases the practicality of 5G low-power and
denser sensor networks. Large concentrated farms, where 5G infrastructure can be
installed across a small area (e.g. a chicken farm) and individual animals may be
tracked, are an exception. Herd management sensors, such as smart collars and ear
tags, have been developed by agricultural technology developers to track an animal’s
position and health. An automated remedial action can be triggered based on any
variation in these variables in order to preserve the typical circumstances for crop
yield. Sensor data obtained for agriculture practices are in various forms depending
on the precision and compatibility will necessitate the use of the relevant interfaces.
To cover minimal or maximal distances, communication protocols are very impor-
tant in IoT-based smart irrigation practices. Short ranges are covered by ZigBee or
Wi-Fi, whereas to cover long ranges LoRaWAN, LPWAN protocols and Bluetooth
are used. Narrowband IoT and long-term evolution of machine-type communica-
tions (GSMA 2019) are paving the way for 5G integration in the future and will
have a significant impact on smart farming in the next years. The sensors must have
maximal-range communication and should be energy-saving (Yao and Bian 2019).
As a result, the sensors’ lifetime is significantly extended by transferring data at
reduced energy and eliminating data repetition. 5G NR improves network energy
performance and decreases interference by allowing adaptive bandwidth switching
from lower to higher bandwidth, while interworking and LTE coexistence allow
existing cellular networks to be used while still accommodating future evolution.
118 P. Majumdar et al.

Autonomous Agriculture Vehicles

Farm tools will benefit from the development of autonomous vehicle technology in
other industries. Tractors with onboard computers already allow operators to regulate
minute details of farming tasks. Farm equipment that is self-driving will improve,
allowing farmers to have more flexibility and efficiency while also saving money on
labour. IoT sensor benefits can also be reaped by trucks used for crop transportation.
These sensors can monitor cargo temperature and inform you if it gets too hot or
cold. High-latency technologies like LPWAN will likely continue to be used by small
mobile sensors like asset trackers. 5G will allow autonomous vehicles to send and
receive larger, ultra-low-latency data streams, including videos using more powerful
onboard computers (Tang et al. 2021).

Weather Stations

Farming operations are at the mercy of the weather. Large sections of crops can
be lost due to illnesses and damage that can be avoided. Farmers can tackle this
problem by using connected weather stations in the field to provide data on agri-
cultural conditions. The InField monitoring system, designed by AMA Instruments,
is one example. InField monitors soil moisture and texture, air temperature, wind
speed and exposure to the sun. Weather stations in remote locations will very certainly
continue to use LPWAN connectivity in the near future. 5G will help them because
it will allow for more data-dense observation and edge computing. Smart farming
will continue to grow as the cellular-connected world switches to 5G. Farmers will
be able to make better decisions based on data and predictive analytics, resulting in
increased productivity and efficiency (Tang et al. 2021).

5G Enabled Components in Agriculture

To implement seamless farming practices 5G enabled the use of a number of

components as summarized below.

Drones and Unmanned Aerial Vehicles (UAVs)

UAVs can boost crop yields, save time and maximize long-term performance. These
drones can be utilized for a variety of purposes. Both aircraft and ground-based
missions are possible. Drones are helpful for doing quick and effective livestock
monitoring (Vayssade et al. 2019). Farmers may fly a drone across a long distance
6 Utilities of 5G Communication Technologies for Promoting … 119

Fig. 6.1 Applications of unmanned aerial vehicles (UAVs) in the field of agriculture using 5G

using 5G technology (Faraci et al. 2018). In comparison to previous-generation

mobile networks, the 5G network allows farmers to get real-time data as well as
other critical sensory data faster. Drones do not utilize a lot of processing power,
and all the data can be transferred to the cloud. Multiple drones (Razaak et al. 2019)
can interact with one another to provide coordinated autonomous flight to perform
several tasks with least energy expenditure, allowing for extended sensing time and
economic operations. For decision-making, a large volume of data can be kept and
processed. Drones can fly to supply agricultural products using 5G network’s vast
network coverage and steady connection. The 5G cellular network combines with
drone traffic supervision technologies to improve operations’ high-quality connec-
tivity. Since, there is a large amount of data to be transferred, a data link with maximal
rate of data transfer per unit time is needed, which is provided by robust 5G network
coverage. Figure 6.1 shows applications of Unmanned Aerial Vehicles (UAVs) in
Agriculture using 5G.

Virtual Consultation and Predictive Maintenance

Virtual consultation allows session services to attend to the requirements of farmers.

Domain experts can straightforwardly derive a live streaming in real-time using
sensors to obtain thorough views regarding condition and provide farmers solution
for the improvement of agriculture and irrigation practice. Precision agriculture,
120 P. Majumdar et al.

soil sampling, disease management and animal health monitoring are some of the
services provided by consultation services. Multiple machineries can be monitored
in real time with 5G having fast transmission speed and low latency to monitor in
advance and give repairs on time without any interruption. Using several sensors to
monitor a huge number of weather conditions in real time, 5G will provide a new
maintenance paradigm called advanced predictive maintenance. Based on feedback,
the farmer is alerted of any forthcoming issues and any weakening parts, allowing
repairs to be planned at suitable time rather than postponing any operations (Compare
et al. 2020). This can drastically diminish unintended downtime caused by defective
apparatus or machine malfunction.

Augmented and Virtual Reality

Farmers can benefit from augmented reality (AR) and virtual reality (VR) equipments
in a diversity of ways. Through wearable glasses and smartphones, AR can provide
diversities of information such as crop, animal and machinery statistics, soil and
weather pattern changes, disease exposure for livestock, land examination and more
(Garzón et al. 2020). The farmers can acquire important information like if the
crops are unwell or when they can be reaped or sown using AR glasses. So, farmers
can cultivate in a more efficient way to potentially lessen labour and ensure timely
delivery while ensuring a premium quality harvest. Virtual reality can be utilized
for immersive agriculture training and practice (Wang et al. 2014). An interactive
VR experience increases the connectivity requirements even further. By offering
realistic and powerful experiences for learners, 5G will enable online interactive
learning taking maximum benefit from augmented reality and virtual reality over
conventional offline education.

Agriculture Robots

The collaboration of AI and 5G exposes new advantages in live video monitoring,

remote diagnostics, as well as the stabilization of drones and robots using precise
parameter management. AI in agriculture is budding on a continuous rate to give
modern solutions for enhancing crop yield and AI-powered robots are ready to revo-
lutionize the industry. Recently, agricultural robots have been used to autonomously
plant a variety of crops across large areas of land. Robots are designed to plot a
route themselves all through the Machine vision is a core potential, allowing them
to perceive, identify, confine and implement intelligent actions on plants. To avoid
collisions, laser rangefinder is used to detect impediments in the robot’s pathway
(Ramin Shamshiri et al. 2018). The robot can plot a route to its surroundings and
be directed from any location utilizing cloud computation and a noteworthy amount
6 Utilities of 5G Communication Technologies for Promoting … 121

of data is sent via 5G. Transmission of real-time photos recorded from sensors with
super low latency via 5G network (Aijaz et al. 2017).

Cloud-Based Data Analytics

Data is one of the most significant aspects that are developing the smart agricul-
tural business forward. On numerous farms, all the data acquired from sources like
sensors and drones, are saved in cloud. 5G and edge computing allows data trans-
fers to the cloud, allowing real-time analytics to help automate the farming process.
Larger data must be transported to the cloud and then returned to users. To reduce
complexity, cloud-based edge computing is mostly employed in smart robots. The
cloud can be utilized as a data centre or a host for storing robot navigation and data
processing control services. In the precision agriculture scenario, intelligence anal-
yses these data in real time to develop AI for protective drones or machineries. By
placing the GPU on the edge server, the need for the robot’s graphics processing
unit (GPU) is eliminated. Because the data processing bandwidth is so high, only 5G
can handle it. The robots’ physical size, power expenditure and cost have all been
decreased dramatically. Over existing cell networks, 5G will vastly improve the data
transfer experience. A huge volume of data may be successfully sent across several
devices while minimizing data loss, reducing connection downtime and avoiding
retransmission of data that consumes much time while transmitting a large number
of data unnecessarily. Cloud computing provides faster data acquisition, transmis-
sion and processing at the cloud with minimal round transfer latency between various
5G devices, enabling maximum efficiency for sustainable agriculture management
(Song, et al. 2019). Sensors, drones, robotics and smart devices, are the usage of
5G. There are several 5G characteristics like device density, ultra-low rate of data
transfer per unit time, ultra-reliability and security. Drones, robotics and IoT sensors
work together to increase output and drastically decrease price. Figure 6.2 shows
applications of data science and cloud repository.

Recent Development Scenarios in 5G-Based Agriculture

Relevant Literature based on recent development scenarios in 5G-based agriculture

concerning UAVs, predictive maintenance, AR and VR, real-time monitoring and
agribots are summarized as follows (Table 6.1).
122 P. Majumdar et al.

Fig. 6.2 Applications of data science in data analytics and cloud repository

Conclusion and Future Work

The 4G network although allows faster data transmission rates and adequate
coverage, it is unable to transmit the massive amount of data between the number
of devices. 5G comes into picture to meet the requirement of precision agriculture
for improved output with a lesser amount of effort. In the forthcoming days, the
5G networks will be implemented in all industries; so, internet price will be much
reduced and connectivity is going to be boosted. The utilization of 5G will drastically
lower the implementation costs, which will be a godsend to farmers. Farmers will
be well equipped for smart farming, with the capacity to use their mobile phones to
forecast and prevent crop disease. By expanding their physical infrastructure, mobile
carriers will make substantial contributions to precision agriculture. Data from the
field will be collected by sensors and saved in the cloud. Sensors having a prolonged
battery life will grow smaller and less expensive and networks will be more effi-
cient, become smarter as well as secured. Although 5G has several applications and
benefits in the agricultural industry, it will fundamentally alter the structure of jobs.
There’s a good chance that the number of agricultural jobs will decrease. Specific
power supervision approaches, like sleep modes, have to be implemented to map the
5G network’s no-load traffic allocation and maximize the use of harvested energy. To
accomplish the ideal active periods of the 5G base stations while fulfilling the quality
of service rendered, the active times of IoT devices should be efficiently coordinated.
6 Utilities of 5G Communication Technologies for Promoting … 123

Table 6.1 Recent development scenarios in 5G-based agriculture

Methods Contribution
Unmanned aerial vehicles (Li Various drones can interact with one another to offer
et al. 2019) synchronized autonomous flying across a geographic area to
execute numerous jobs with little data communication loss and
battery expenditure, allowing comprehensive sensing time and
gainful economic operation
Unmanned aerial vehicles A large number of obtained data can be kept and processed. A
(Aweiss et al. 2018) number of self-directed drones can soar across the air to deliver
agricultural items to doorsteps using the 5G cellular network’s
stable connection. Unmanned aircraft system traffic
management, flights outside line of sight and sensor data
transmission are three major requirements for connecting
drones utilizing 5G cellular connectivity
Real-time monitoring (GSMA Machine-type communications with long-term evolution and
2019) narrowband IoT are paving the way for 5G integration in the
future. It is used to connect numerous sensors to a single
cellular tower, allowing farmers to set up IoT devices to
perform tasks proficiently
Predictive maintenance Based on the response of 5G sensors, the end user is alerted
(Compare et al. 2020) regarding any upcoming issues and deteriorating parts, allowing
maintenance to be scheduled in a timely way and without
stopping any operations. It can significantly reduce unintended
downtime caused by machine breakdown
Augmented reality and virtual Wearable glasses and smartphones can provide useful
reality (Garzón et al. 2020) information about crop, animal and machinery statistics,
weather updates, AI-powered disease identification, land
assessment, etc
Agribots (Skvortov et al. AI-powered robots are programmed for using Global
2018) Positioning System to plot a route and recognize fruits and
vegetables that are ripe to harvest
Augmented reality and virtual Using a rapid and forceful mobile network, broadcast of inputs
reality (Skvortsov et al. 2018) and audio output is reverted to the user with ultra-low data
transfer time, allowing users without experiencing motion
sickness

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Chapter 7
Security Attacks and Countermeasures
in 5G Enabled Internet of Things

A. K. M. Bahalul Haque, Tasfia Nausheen, Abdullah Al Mahfuj Shaan,

and Saydul Akbar Murad

Abstract The use of previous generation networks like 4G was vastly used in the
Internet of Things (IoT) devices. The constant need to grow and develop just so the
network can fulfill the requirement of IoT devices is still going on. The exponential
growth of the data services substantially challenged the security and the networks of
IoT because they were run by the mobile internet requiring high bit rate, low latency,
high availability, and performances within various networks. The IoT integrates
several sensors and data to provide services and a communication standard. Fifth
Generation Communication System (5G) enabled IoT devices to allow the seamless
connectivity of billions of interconnected devices. Cellular connections have become
a central part of the society that powers our daily lives. Numerous security issues
have come to light because of the exponential expansion of 5G technologies and the
adaptation of the slow counterpart of IoT devices. Network services without security
and privacy pose a threat to the infrastructure and sometimes endanger human lives.
Analyzing security threats and mitigation is a crucial and fundamental part of the IoT
ecosystem. Authorization of data, confidentiality, trust, and privacy of 5G enabled
IoT devices are the most challenging parts of the system. And to provide a solution to
these, we need a robust system to handle cyberattacks and prevent vulnerabilities by
countermeasures. This paper includes a comprehensive discussion of 5G, IoT funda-
mentals, the layered architecture of 5G IoT, security attacks and their mitigation,
current research, and future directions for 5G enabled IoT infrastructure.

Keywords IoT · 5G · Security and privacy · 5G IoT networks · SDN · NOMA ·

WSN

A. K. M. Bahalul Haque (B) · T. Nausheen · A. Al Mahfuj Shaan

Department of ECE, North South University, Dhaka, Bangladesh
e-mail: bahalul.haque@lut.fi
T. Nausheen
e-mail: tasfia.nausheen@northsouth.edu
A. Al Mahfuj Shaan
e-mail: abdullah.mahfuj@northsouth.edu
S. A. Murad
Faculty of Computing, Universiti Malaysia Pahang, Pekan, Malaysia

© The Author(s) 2023 127

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_7
128 A. K. M. Bahalul Haque et al.

Introduction

Fifth Generation Communication (5G) networks are constantly developing, and it is

becoming a significant catalyst for the growth of Internet of Things (IoT) devices
(I-Scoop 2018). Future applications of IoT related application are going to be inte-
gration of various other technologies. IoT integration with other technologies can
facilitate seamless connections between heterogeneous devices and promote ubiqui-
tous computing infrastructure throughout our daily life. However, multiple complex
integration challenges have emerged because of the heterogeneity and fragmenta-
tion of the connectivity system that hampers IoT development (Rahimi et al. 2018a).
Moreover, as the 5G technology is emerging, this is considered the pioneer of IoT
development. As the application of IoT is becoming widespread and diverse, the secu-
rity and vulnerability of 5G IoT networks are becoming a significant concern (Haque
et al. 2021). The services 5G integrated IoT provides need to be secured against
cyberattacks and data loss. Even though there are no discrete security systems for
IoT devices, they mostly rely on detecting vulnerabilities and taking countermeasures
(Li et al. 2018).
Security is an integral part of any infrastructure. The same is true for IoT and
5G integrated architecture. Therefore, it is crucial to address the issues of cyber-
attack vectors, analysis, prevention, and countermeasures of 5G IoT networks. IoT
devices are interconnected, combining various types of sensors, actuators, embedded
software, and operating systems to run the network (Gautam et al. 2019). The applica-
tion vectors have also spread across from our day-to-day hose hold toward industrial
implementation. IoT devices are generally equipped with low-power capacity, rela-
tively smaller physical architecture, and closely attached wireless and ward links.
Some of these attributes of IoT are used by the cyber attackers to exploit the network
by probing, inserting malicious codes, and unauthorized access to the network.
Third Generation (3G) communication and Fourth Generation (4G) communi-
cation technology are widely used in different walks of lives and around our daily
environment. However, both the 3G and 4G technologies are not completely opti-
mized for the applications of IoT. 5G technology is drastically improving and their
integrations with IoT have proven to be reliable and capable. For the last 20 years,
many M2M technologies have been implemented like BLE (Rahimi et al. 2018a),
ZigBee (Alam et al. 2015), RPMA (Girson 2018), SigFox (Chen et al. 2017), LoRa
(Palanisamy et al. 2022), etc. There are many other technologies like these, and they
bring in a new set of challenges for the 5G technology (Mehbodniya et al. 2022).
However, they are required to meet the necessities of IoT applications as well (Singla
et al. 2021).
Motivated by the factors mentioned above, in this paper, we have comprehensively
analyzed the integration factors of 5G IoT and security threats and countermeasures
(Dener 2014; Teniou and Bensaber 2018; Sinha et al. 2017). We have analyzed
various security threats at different levels of 5G enabled IoT devices from existing
literatures and comprehensively presented the countermeasures as well. We have
also outlined the research challenges associated with 5G IoT, which were not limited
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 129

to reliability and communication issues. As 5G incorporates several technologies

(Howe 2006; Rahimi et al. 2018b; Ahmad et al. 2018), it poses a significant impact
on IoT applications. The paper’s primary objective is to gain insights on the overview
of the 5G network IoT devices, analyze the security issues, and develop solutions
to prevent them from happening. The major contribution of this paper is listed as
follows:
. This paper highlights the key features, architectural description, and extensive
review of the 5G network.
. This paper provides an in-depth understanding, insights, and comprehensive
description of IoT.
. This paper comprehensively discusses the integration trend, layered architectural
analysis, and state-of-the-art technologies involved in 5G and IoT.
. This paper postulates various types of threats existing in 5G IoT networks.
. This paper also holistically analyzes the threat analysis and countermeasures of
5G IoT networks.
. Finally, this paper discusses the research gaps and future research directions of
5G IoT.
The paper was introduced in “Introduction” section; Second section includes
the “Overview of 5G”; Section “Overview of IoT” provides a background on IoT;
Section “5G Enabled IoT” contains the outline of the 5G and IoT integration;
Section “5G Enabled IoT Architecture” includes the architectural description of 5G
IoT integration; Section “Technologies in 5G Enabled IoT” introduces the technolo-
gies of 5G and IoT integration; threat analysis and countermeasures are comprehen-
sively described in section “Threat Analysis in 5G IoT”; Eighth section demonstrates
the “current Research for 5G and IoT”; Section “Future Research Directions and
Challenges” demonstrate the current research trends and future research directions
for 5G enabled IoT devices; and lastly, “Conclusion” section concludes the paper.

Overview of 5G

In telecommunications, the mobile network that has been launched is 5G which is

available in different cities all over the world. After the launch of 5G, it has come
to attention that it is far more significantly enhanced than 4G LTE, especially in 3
aspects. In the advanced 5G core network, a support network function virtualization
and network slicing are present, which is actually, Software Defined Networking
(SDN) (Bosshart et al. 2014; Lin et al. 2018; Chuang et al. 2018). The SDN in a
network decouples the data, as well as the control planes, and these control planes
are the ones who play a role in issuing instructions for the SDN switches in the data
plane, and this control plane is present in the SDN controller.
Programmability, centralized policy management, and a global network state are a
few of the advantages of SDN to the 5G system. Still, the benefits have to be properly
looked at in the 5G core network. For this very reason, it is a must to maintain security
130 A. K. M. Bahalul Haque et al.

within the communication channel to prevent potential attacks and safely guard the
privacy and security of the data. In today’s time, 5G, the state-of-the-art technology,
can create new interfaces for regularly used devices and networking components.
One of the essential roles of a 5G connection is to build connections between huge
numbers of users so that it can provide more competent and faster communications.
The design of 5G was done in a way where it can provide better coverage, bandwidth,
reliability, and latency because these are what make 5G better than any other mobile
network that was launched before 5G. However, even being better than other mobile
networks, like any other, there are security issues, and there are several issues that
look into these 5G security issues.
Ferrag et al. (2018) show 5G authentication and privacy-preserving surveys.
Prasad et al. (2018) show privacy surveys, replay, bidding down, attacks on control,
and user planes. But there was, this study in Basin et al. (2018) gives a formal
analysis of the authentication of 5G. Jover and Marojevic 2019 showed a few unre-
alistic assumptions made by 5G specifications, which caused the adversarial attacks
because of the vulnerability in the 5G systems until other optional security features
were added. And potential security attacks or threats were shown by Teniou and
Bensaber (2018) which measures are to be taken for the security of 5G. It also indi-
cates that IPsec, a security protocol, is used primarily on LTE and to make IPsec more
secured for the communication of 5G, IPsec tunneling can be done by authentication
integration, integrity, and encryption.
As the mobile communication network is actually on the way to completing the
5G network cycle, it is becoming capable of supporting novel usage scenarios with
stringent performance requirements. But 5G is not just stuck to seamless broadband
connectivity only. It is already on the verge of advancement toward the vision of
IoT. It has been prepared in order to be able to enable a wide range of machine-type
applications. In today’s world, wireless media is the way to have most communica-
tions that are actually open to various attackers. For this reason, efficient security
operations must include and have (Sinha et al. 2017).

Overview of IoT

Nowadays, the advancement of technology, the IoT, is a significant paradigm shift

in mobile service providers and the manufacturer of electronic devices. It helps to
contemplate their business models and innovation context. IoT helps create an inter-
connection of billions of devices via the internet and has a tremendous growth rate.
IoT is a network of devices consisting of sensors and actuators and helps to enable
multiple applications and services by exchanging data between each other. The end-
user does need compute-intensive operations, and along with it, there is a need for
huge storage and real-time communication, and it is thought to be not an efficient
way by the cloud service providers. And one of the essential features of all is the
authentication of legitimate IoT devices (Howe 2006). And the authentication is
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 131

addressed with the help of certificates given by a specific Certificate Authority (CA)
but is one of such which offers a lightweight solution.
Smart city, among a large number of applications, is an integral field of IoT that
is increasing the number of smart services within IoT systems (Rahimi et al. 2018b;
Ahmad et al. 2018). The IoT application focuses on cities that are always composed
of and controlled by computing units (Bosshart et al. 2014). Different definitions
are given to smart cities like intelligent and digital cities (Lin et al. 2025). Smart
cities focus on improving the service quality of the people and taking advantage
of resources or decreasing the costs of public administration (Chuang et al. 2018).
Smart lighting or smart traffic, and many other services are seen to grow exponentially
(Ferrag et al. 2018). But with efficiency and advanced technology comes security.
Security is the most crucial and the most significant feature of any smart device
with an IoT architecture. Data confidentiality, authorization, trust, and client privacy
are security challenges IoT systems face nowadays. So, to challenge these security
problems, secured taxonomy is applied in order to handle cyberattacks and all other
vulnerabilities using forensic techniques (Prasad et al. 2018).
Without efficient and strong security, IoT devices can create trouble rather than
making life of people easier and more efficient because these devices end up endan-
gering the privacy and safety of the people. There might be no trusted security, but
the advancement and growth in the IoT systems and their services depend on recog-
nizing potential security breaches and not being able to defy them. Security breaches
occur due to various communication technologies being used in different layers of the
wireless sensor network. The security and privacy issues were looked at with more
details of the three-layer IoT architectures (Basin et al. 2018), and those defects were
further investigated in Jover and Marojevic (2019).

5G Enabled IoT

Various types of research from the academic and industrial point of view focusing
on 5G and IoT have been conducted (Ahmad et al. 2017; Liyanage et al. 2018;
Bhushan and Sahoo 2017). Currently, advances are seen in theory, applications, and
standardizations, especially in the implementations of the technologies related to 5G.
The most crucial focus is to offer them a place to grow within IoT scenarios. And in
the past few years, various work has been done on 5G and IoT as well (Ahmad et al.
2017). On 5G, a wireless research project was initiated by CISCO, Intel, Verizon
combinedly to launch a novel set, “Neuroscience-Based Algorithms,” and for the
requirement of the human eye, adaptive video quality was launched where a hint
was shown that it even has wireless networks which would include built-in human
intelligence (Liyanage et al. 2018).
5G played a crucial role in the advancement of IoT because of 5G, billions of
smart devices could create an inter-connection and interact and share data without
the help of any users (Bhushan and Sahoo 2017). But recently, different application
domains are making it more complicated for IoT to recognize devices that meet the
132 A. K. M. Bahalul Haque et al.

application needs requirements (Bhushan and Sahoo 2017). IoT system which exists
vastly uses fixed application domain like
. BLE
. ZigBee, etc.
There are other technologies like
. WiFi
. LP-WA networks
. Cellular communications, e.g., MTC using 3GPP and so.
IoT is constantly evolving quickly but with evolved proposals and new application
domains. But IoT is growing and becoming more efficient to make people’s lives more
efficient and fast paced and trying to make more efficient inter-connection networks
between smart devices. But with the growth of Industry IoT (IIoT), new challenges
and obstacles are also coming in the way, like, the need for new advanced addition to
the existing business models and products and solutions for the betterment (Ta-Shma
et al. 2018). And there are technical challenges in Industry IoT:
. Reliabilities
. Timeless
. Connection robustness, and so on.
There are most used communication techniques within the IoT connectivity and
are, 3GPP and LTE (Rathore et al. 2016), which are offered to the IoT systems also
like (Zanella et al. 2014),
. Wide coverage
. Low Deployment costs
. High-security level
. Access to dedicated spectrum
. Management simplicity.
However, MTC communication cannot bear the cellular networks present because
those present cellular networks are the primary key in IoT, which is one of the
problems. But this isn’t a problem when instead of MTC communication, the 5G
network is used because the present cellular networks are making it faster in terms
of data rate, and it occurs because of low latency and the better version of MTC
communication with respect to current 4G (LTE) and which results in more efficient
IoT applications and devices.
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 133

5G Enabled IoT Architecture

More efficient and advanced architecture is needed, which will help achieve more
sustainable and efficient new technologies. More scalable architectures of IoT devices
will be better than the present 5G IoT architecture (Jin et al. 2014). The advantage
of using new technology during the development is that it makes the architecture
. More simple
. Convenient for scalability
. Analysis
. Modularity
. Efficiency
. Agility
. Accessibility to high-demand services
. Eight layers interconnected along with data exchange capability, two-way, this
architecture has been designed, explained below (Jin et al. 2014).

L1 Physical Device Layer

The general layer of the architecture of the IoT includes

. Wireless sensors
. Actuators
. Controller

L2 Communication Layer

The two sub-layers discussed below.

Device to Device Communication Sub-Layer: 5G enhances D2D communication
in this sub-layer and is an important participant that provides connectivity for devices
with Machine-Type Communication (MTC) (Mohammadi et al. 2018).
Connectivity Sub-Layer: Cell phones, tablets, etc., are the devices connected to
the BSs communication centers within the sub-layer. These devices proceed with
data analysis and are then sent to the storage units through centers with an Intranet
connection (Millr 2015; Conti et al. 2018). Those are specified with
. High Reliability
. Performance
. Agility.
134 A. K. M. Bahalul Haque et al.

L3 Fog Computing Layer

Within this layer, in order to make decisions, edge processing is applied on the data
by the nodes (Kumar and Patel 2014).

L4 Data Storage Layer

Physical devices send the information of edge processing to the data storage units
here in this layer (Zhao and Ge 2013). And here, large amounts of data are handled
and the traffic of the future devices and applications but not without the data security,
which is the key of this layer.

L5 Management Service Layer

Here in this layer, there are three sub-layers, and these are
Network Management Sub-Layer: Communication purposes occur between
devices and data centers in this layer. 5G IoT or ZigBee are communication proto-
cols where the network type is consistent with the technology present in this layer:
Wireless Network Functionality Virtualization (WNFV). IoT networks are managed,
and network reconfiguration is enabled because of the Wireless Software Defined
Network (WSDN) (Xu et al. 2014) technology. Because of this technology, traditional
network monitoring for performance enhancement is unnecessary.
Cloud Computing Sub-Layer: The data from a layer from Fog Computing Layer
can be reprocessed in this sub-layer, and then the processed information is generated
in the final step.
Data Analytics Sub-Layer: For generating values for decision-making in this
layer, new learning algorithms of data analytics can be implemented to the last sub-
layer information (Millr 2015; Conti et al. 2018). Since the information from the IoT
networks is collected, it starts turning more dominant and expanding with time.

L6 Application Layer

Business related people make the most of use of this layer because they need to plan
with the correct data, and it also helps in revolutionization, which are (Kumar and
Patel 2014).
. Vertical markets and business need by control applications
. Vertical and mobile applications
. Business intelligence and analytics.
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 135

L7 Process and Collaboration Layer

Collaborations and communications are permitted in these layers within IoT devices
and services. That occurs because the data and information cannot be utilized with
a single entity since they come from the previous layers (Rahimi et al. 2018a).

L8 Security Layer

This is the protection layer for all the other previous layers, and this protection is
done without impacting the different previous layers’ functionality. Also, the security
taxonomy for blocking and foreseeing the dangers of cyberattacks is protected here
in this layer.
The Fig. 7.1 added below shows a brief overview of the 5G IoT architecture.

Technologies in 5G Enabled IoT

In the last decade, much research has been done on 5G enabled IoT (Mohammadi
et al. 2018). To build the state-of-the-art IoT and 5G systems, extensive research is
done by academics and industry (Millr 2015; Conti et al. 2018; Kumar and Patel
2014). 5G enabled IoT devices can significantly impact the interconnections of IoT
devices. Heterogeneous networks currently are unable to satisfy the needs of the
application of IoT devices (Zhao and Ge 2013). Popular IoT systems include BLE,
ZigBee, WiFi, LP-WA, etc. (Hošek 2016). The current systems focus on improving
our regular life, making a better-quality life, and engaging interconnections between
smart homes, smart cities, agriculture, and healthcare (Mohammadi et al. 2018;
Millr 2015; Conti et al. 2018). 3G and LTE networks are currently the most used
connectivity technologies that offer low cost and wide coverage. However, these

Fig. 7.1 5G IoT architecture

136 A. K. M. Bahalul Haque et al.

Table 7.1 Technologies in 5G enabled IoT

Technology Use cases
Heterogeneous Enables on demand transmission rate for 5G IoT (Millr 2015; Conti
networks (HetNet) et al. 2018)
Direct device to D2D was proposed to create communications between short ranges.
device (D2D) power efficient, optimized power and communication load occur here
and are expected to provide an efficient spectrum (Millr 2015; Conti
et al. 2018)
Spectrum sharing To enable the technology in covering the traffic load imbalance,
spectrum management is the key. Massive MIMO is centerpieces of
spectrum sharing (Mohammadi et al. 2018; Millr 2015)
Zigbee It’s a power optimized and cost efficient mesh network widely used in
WSNs and primarily used in Industrial IoT applications (Millr 2015;
Conti et al. 2018)
Other technologies Other enabling technologies include machine-type communication
(MTC), mmWave, SDN, NFV, and NB-IoT

present networks cannot manage to support MTC, which is the key to enabling the
factor in IoT devices (Jin et al. 2014; Mohammadi et al. 2018).
Several 5G enabled IoT technologies have been developed in the last few years,
and some are developing. Some of these are described in Table 7.1.

Threat Analysis in 5G IoT

As security in IoT is emerging as one of the significant factors in security schemes,

it is essential to analyze and mitigate the attacks. In this section, categorization of
various attacks and countermeasures to emphasize the contribution to this paper are
done.

Eavesdropping

The attackers of eavesdropping try to intercept some of the confidential information

but detecting the legitimacy of the transmitters or receivers is challenging to locate
or trace since the attackers do not transmit any signals (Xu et al. 2014; Kaplan 2018).

Interception

The attackers can easily detect the authentication of the communication since they
snoop within the nearby wireless environment. With this technique the attacker can
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 137

capture the information about the network. The network information can include the
configuration, sensory data transfer protocol, etc. Eavesdropping through intercep-
tion is one of the most effective and oldest techniques to exploit the security (Hošek
2016; Liyanage et al. 2018).

Traffic Analysis

Cryptographic algorithms may encrypt the critical information in legitimate commu-

nication. In this case, attackers intercept the transmitted signal. Still, they cannot
obtain significant content, but traffic analysis can come in handy for the tracking of
communication patterns in order to realize other forms of attacks (Wang et al. 2019).

Contaminating

In this type of attack the attackers try to gain illicit access to the network and contam-
inate the channel estimation stage as well (Astely et al. 2013; Palattella et al. 2016).
This sort of attack can be categorized into two types of contamination according to
different channel.

Spoofing

Attackers inject fake identity information to destroy or join communications. The

attacker is able to establish a fake communication channel between two or more
legitimate points. In this way, unknowingly, two legitimate parties communicate
with each other through a fake entity (Liyanage et al. 2018).
Here, malicious nodes can copy other nodes, claim fake identities, and generate
a random number of different identities only using hardware devices (Li et al. 2021;
Lin et al. 2019). Sybil types of attacks make the system generate false reports, and
that can make users get spam and lose privacy (Wang et al. 2021).

Jamming

Here the target of the attackers is to block legitimate communication using noise
(Haque and Bhushan 2021), and an adversary can send continuous signals by
decreasing signal to noise ratio (Xu et al. 2014) through the channel only to hamper
communication. It can also prompt DoS attacks at the physical layer (Kaplan 2018).
There are three types of signal jamming, in general, such as pilot jamming, proactive
jamming, and reactive jamming (Hošek 2016).
138 A. K. M. Bahalul Haque et al.

Pilot jamming is launched when a channel is trained (Hasan and Hossain 2013; Ge
et al. 2014; Ahmad et al. 2020) and aims to create an illegitimate connection without
the exact pilot sequences. An adversary can launch the attack when he knows the
pilot length and sequence. Pilot jamming is also very efficient as only the signals
need to be corrupted (Millr 2015; Conti et al. 2018; Haque et al. 2020).

Physical Layer Security

5G and IoT are the fundamental paradigms of today’s time, and for the security of
wireless communication, physical layer security is becoming a growing prospect.
PLS protects the confidentiality of data by exploiting the intrinsic randomness of the
communication medium (Padmavathi and Shanmugapriya 2009; Shiu et al. 2011).
This technique plays an aid in improving 5G IoT security from two main aspects,
The network latency on the Internet of Vehicles (IoV) and Unmanned Aerial Vehi-
cles (UAV) can be reduced. The vehicles can randomly join and leave the network,
making the UAV highly dynamic (Steinmetzer et al. 2018). PLS will offer an efficient
and quickest authentication by exploring radio frequency (RF) fingerprint otherwise,
roaming in different networks will lower communication performance. Different
schemes in PLS can be additional protection that cooperates with the existing security
architecture to provide better protection for 5G IoT devices.
Random wireless channel use cases are done to generate keys in PLS schemes
that can release the burden (Zhou et al. 2012) in 5G IoT networks; it becomes
difficult or rather challenging to achieve effective key distribution and management.
Reinforcing communication security can be done without encryption and decryption
using information theory.

Massive MiMo

Core 5G technology received considerable attention in IoT research (Nitsche et al.

2014; Ylmaz and Arslan 2015). It helps in providing numerous communication supe-
riority based on the beamforming technology like an array that gains channel hard-
ening and nearly orthogonal channels (Ylmaz and Arslan 2015). In the meantime,
MiMo techniques for PLS are also discussed in Zeng et al. (2010), Newsome et al.
(2004).
Passive eavesdropping (Xiao et al. 2009) and also the active attacks in massive
MiMo were investigated by many authors. The analysis showed that PLS against
passive eavesdropping could increase pilot contamination attacks (Zhang et al. 2014),
and it is fatal for MiMo communication since an active attacker can send the same
pilot sequence.
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 139

NoMa

IoT with NoMa and non-orthogonal resources can improve spectral efficiency and
also reduce low transmission latency and signaling cost (Mpitziopoulos et al. 2009),
and it can also be used where the number of sensors is huge, like in smart farming and
intelligent manufacturing (Bhushan and Sahoo 2020). Allocating two users to a single
orthogonal resource block for user pairing is a technique for balancing complexity
and efficiency (Clancy 2011). The capacity to superpose numerous signals into an
orthogonal resource is achieved via superposed coding technology.

MmWave

A 5G technology that can improve network transmission (Mpitziopoulos et al. 2009;

Yang et al. 2018) helps the devices connect to higher bandwidth communication
channels (Wood et al. 2007). Characteristics like blocking effect, highly directional
transmissions are new in mmWave (Wood et al. 2007). The new characteristics of
mmWave channels can help in improving the efficiency of traditional PLS techniques
(Wang et al. 2018; Hamamreh et al. 2018; Arsh et al. 2021). Because of the tiny
wavelengths of mmWave, dozens to hundreds of antenna elements may be put in an
array on a small physical platform, which significantly aids the application of MiMo
and the integration of different 5G technologies (Zeng 2015; Lu et al. 2014, 2018;
Araujo et al. 2016).

Trust Mechanism in WSN

This mechanism in WSN has been emerging as a significant factor when it comes to
security schemes, and so, it is really a necessity to analyze how these attacks can be
resisted with the help of trust schemes (Zhou et al. 2012; Goyal et al. 2021; Zhu et al.
2014; Kapetanovic et al. 2015). Recently these mechanisms have been remodeled
to filter the fake nodes in a sensor network. This approach was first introduced
in E-commerce to choose dependable transaction objects, and many researchers in
different fields have since developed it (Zhou et al. 2012). Because the evaluation
of trust is entirely based on past behaviors of participants or indirectly mixed with
the reputation of other recommenders, this mechanism has the potential to be more
efficient, but higher standards are required to develop an effective trust framework
in WSNs because of
. Limitations of energy
. Limited Storage Space
. Wireless communication’s inherent vulnerabilities.
140 A. K. M. Bahalul Haque et al.

Crowdsourcing Analysis

In 5G networks, crowdsourcing is a potent tool against hackers. The major goal of

this analysis is to present the problem to a participatory community that is eager
to solve the problem and then anticipate a reward (Zhou et al. 2012). This concept
has been used in the IoT in a variety of ways by users and their IoT devices for a
variety of reasons. (Ding et al. 2017) However, there hasn’t been enough discussion
of how these features can help mitigate the impact of cyberattacks in truly complex
networks, which are particularly vulnerable due to the wide range of technologies at
various levels of abstraction, as in the case of 5G networks (Li et al. 2018; Gautam
et al. 2019; Rahimi et al. 2018a). The concept of crowdsourcing connects both the
5G and the IoT network world naturally
. Participant’s interests are defined (users and providers)
. Motivating mutual cooperation to stop cyberattacks

Commercial Purposes

The 5G business model requires infrastructure sharing among service providers,

and in this case it is very important to use crowdsourcing analysis between the
service providers to identify attacks (Millr 2015; Conti et al. 2018). It is essential
to use security mechanisms to mitigate potential attacks and ensure privacy and
confidentiality. Malware Information and Sharing Platform also uses a similar system
to develop countermeasures for threats (Ding et al. 2018).

Removing Physical Attacks

Data coming from mobile phones are used in crowdsourcing analysis, and it is used
to provide a warning system. Software and physical attacks are very different and
require more research (Ding et al. 2016). A novel way of solving problems like finding
the attacker’s location is to expand the security controls at the edge of the user’s IoT
device, and crowdsourcing analysis can be implemented to identify potential attacks
(Rappaport et al. 2013).

Social Media

Social media like WhatsApp, Twitter, and Facebook are the biggest platforms for
crowdsourcing (Niu et al. 2015). Social media is a gateway for crowdsourcing, and
it can be done in two ways: 1. by the traditional method, which involves humans but
focuses on the attacks on the systems of the whole infrastructure, or 2. the other is
done by extracting relevant information and identifying attack patterns (Heath et al.
2016).
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 141

Attacks at the Architecture Level

Today’s world is becoming more interconnected, and smart cities are the key. In smart
cities, various IoT devices are integrated, and nodes in smart cities are vulnerable to
security threats like DoS attacks and manipulation of data (Wang and Wang 2016).

L1 Physical Device Layer

Various threats and attacks can damage the sensor nodes in the architecture (Gautam
et al. 2019). Some attacks in the L1 are described below.
. Unauthorized Access to Tags: Attackers can easily access tags in RFID due to
the lack of proper authentication techniques.
. Tag Cloning: RFID tags can be cloned in the physical layer, and reverse
engineering can extract relevant information.
. Sleep Deprivation Attack: Unstoppable sending of control information is done
in this attack and it keeps the nodes constantly in a working state in the network.

L2 Communication Layer

. DoS Attack: DoS attacks engage the user to overflow the victim’s system with a
large amount of network traffic.
. Sybil Attack: This attack deceives the victim to do one task multiple times as it
shows pseudonymous identities in the node.
. Replay Attack: During eavesdropping, a valid data packet is collected from the
network and every time the user connects to the network, and the attackers collect
resources from them.
. Sinkhole Attack: The flow of data is attracted from other nodes residing nearby
by using another compromised node.

L3 Fog Computing Layer

Fake gateways and attackers replace edge devices to collect data from these edges
(Zhou et al. 2012).

L4 Data Storage Layer

Data privacy, confidentiality, and integrity are concerned with any IoT data storage
system.
142 A. K. M. Bahalul Haque et al.

L5 Management Layer

The attackers attack the server, database, and other services in this layer.
. VM Manipulation: VMs run in the host system, and the adversary controls it.
This can be attacked, and the range of these attacks is from extracting information
and manipulating data in the VM (Xu et al. 2014).
. Flooding Attacks in Cloud: This attack is done in the sub-layer of the cloud, and
the attackers frequently send service requests (Xu et al. 2014).
. Cloud Malware Injection: Malicious services can be inserted into the cloud and
used to manipulate the system, and sensitive data could retrieve sensitive data.

L6 Application Layer

Attacks in this layer are mainly to access the data of users.

. Code Injection: Attackers insert worms and other malicious codes to exploit the
errors in the program and gain system control.
. Buffer Overflow: Attackers use programs to violate the data buffer or codes to
overflow the entire system.
. Permission Manipulation: This attack mainly leads to the illegitimate adminis-
tration of data and violates user privacy.

Current Research for 5G and IoT

This section discusses the current research topics and solutions that can also be
introduced to future research directions.
The number of research done on mobility is minimal, and it is a fascinating subject
matter in terms of mobility in physical layer attacks on both user and attacker sides.
The attacker may use the mobility feature to find the best area of attack and try to
avoid detection. Mobility can also be used to counter this attack. But users might
also have to consider the performance if mobility is implemented and investigating
the 5G IoT mobility system is a current research topic (Mohammadi et al. 2018).
mmWave and NOMA are new features currently available in 5G technology. Few
studies show exploitations of these new features to achieve physical layer attacks.
And schemes for these new features are yet to be found (Millr 2015).
Trust models for securing data are another field where data sensing and aggre-
gation are focused. The wide range of data types and privacy safety increases the
obstacles and brings in newer problems (Haque et al. 2021; Li et al. 2018; Gautam
et al. 2019). Current threats and attacks in the WSN can be identified with trust
models, and also trusted models can be used to plan the attack itself. And currently,
the analysis of existing and potential threats is the objective (Mohammadi et al. 2018;
Conti et al. 2018; Conti et al. 2018).
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 143

Future Research Directions and Challenges

Even though 5G satisfies the requirements for IoT security, it also opens up newer sets
of challenges like architecture security of IoT and verified communications between
devices. In this section, we have reviewed future research areas for 5G IoT security.

Characteristics Synthesis

The 5G framework is a synthesis of many technologies (Mohammadi et al. 2018).

A combination of MIMO, mmWave, and NOMA increases the spectrum efficiency.
An IoT environment with low-cost and low-power different virtual channel models
can help build a powerful Access Point. It can help distinguish between multiple
users which can help prevent attacks like pilot contamination attacks. Researching
the field of synthesis characteristics of 5G IoT can be a boost to novel solutions.

Signal Revoking

Detection of any active attack in the network is the primary step toward any kind of
countermeasure. It is expected for IoT devices to maintain secure communication
even if it is under any attack. And it is very challenging to eliminate the attacks even
while maintaining contact. Waveform designs (Wang et al. 2018) can be an additional
functionality, and they could be used to recognize if the signals are coming from the
same user. Afterward, a filter mechanism might be developed to filter eavesdroppers
in the network using this technology.

Location Awareness

Location awareness can be a positive factor for removing threats and preventing
threats, as 5G location services can accurately provide location data (Jaitly et al.
2017). Location awareness could help mitigate threats in the network, and there
are many prospective characteristics of the 5G network to attain location aware-
ness (Goyal et al. 2021). And to achieve more efficient communication, location
information is an exciting research direction.
144 A. K. M. Bahalul Haque et al.

Technical Challenges

Many works have been made to mitigate any challenges for the 5G enabled network.
But there are still many technical challenges. There are design-related issues at the
architecture level that includes Scalability and network management, which is a
major issue in managing the state of the information (Fuentes et al. 2013).
Interoperability and heterogeneity allow devices to connect seamlessly, and it is a
major concern as it is used to collect information about smart networks or applications
(Zeng 2015).

Wireless Software Defined Network

Even though WSDN solves the scalability issue in the 5G network, many cases need
to be resolved in SDN. It needs to provide flexibility and separation of control and
data plane, which is the most challenging part of SDNs.

Security Assurance and Privacy Analysis

Next-generation 5G enabled IoT devices, security, and privacy needs to be added to

the network and device levels as they will address many different complex applica-
tions, including smart cities and intelligent networks. 5G is a diverse system, and
security system is very complicated, and security assurance must be considered at
the device and network levels during the design process.

Standardization Issues

As 5G is being developed, it has also enabled to provide many IoT solutions. And
the calibration of IoT will make the implementation of 5G IoT even easier. Lack of
consistency and standardization (Li et al. 2018; Gautam et al. 2019; Rahimi et al.
2018a) has made it a big hurdle and challenge for closing the gap between humans
and environment control. IoT as a service (Haque et al. 2021) might one day be a
possible result.
7 Security Attacks and Countermeasures in 5G Enabled Internet of Things 145

Conclusion

This paper focuses on various security attacks and their countermeasures, the impact
of 5G enabled IoT, and possible solutions for mitigating threats in a 5G enabled
network. We have reviewed 5G and IoT characteristics and physical layer threats. We
also categorized various types of threats with different kinds of attacking purposes.
The open issues for 5G enabled IoT were also discussed, and current and future
research trends were also introduced in the last section of the paper. The development
of 5G enabled IoT devices will open many more gates for the future, bringing in
possible data privacy and security issues. And it is essential to acknowledge what is
associated with 5G enabled IoT and its security and different solutions under the wide
spectrum of the 5G network. The paper’s main aim was to provide a comprehensive
insight into the 5G enabled IoT and threat analysis and discuss the future research
areas. We hope this paper will help further research on the future of 5G enabled
devices.

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Chapter 8
Energy Efficiency and Scalability of 5G
Networks for IoT in Mobile Wireless
Sensor Networks

Smriti Sachan, Rohit Sharma, and Amit Sehgal

Abstract A widespread deployment of 5G technology with the Internet of Things

(IoT) will be there in future years. The implementation of 5G technology perhaps
becomes fortuitous for IoT as IoT has different variants of applications in the field
of tracking data, and security systems. It is also applicable to applications like smart
cities and smart buildings etc. Further, the introduction of the new frequency band in
the present communication system gardened the interest of researchers in the area of
optimization of energy in a mobile environment with dense traffic. This paper aims
to represent the basics of 5G system along with IoT implementations. Also different
techniques for energy efficiency are comparatively analyzed with their pros and cons
for mobile wireless sensor networks.

Keywords Internet of things (IoT) · 5G technology · Energy efficiency · Mobile

wireless sensor networks

Introduction

The new expansion of cellular network reaches to 5G technology. 5G is affected by

several factors like high mobility access in a dense area, lifespan of battery, commu-
nication system, traffic scenarios, low latency, etc. 5G technology also enters to the
industrial sector with the help of IoT technique. As the clients of mobile servers
increases day by day, the apprehension for network issues also arises simultane-
ously. The introduction of IoT systems based on 5G technology resolves various
problems related to network and the combination of these technologies works as
a backbone to the growing economy of the emerging field (Alsharif et al. 2018).

S. Sachan (B) · R. Sharma

Department of Electronics and Communication, SRM Institute of Science and Technology, NCR
Campus, Ghaziabad, India
e-mail: smritisachan@gmail.com
A. Sehgal
School of Engineering and Technology, Sharda University, Greater Noida, India

© The Author(s) 2023 151

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_8
152 S. Sachan et al.

There are various unresolved challenges in the system such as security at the level
of edge devices, large-scale deployments, low latency, communication issues and
network connectivity. The IoT technology made the system more efficient and smart.
It is applied for different areas like transportation system, electricity supply system,
water reservation, smart homes, and the whole smart city. IoT incorporation with
5G improves the quality of service, communication system, network connectivity,
etc. Also the cellular networks associated with IoT contributes toward the growth
of economy of country as well as efficiency of communication system for human
lives (Ericsson Mobility Report 2018; Yaqoob et al. 2019). There is an exponential
increase in mobile subscribers which also enhances the data traffic and the average
utilization of data per user also increases (Sah et al. 2019). The data operators have
to supervise the performance of the whole network and energy efficiency has to be
maintained while retaining the connectivity (Alsharif and Nordin 2017). According
to today’s environmental and economic conditions, the most focused area is energy
efficiency for the operators (Abrol et al. 2018; Gautam et al. 2019). Researchers are
working in the field of “green communication system” nowadays. Carbon neutrality
is a heavily desired feature for network providers based upon energy savings. Also,
due to energy savings, it is a cost effective method to make the system more reliable
and sustainable in terms of the financial aspect of a network (Mowla et al. 2017). The
combination of 5G communication and IoT system addresses some of the crucial
issues of the network which is not only helpful to the particular mobile user but
also to the overall communication system. 5G is not to substitute other running tech-
nologies in a cellular system but to provide stability and improvement in the current
network so that a strong, reliable, sustainable and fast communication system can be
established globally (Hasan et al. 2011).
The above given background shows that there are lots of issues that can be solved
by using the combination of various techniques of 5G, IoT, and MWSN. Also there
are lots of challenges in the field of energy efficiency in 5G communication system.
So, this motivates us to do a review and study of various parameters which is helpful
to understand the accountability of energy efficiency and IoT services in 5G system
for mobile wireless sensor networks. The major contribution of this work can be
summarized as follows.
. Identifying the issue based upon rigorous literature study.
. To make understand the basic concept of energy efficiency, 5G system along with
IoT in MWSN.
. To summarize the concept of energy efficiency, 5G network through its frequency
bands, technologies, IoT-based services, etc. and IoT techniques, its challenges
and security threats, etc.
The remainder of the work is organized as follows. Section “Energy Efficiency in
Mobile Wireless Sensor Networks” discusses the energy efficiency in terms of IoT
in MWSN. Further, the section presents the basics of energy efficiency in MWSN
through its architecture and existing schemes. The evolution of 5G networks, its
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 153

frequency band, security issues, etc. are addressed in third section. Section “IoT
Services Based on 5G” shows the IoT services, challenges, industry supported
techniques followed by the “Conclusion” section.

Energy Efficiency in Mobile Wireless Sensor Networks

One of the most desired features of sensor networks is energy efficiency. The opti-
mization of energy with respect to mobile nodes is a very tough task. The below-given
Fig. 8.1 is of basic architecture of a mobile wireless sensor network. In this type of
network, sensor nodes are mobile, and at every time instance, they change their posi-
tion resulting in frequent connection make-break with the access points. This makes
it really tedious to analyze these kinds of systems.
The evolution of sensor nodes has seen an increase in low-power devices with
reduced power utilization thus making them suitable for power constraint systems.
The sensors work together to form a network known as a wireless sensor network.
They are used for data processing at high-risk zones as well for environmental factors
and many more. The critical issue associated with sensors is that battery lifetime is

Fig. 8.1 Basic architecture

of mobile wireless sensor
network (MWSN)
154 S. Sachan et al.

limited and uninterrupted connectivity must be maintained (Mostafaei et al. 2018).

The energy efficiency is maintained by optimizing power consumption. Also, the
network designing plays a vital role to utilize the resources efficiently (Liu et al.
2018; Feng et al. 2013). The percolation subject is there to learn about network issues
(Wang et al. 2017). The researchers explore the probability of network connectivity
while optimizing the energy consumption. Also the battery life time is enhanced by
reducing the transmit power. In MWSN the major task is to continue communication
with the same energy level as in mobile wireless sensor network at every instance
of time even when the point of contact changes. The modern sensor network is
deployed in association with coverage of the network for ad-hoc networks. Moreover,
the network is divided into smaller cell with single and multiple hops (Naghibi and
Barati 2020). The reinforcement technique is used to observe the natural conditions
to identify the transmission and reception of the signal in the mobile wireless sensor
network. It is validated with the help of simulation (Banerjeea et al. 2020). A selec-
tive method for hoping is used to maximize the lifetime of the network (Kakhandki
et al. 2018). Some of the researchers work on the capacity and cost of the system,
they give a power management system to enhance the capacity of the system which
is cost effective (Sofi and Gupta 2018). The authors of Sofi and Gupta (2018) devel-
oped a scheduling algorithm and energy-based routing protocol for the evaluation
of residual energy. It was also compared with other pre-existing techniques (Buzzi
et al. 2016; Usama and Erol-Kantarci 2019). In Liu and Wu (2019), the authors
presented an algorithm for retaining the connectivity within the network, and some
of the researchers presented the survey on clustering algorithm (Buzzi and Poor
2016; Minoli and Occhiogrosso 2019). The paper proposed a security-based routing
protocol for enhancing the network lifetime (Shafiabadi et al. 2021). Numerous other
published works are available which are based upon energy efficiency, network issue,
connectivity, and network lifetime in which the authors used different techniques to
improve like graph theory, transmission model for node density, etc. (Usama and
Erol-Kantarci 2019; Wang et al. 2019; Kaur and Kaur 2021; Tahiliani and Dizalwar
2018). The below-given Table 8.1 is to show the summary of some of the researchers
work in the field of mobile wireless sensor networks in terms of energy efficiency.

Basic Concept of 5G Technology

The vital development in living style leads to the new developments in commu-
nication techniques. The growing era shows a significant change in communication
traffic volume. Industry observers identified that the role of 5G technology makes the
communication system more advanced and fast as compared to the existing telecom
network (GSMA 2018). The devices in 5G network are connected in such a manner
that it processes through 5G core network via 5G access network. 5G is an expan-
sion of 4G network with new radio features. The 5G core network is implemented to
support IoT system with improvements in network slicing and services in compar-
ison to the 4G network. Also, the 3GPP (3rd Generation Partnership Project) base
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 155

Table 8.1 Review on existing energy efficiency schemes and challenges

Sr. Reference Researchers work Limitations
No.
1 Mostafaei et al. The researchers proposed a dynamic The technique can be
(2018) energy-efficient routing protocol (DEER) combined with energy
to enhance the network lifetime harvesting to improvise
2 Liu et al. (2018) The paper presents a comparative Real-time data processing,
analysis of different energy efficiency security analysis, and
protocols reliability should be
considered
3 Feng et al. The researchers proposed an SINR Data dissemination for
(2013) model for connectivity issues communication should be
taken care of
4 Wang et al. The paper focuses on enhancing the Security mechanism to
(2017) lifetime of the network along with load protect from threat in
balancing WSN
5 Naghibi and The authors present algorithms for task Some other parameters can
Barati (2020) scheduling also be included
6 Banarjee et al. The authors present a survey on routing Some more real-time
(2020) protocols applications can be
focused
7 Kakhemdki The paper presents a survey on data The other parameters can
et al. (2018) acquisition and mobility be considered as speed and
security of the network
9 Sofi and Gupta The authors proposed a multi-tier The overall proposed
(2018) network architecture for spectrum system architecture is well
sharing, use of massive MIMO, and defined for design and
solution for hardware changes planning
10 Buzzi et al. The researchers present the concept of The paper presents good
(2016) energy efficiency for 5G technology in network planning issues
terms of energy harvesting, deployment but not addressed any
of the network, traffic density and virtualization technique to
offloading parameters, etc. solve them
11 Usama and The authors present a survey on energy The edge network
Erol-Kantarci efficiency at radio access level problems are not discussed
(2019) in the paper for energy
efficiency
12 Liu and Wu The paper presents an energy balancing More description of power
(2019) scheme to reduce the power consumption line cables can be added
14 Buzzi and Poor The paper shows a survey on designing a Interference and
(2016) 5G network Randomness can also be
included as parameters for
challenges
16 Minoli and The paper reviews the practical aspect of Overall coverage of topics
Occhiogrosso the 5G network is good
(2019)
(continued)
156 S. Sachan et al.

Table 8.1 (continued)

Sr. Reference Researchers work Limitations
No.
17 Shafiabadi et al. The researchers have a given a new The overall energy
(2021) routing protocol for energy management optimization is done but
the latency constraint is
not managed
18 Usama and The paper presents a survey of various The SINR ratio is shown
Erol-Kantarci issues present in 5G networks by 1 bit/joule notion
(2019)
19 Wang et al. The major focus of the paper is to show Simulation can also be
(2019) the balancing of network connectivity done
with communication
20 Kaur and Kaur The authors present a survey on network Some comparisons can be
(2021) lifetime, energy optimization, and energy shown
efficiency
21 Zanaj et al. A survey of IoT associated with Use of Optimal Relay
(2021) LPWANS and LPSANS is presented by Selection
researchers
22 Usama and Paper surveys the recent works on energy Less study of self-learning
Erol-Kantarci efficiency and wireless networks mechanism
(2019)
23 Tahiliani and The trends of IoT technology Practical WSN-based IoT
Dizalwar networks to study
(2018)
24 Yaohua et al. Paper focuses on energy management More techniques can be
(2019) issues in 5G shown to optimize the EE
network
25 Bakht et al. The paper focuses on power Energy harvesting can be
(2019) transmission, wireless information used in more efficient
transfer, and routing techniques manner

stations are included in 5G access network. The important aspects of 4G systems

like low-power system, low latency, and energy optimization in NB-IoT are used in
5G systems. The slicing of network allows users to use network as a service and the
wireless traffic is enhanced by using these services like virtual services, augmented
reality, IoT techniques, entertainment with high resolution, etc. The implementers
of network constructed the design in such a way that it covers all the remote areas
as well as the urban one. The signals spread all over the network uniformly, in many
countries the transition in cellular network is used by new generation as compared to
the former technology used. Therefore, the 5G is evolved with the new era and it is
faster than existing 4G networks (Goyal et al. 2021; Ahmad et al. 2020). The funda-
mentals of 5G networks are laid down in some of the advanced countries and but the
technology used before 5G like 4G/LTE is also existing still there. The realization
of the 5G with various situations is described in Table 8.2.
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 157

Table 8.2 Realization of 5G technology for different cases

Sr. Parameters Accomplished network Precis
No.
1 Movability – Basics of 5G and industry 4.0 (Rao and
Prasad 2018)
2 Mobile edge Demonstration in physical Robotic arm control on MEC (Tsokalo et al.
computing world 2019)
3 Compatibility Demonstration in physical Industrial application of 5G in real world
world (Voigtländer et al. 2018)
4 Slicing of Simulation analysis Simulation and framework of autonomous
network vehicles based on 5G (Chekired et al. 2019)
5 Reliability Network infrastructure Manufacturing system based on 5G
communication (Karrenbauer et al. 2019)
6 Slicing of Network architecture Network slicing for industry 4.0 in 5G
network system (Taleb et al. 2019)
7 Compatibility Measurement and Distributed control system for robotics
demonstration in physical (Voigtländer et al. 2017)
world
8 Network Framework prototyping Demonstration of smart manufacturing
function through NFV (Peuster et al. 2019)
virtualization
9 Mobile edge System infrastructure IoT smart manufacturing in 5G system
computing (Cheng et al. 2018)
10 – – Construction management IoT (Reja and
Varghese 2019)
11 mmWave None Localization using 5G (Lu et al. 2018)
12 Network Network architecture Prototype of smart production through NVF
function (Schneider et al. 2019)
virtualization
13 Reliability System architecture Palpable internet for 5G system (Tsokalo
et al. 2019)
14 Slicing of Network infrastructure Tele surgery description for 5G (Ahmad
network et al. 2017)

Evolution of 5G

5G network is the enhancement of 4G technology and has a new radio access that is
also known as 5G “New Radio (NR)”. The new network of 5G system has different
features like controlling of user pane, virtualization of network, slicing of network,
low latency, and high speed data (Priyadarshini et al. 2021a). The designing of 5G NR
model is made in such a way that it should be compatible with existing LTE system.
The configuration of new 5G NR system is dual frequency in nature. This is the reason
it is compatible with LTE system and also with narrow band IoT. It might be happened
that different elements have to be inserted in 5G system with different access, to do
158 S. Sachan et al.

Table 8.3 5G technology security threats (Dhiman and Sharma 2021)

Sr. No. Elements of network Security issues Particulars that are effected
Medium Seclusion Analogy of cloud
1 Identity of client IMSI attack Yes Yes No
2 Location of client Data access Yes Yes No
attack
3 Controlled Service denial No No Yes
centralized elements attack
4 SDN controller Hijacking No No No
5 Core element of 5G Signaling Yes No Yes
6 Cloud resource Stealing of No No Yes
sharing resources
7 Routers Attack on No No No
configuration
8 Data base of users Clients identity No Yes Yes
tampering
9 Switch TCP attack Yes Yes No
communication
10 Decrypted medium Key exposure Yes No No

this basically two techniques are in fashion right now, i.e., non-standalone (NSA)
and standalone (SA). The standalone technique contains all the core part of 5G radio
access and the Non-standalone technique uses dual frequency mode to access with
existing LTE packet core. The network deployment in different parts of countries
may take a long time might be a decade as every area has its own situation and
migration configuration will vary according to the various situations of the different
areas (Dhiman and Sharma 2021). The implementation of IoT requires information
related to 5G features and new radio access. The service provider continues with LTE
and existing network features along with 5G NR to access the IoT-based network.
The 5G network also supports 28 GHz millimeter-wave (mmWave) spectrum. Also
the system is threatened by various types of threats that can damage the network in
various ways. The effect of threats in a 5G network is discussed in Table 8.3 with
respect to the different elements of the system (Varga et al. 2017; Arora et al. 2020).

Frequency Band of 5G System

The spectrum of 5G spreads over multiple frequency bands starting from sub-GHz
to millimeter-wave. The sub-bands are categorized into three macro groups, i.e.,
1, 1–6, and above 6 GHz. 5G spectrum very well uses the millimeter waves for
a higher data rate. The 5G generation has more bandwidth and higher frequency
rate. Earlier without modified MIMO antenna only around 10 bits per hertz was the
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 159

channel bandwidth (Sachan et al. 2021). Now the adaption of new radio techniques
like D2D, massive MIMO, ultra-dense networks, etc. enhances the data rate of the
new mobile generation and IoT environment. The mm-wave spectrum is from 30 to
300 GHz and also called as extremely high frequency (EHF). The 5G network mostly
uses 3.5 GHz, mm frequency bands ranging from 30 to 73 GHz, also some other
frequency bands. To fulfill the scarcity of spectrum the service provider has to use
the spectrum effectively and the utilization of smaller cell has to be keenly observed.
The 5G technology is about to use beam-tracking and beam forming while the cell
antenna is focused on the signal when device is tracked when in mobility (Ghanem
et al. 2021). The throughput and directivity are optimized by using the beamforming
technique as it uses a huge number of antennas to access the signal.

5G Innovative Technologies

. Millimeter Waves: These are used for shorter distance and has a range of 30 GHz
to 300Ghz. These are used for smaller cells at shorter distance.
. Massive MIMO: This makes an efficient spectrum utilization as large number of
antennas are connected together to cover huge area. It has less interference due
to beamforming capability which makes it more efficient to use.
. Heterogeneous Network (HetNet): It provides good capacity and coverage with
different technologies.
. Software Define Radio (SDN): It divides the plane as data and control to provide
the high speed network. It manages the network more efficiently to do the further
processing.
. Network Functions Virtualization (NFV): It transfers the functions to virtual
networks like servers, switches, hardware, etc., this is efficient as it full the require-
ment of hardware changes also. It decouples the hardware from the system which
enhances the scalability of the network.

5G Network slicing is also one of the important parameters of the 5G system.

It is the type of configuration that allows various networks to work on the same
platform. The network is divided into various slices and each slice is according to
the need of the application. Figure 8.2 shows the 5G network slicing example as per
the applications.

IoT Services Based on 5G

The total revenue of IoT worldwide is expected to increase by 23% by 2025. The
integration of features of both NB-IoT and LTE-M along with 5G enhances the
capability of IoT structure. There are lots of commercial deployments of IoT networks
at the global level. It will be a tough task for operators to meet the data rate requirement
160 S. Sachan et al.

Fig. 8.2 5G network slicing

of the user as per transmission demands. The operators have to work hard on their
service models to enhance the efficiency of the network. The major market (around
70%) of IoT networks will be covered by its platforms, services, and applications
by 2025. Mostly the services which are provided professionally will be enhanced by
25% in the near future by using IoT networks.
Figure 8.3 shows the basic architecture of the internet of things. It shows that
data is sent through the devices via gateway to the cloud. It can be processed there
and clients can use that data. Each and every data is stored in the cloud and one can
process it by deriving it from there only.

IoT Industry Supported by 5G Technology

The modern techniques and application of IoT are supported by 5G technology. In this
section cases of industries with the adoption of modern methodology are discussed
which have the specific role of 5G in it. Table 8.4 summarizes the resistance of
various wireless technologies to the Internet of Things (IoT).

Industry 4.0

5G supports Industry 4.0 for most of the applications and methodologies incorpo-
rated in the industry. In the manufacturing industry, the recent trend is information
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 161

Fig. 8.3 Basic architecture of IoT network

exchange and automation which is known as industry 4.0 (Sachan et al. 2021).
The major area of focus is cyber-physical system, the Internet of things, and cloud
cognitive computing. It creates a smart factory with a modern structure in which
cyber-physical system processes the physical network. The limitations in 3G/4G
are omitted with the help of 5G technology like the huge population of devices,
large energy consumption, more delay and efficacy of wireless network, etc. The
researchers focus on 5G industrial applications for various features like high data
rates, low latency, robustness, etc. The 5G network shows a better option as compared
to the wired network. It also has good industrial applications with heterogeneous data
resources. It has a variety of features that emphasis on energy efficiency, mobility
in network, virtualization and mesh network, etc. The researchers further listed the
communication network features, processing, infrastructure issues, and reference
architecture of Industry 4.0. The distributed robotics capabilities are shown with the
help of 5G technology by the authors. The mobile robots are used to detect critical
issues in real-time 5G scenarios. The communication is set up between mobile robot
and cloud server to process the critical loads. The complexity of NB-IoT perfor-
mance is also calculated with the help of cloud server in harsh environment, also
it has been observed that LTE cannot full fill the requirement of industry (Sachan
et al. 2021). There is various technique that enable LTE system to work under 10 ms
of delay (Priyadarshini et al. 2021b), but at other end in 5G technology the process
can be done under 5 ms of delay with more than 50% of traffic load (Priyadarshini
et al. 2021c; Singh et al. 2021) which shows the capabilities of the 5G technology in
comparison to the existing ones.
162 S. Sachan et al.

Table 8.4 Persistency of wireless technology for IoT (Sachan et al. 2021; Priyadarshini et al.
2021b)
Sr. Technology Key features Accessibility Accessibility in outdoor
No. used in indoor
1 Zigbee • Applications related to Accessibility No Accessibility
home and industry (30–300 feet)
• Less power consumption
and better battery
optimization
• Low data rates
2 Wi-Fi • Different bands Accessibility To some extent
• Indoor IoT usage (mostly (300 feet)
adopted)
• Better functionality
3 Bluetooth • Application in the Accessibility No accessibility
industrial and medical field (30 feet)
• Real-time location
detection usage (for limited
range)
• Less bandwidth
4 Sigfox • Narrowband To some Accessibility (30 miles in
• Less power extent rural and 1–6 miles in
• Less bandwidth urban)
5 LTE-M • Less power modem used Accessibility Accessibility (10–20
• Cellular architecture miles)
deployment
• Used for tracking mobile
objects
• Use 4G-LTE bands below
1 GHz
6 NB-IoT • Licensed spectrum Accessibility Accessibility (20 miles)
• Less power
• Less cost
• Less penetration in
buildings
7 5G • Various bands Accessibility Accessibility (10–15
• Not widely deployed miles)
• Cellular network
architecture
• Cost effective
• Licensed spectrum

Palpable Internet

The palpable Internet performs all the operations of physical interaction while incur-
ring manipulations. The concept of the palpable Internet is to operate with health
care system, smart grids, infrastructure, education, etc. (Sahu et al. 2021). It enables
the technique to control virtual and real platform by humans. It can be controlled
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 163

Table 8.5 Challenges and solutions supported by IoT (Sharma et al. 2021a)
Sr. Urban Pertinency of Reliability/ Solutions supported by IoT
No. challenges 5G latency
1 Storm and Exists Good/ Sensors are placed to detect early fault
flood control average and warning is placed
2 Nature Exists Average/ Environmental parameters are
monitoring average monitored by placing temperature
sensors, humidity sensors, pressure
sensor, etc.
3 Monitoring of Exists Good/ Sensors are placed to identify toxic
pollution average elements generated from factories,
plants, vehicular pollution, etc.
4 Mobility Exists Good/poor System sensors are placed to monitor
management traffic flow, logistics, good
transportation, etc.
5 Power Exists Good/ Infrastructure with smart grid
supporting average solutions
utilities
6 Security of the Exists Good/ Sensors and drones are placed to
network average detect gunshots, biohazards, crowd
monitoring, and plated reading for
license
7 Real estate Exists Average/poor Sensors and drones are placed to
management check building functionality, water
resources, electric meters, smart
parking, etc.
8 Quality of life Exists Average/ Sensors are deployed to detect
average real-time health problems (air
quality), multi-modal infrastructure,
transportation systems, etc.

by human reaction of listening, visualizing, and manual interaction. The need for
palpable Internet is to maintain connectivity in critical situation to achieve user’s
requirements. The basics of 5G features and services are elaborated to deal with
palpable internet (Sharma et al. 2021a). The 5G system architecture is based on
software-based design on the basis of distributed cloud structure. Table 8.5 shows
the different types of issues and their solutions that are presented in the IoT system.

Security of IoT in 5G System

To deal with security concerns is the most critical task in the area of IoT. In today’s
era the security concern is the most important aspect of day-to-day life, so many IoT
devices are placed for residential as well as industrial security. The deployment of
IoT devices is widespread to achieve the solutions for threats and security issues of
164 S. Sachan et al.

Table 8.6 Security menaces in IoT with their alleviation possibility (Sharma et al. 2021b)
Sr. Type of menaces Layer Alleviation
No.
1 Information access common channel application Application Trackable
of client layer Verification,
Multiple client access authentication
Filtering of anti-virus
Design, planning, and
process
2 Spyware Data layer Detection of spyware
Social planning Spreading threat
Enervation awareness
Monitoring of
congestion
3 Bug Network Detection and
Service denial layer encryption
Firewall usage
4 Service denial Physical Use of spread spectrum
technique

the network (Ha et al. 2021). As compared to the conventional network IoT structure
has different security issues, also the IoT devices are low-power device with low
storage capacity that’s why all types of solutions cannot be used for end to end
security issues (Arsh et al. 2021). The network has a heterogeneous system for the
security threats and relates it with cloud servers to process the IoT services (Sharma
et al. 2021b). Table 8.6 summarizes the security issues in IoT with their alleviation
possibility for different layers.

Conclusion

The chapter presents a holistic approach to show the review on energy efficiency
with their challenges in mobile wireless sensor networks. The basics of 5G network
are very well analyzed in the paper in terms of their evolution, technologies, security
issues, and frequency bands. The role of IoT networks w.r.t to 5G technology is also
discussed and shows that it has a very high impact on overall 5G system associated
with IoT and mobile wireless sensor network (MWSN).
8 Energy Efficiency and Scalability of 5G Networks for IoT in Mobile … 165

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Chapter 9
Security Services for Wireless 5G
Internet of Things (IoT) Systems

Ayasha Malik, Veena Parihar, Bharat Bhushan, Rajasekhar Chaganti,

Surbhi Bhatia, and Parma Nand Astya

Abstract The Internet of Things (IoT) is an emerging field that has evolved in
recent past years and tends to have a major effect on our lives in the coming future.
The development of communication techniques is very rapid and tends to achieve
many innovative results. With the invention of 5th Generation mobile networks, i.e.,
5G, it is becoming an exciting and challenging topic of interest in the field of wire-
less communication. 5G networks have the ability to connect millions or billions of
nodes without affecting the quality of throughput and latency. The 5G technology
can develop a truly digital society in which every device may be connected through
the Internet. IoT is an emerging technology in which everything can be connected
and communicated via the Internet, the term everything may include computing
devices, humans, software, platforms, and solutions. The development of this tech-
nology leads to the advent of a number of solutions that are helpful for humankind,
for example, smart retailing creation of smart cities, smart farming, intelligent trans-
port systems, smart eco-systems, etc. While IoT is a revolutionary technology in the
progression of the Internet, it still has some significant challenges for implemen-
tation like ensuring security, performance issues, quality of support and saving of
energy, etc. Furthermore, the paper elaborates on the motivation of combining two

A. Malik
Delhi Technical Campus (DTC), GGSIPU, Greater Noida, India
V. Parihar (B)
KIET Group of Institutions Delhi-NCR, Ghaziabad, India
e-mail: veena2parihar@gmail.com
B. Bhushan · P. N. Astya
Department of Computer Science and Engineering, School of Engineering and Technology (SET),
Sharda University, Greater Noida, India
e-mail: parma.nand@sharda.ac.in
R. Chaganti
University of Texas, San Antonio, USA
S. Bhatia
College of Computer Science and Information Technology, King Faisal University, Hofuf, Saudi
Arabia
e-mail: sbhatia@kfu.edu.sa

© The Author(s) 2023 169

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_9
170 A. Malik et al.

technology together named IoT and 5G for better communication. Additionally, the
paper illustrates the basic architecture of IoT enabling 5G and discussed various
solutions to provide communication. Moreover, the paper also discussed the various
challenges and research gaps of 5G-IoT technology.

Keywords 5G · Internet of Things · Security · Communication · Devices · Data ·

Network · Layer · Challenges · Performance · Application

Introduction

The history of mobile communication systems’ developments aims at meeting the

requirements of humanity. With the passing of time, data rates of mobile commu-
nication have been improved and achieved great results compared to previous ones.
Generations of mobile communications have progressed through five stages, i.e.,
from 1 Generation (1G) to the 5 Generation (5G) which is the current generation
(Zhang et al. 2019). The generations between 1 to 3G have sequentially evolved on
the basis of service qualities and speed factors. In the early stages of the year 2000, the
concept of 4G was invented and it was the first communication generation that was
totally built on the top of the IP packet switching technique (Liyanage et al. 2018).
As the implementation of 4G technology improved over the years, the advantages of
previous times started to convert into drawbacks. Currently, the 4G network’s speed
has turned out to be very low with respect to high latency (Maier and Reisslein 2019).
A present time, kinds of solutions are needed which may connect at the data rates up
to Gbps. In this sequence, the invention of the next-generation network, i.e., 5G in
the duration of the initial 2021s, affected the whole digital world. Particularly, with
the advent of the 5G concept, a new area of research has been generated, called as
Internet of Things (IoT) (Bonfim et al. 2019). IoT technology can be defined as an
integrated solution involving innovative technologies, allowing to connect together
the people, platforms, software, services, devices, etc. via the Internet (Kitanov et al.
2021). According to a study by Cisco, by the year 2035, 1000 billion or more devices
will be linked together through the Internet. Such devices, forming the network of IoT
will be having all the advanced modules of IoT for allowing and implementing the
Device to Device (D2D) communications with one another. The applications of IoT
may be implemented in almost all the dimensions of humanity such as smart grids,
smart farming (Han et al. 2019), self-driven cars (Zhang 2019), healthcare industries
(Sarraf 2019), smart homes, supply chain industries, and many more (Architecture
2019). Moreover, some of the main features of 5G technology enabled with IoT are
shown in Fig. 9.1.
The history of the development of all network generations presents an important
insight that every single generation is evolved for correcting and modifying the draw-
backs of earlier generations and for incorporating some new concepts things that the
past generations could not apply (Malik et al. 2019). In the early stages year 2020, the
concept of IoT has been proposed parallelly with the advent of 5G communication.
The prediction according to a study state that by the year 2020 almost 40 billion nodes
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 171

Fig. 9.1 Features of 5G-IoT

are connected as part of an IoT network (Gautam et al. 2019). A critical challenge
of implementing efficient IoT is the requirement of transferring huge data volumes
among devices, which derives the need of improving the already existing infrastruc-
tures. The IoT technology has transformed the ubiquitous computing by considering
various sensors-based applications (Shahabuddin et al. 2018). A huge amount of
activity has been noted in IoT-based products and also it is expected to grow in the
coming years at a high rate, i.e., billion devices and almost 15 devices on an average
per person by the year 2024 (Shafique et al. 2020). In the past research works, most
of the issues of protocol-level or device level have been resolved and presently the
recent trend involves working on the issues of integration of sensor-based systems
and D2D communications. IoT is becoming a central part of 5G wireless commu-
nication systems as IoT is the integration of various computing and non-computing,
it will form a major part of this 5G network (Chettri and Bera 2020). IoT like D2D
communication with the integration of data analytics may drastically transform the
framework of many industries. With the rise of cloud computing technology inclu-
sive of fog computing and the generation of intelligent devices, there are much more
chances for future innovations in the field of IoT (Wang et al. 2018a). These inno-
vations are motivations for the researchers to study and analyze current research,
develop new frameworks, and incorporate them into the new applications. There are
also various challenges associated with implementing IoT with the integration of 5G
technology (Wang et al. 2018b).
Machine to Machine (M2M) communication plays an important role in the
emerging paradigm of IoT. The integration of IoT and 5G technology may be
extended to design and develop robots, drones, actuators for distributed coordination,
and also the low latency execution tasks (Huang et al. 2018). Though the research
studies also show the various significant challenges of IoT with 5G networks, for
172 A. Malik et al.

example, performance enhancement, quality of service support, security and privacy

concerns, etc. (Malik and Steganography, 2020). Various solutions related to proto-
cols, routing algorithms, architecture, or spectrum have been suggested for solving
the significant problems (Esfahani et al. 2019). Moreover, the key motivation of the
study is as follows:
• The work familiarizes with the inspiration and ideas of implementing 5G tech-
nology in cooperation with IoT technology for providing better communication
and connection between smart devices and sensors.
• The work illustrates the vision of the IoT in 5G, the work also described the basic
architecture of IoT in collaboration with 5G technology.
• The work deliberates the inclusive survey on the various recent communication
technologies for IoT in 5G, where various technologies are explained such as
SigFox, LoRa, and ZigBee.
• The work discussed the various challenges or vulnerable issues as well as
numerous research gaps and future research directions to enable this technology
in other technologies to provide better communication.
The rest of the paper is organized as follows. Section “Inspiration and ideas for
5G based on IoT” elaborates on the details and motivation to enable the IoT with
5G technology for better communication and connection in smart cities. Moreover,
section “Structure of IoT in co-operation with 5G” discussed the architecture of
IoT in cooperation with 5G, where all the five layers are discussed in detail. Here
a table has also explained the use of IoT sensors in different smart areas. Addi-
tionally, section “Current resolutions for communication for IoT in 5G” explains
the recent resolutions for communication and connection for IoT-enabled 5G tech-
nology where BLE, SigFox, IEEE 802.15.4, LoRa, Wi-Fi, ZigBee, and NB-IoT are
explained along with a differencing table. Furthermore, section “Challenges and
Security Vulnerabilities” highlights some challenges and vulnerable concerns of 5G
enabled IoT technology. Moreover, section “Future Research Directions” elaborates
on the various research gaps in numerous technologies and future research directions
in 5G-IoT. Finally, section “Conclusion” devotes itself to the conclusion of the whole
study.

Inspiration and Ideas for 5G Based on IoT

By looking into different challenges associated with 5G integrated with IoT, there is
a deep motivation of providing a thorough study on 5G wireless communication that
enables IoT (Henry et al. 2020). Since a huge number of researchers and communi-
cation industries are involved in researching the field of 5G-IoT, thus it gives us a
kind of motivation and encouragement for providing the latest research perspectives
on IoT. The network and communication technologies are the base of investigation
for providing new insightful direction on 5G-IoT (Yarali 2020). At present, security
is the most important concern in IoT as it is prone to cybercrimes. Thus, IoT has
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 173

huge opportunities and possibilities for research and it also covers all technologies
of 5G relevant to IoT. 5G-IoT is an architecture containing five layers. For enabling
effective communication among devices and resource sharing, a generalized network
framework is to be developed in 5G-IoT. This kind of generalized network may be
able to decrease the cost and the complexity (Bikos and Sklavos 2018). Moreover,
the evaluation of 5G from 1G is shown in Fig. 9.2 where IoT is enabled from 4G
onwards.
In the current era of technology and advancements, the Internet is a vital compo-
nent as communication between any devices or machines can only be established
through it without any interruption from humans. 5G IoT is an immense technology
that is implemented over critical communications and complex network technologies
for example mm-wave technology, 5G New Radio (NR), Multiple Inputs Multiple
Outputs (MIMO), etc. (Slamnik-Kriještorac et al. 2020). 5G technology runs at a very
fast speed in comparison to the existing technologies and also comparatively huge
number of devices can be connected within a network and reliable communication
can be established (3GPP 2018).

Fig. 9.2 Evaluation of 5G-IoT

174 A. Malik et al.

Structure of IoT in Cooperation with 5G

IoT integrated with 5G technology is primarily implemented on of five-layered archi-

tecture. It includes the tasks of data collection, data processing, data analysis, and
information sharing among various devices and communication links. In this type
of architecture, the smart sensors remain connected through the gateway to the low-
powered networks like Low-Power Wide Area Network (LPWAN), LoRa, NB-IoT,
or SigFox that are used to establish long distanced communications (Le et al. 2021).
The main task of this efficient gateway is to collect data from all the devices connected
and transmit this information to base stations through the 5G communication links.
By the use of 5G NR technologies with MM wave communication and effective
numerology selection, the communication links of 5G technology can be designed.
Furthermore, the 5G cellular base station processes the IoT signals that involve the
MIMO antenna with the added capacity of spatial multiplexing and beam creation
(Garro et al. 2020). The MM wave communication technology enables us to transfer
the radio signals on higher frequencies, i.e., more than 6 GHz. There are numerous
applications that can be implemented through 5G NR technologies (Kumar et al.
2019). Furthermore, the basic architecture of IoT in cooperation with 5G is shown
in Fig. 9.3.

Fig. 9.3 Architecture of 5G-IoT

9 Security Services for Wireless 5G Internet of Things (IoT) Systems 175

Sensor Layer

Sensors and other data collection devices create the first layer of the architecture.
This sensor layer can be considered as a physical layer that includes devices, sensors,
and actuators, and connects to the next layer, i.e., network layer. The present era is a
technological era and utilization of the technology is everywhere. With the progres-
sion of electronic devices, semiconductor manufacturing industries, and automation
solutions, the utilization of smart sensors is growing. the combination of sensors
and integrated computing resources is called smart sensors. The smart sensors are
able to communicate in a two-way manner between the network layer and sensors
and analyze data to make useful decisions (https://www.5gradar.com/features/5g-sec
urity-5g-networks-contain-security-flaws-from-day-one). In an IoT architecture, the
first layer accomplishes Machine Type Communication (MTC) and connects with
the upper layer, i.e., network layer. The new smart sensors are more advantageous
than the old conventional sensors like Smart protocols for establishing communi-
cation between devices and sensors, reduced communication through cables, easy
to set up and maintain, flexible connectivity, reduced cost and less power solution,
highly reliable, and effective performance (Anamalamudi et al. 2018). Some of the
famous sensors based on 5G-IoT, which are used in different scenarios of IoT are
shown in Table 9.1.

Network Layer

The second layer of IoT architecture is the network layer which comprises LPWAN
like Sigfox, LoRa alliance, NB-IoT, ZigBee, etc. In 5G communication technology,
the main task of the network layer is to deliver long-range and low-power connec-
tivity for applications of IoT. A various number of connections may be established
for achieving huge and complex connectivity by utilizing LPWAN. LPWAN is the
technology that is mostly used for IoT applications as it provides a wide range of
coverage areas, increased energy efficacy, less power consumption, and higher data
transfer rates (Adame et al. 2019).

Communication Layer

The communication layer may be considered the support system of the architecture
of IoT because its main task is of transferring all the information over all the layers.
The Radio Access Technology (RAT) is used in the communication layer for 5G-IoT
applications. 5G NR technology is an effort of 3GPP for developing a new standard
for the wireless communication technologies of the next generations (Dutta and
Hammad 2020). 5G NR technology is standardized as per the releases 15 and 16
176 A. Malik et al.

Table 9.1 IoT sensors used in various applications

Application-based on IoT (Le et al. 2021; Garro et al. 2020; Kumar Sensor-based on
et al. 2019; https://www.5gradar.com/features/5g-security-5g-networks- 5G-IoT
contain-security-flaws-from-day-one; Anamalamudi et al. 2018; Adame
et al. 2019; Dutta and Hammad 2020; Salem et al. 2022; Fang et al.
2018; Malik and Bhushan 2022; Frustaci et al. 2018)
E-healthcare X-Ray, E-wearable,
Temperature,
Pressure, SpO2,
Ultrasound, ECG,
EEG, BP, Pulse rate,
etc
Public security Location sensor
radars, chemical
operations,
temperature
observation, light
monitoring,
gyroscope
Smart industries Proximity sensor, air
quality sensor, fiber
optic sensor, smoke
sensor
Environment Humidity,
temperature, light,
energy, harmful rays,
chemical, pollution
Smart homes Temperature,
electronics device
operators, LPG
monitoring, robotics
Smart transportation Traffic sensor,
proximity, smart
parking, position
sensor, prior accident
sensor, automatic
driving sensor
E-study 3D presentation,
robotic devices,
sensor-based labs

of 3GPP. 5G NR technology is a subpart of RAT that has a composition of 5G NR

and Long-Term Evolution (LTE) technology. There are two operational categories
of 5G NR technology named 6 GHz and millimeter wave (mm-wave) range. Various
intricate technologies such as NR support IoT with the inclusion of massive MIMO
technology, waveforms, and frame structure. Radio access offers the complexities
and opportunities, both, in the structure of Radio Access Network (RAN) specifically
in IoT platforms like smart farming, smart home, critical services, smart factories,
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 177

health care applications, and many more. 5G NR technology not only eases the
market prospects for small base stations and small cells like picocells or femtocells
but also facilitates the smart sensors for various kinds of IoT applications (Salem
et al. 2022).

Architecture Layer

It is one of the layers of the IoT framework in which the architectures are included
such as Big Data Analytics (BDA), cloud computing technology, etc. The cloud
computing technology is mostly considered for 5G-IoT, as it is one of the trending
technologies of IoT, and is mainly related to the Information Technology (IT) solu-
tions. The system programming may also be embedded in it. The architecture of cloud
technology is deployed with devices like a smartphone, Personal Computers (PC),
host machines, and laptops. The integration of cloud technology with IoT architecture
provides ubiquitous services which can be distributed to the clients with increased
efficacy and less service management. Thus, IoT is a technology that is implemented
with Big Data (BD), and data management is done by cloud computing technology.
It is a kind of interface where all the services such as storage, data servers, authenti-
cation and authorization processes, the user interface for registration and login, are
made available through the cloud (Fang et al. 2018). The cloud computing technology
is categorized into three models which are as follows.
• Infrastructure as a Service (I-a-a-S)—This service is also called hardware as a
service. This kind of service removes the need of installing any kind of hardware
at our ends like server, storage, or computing resources. This service virtually
provides all the services related to the infrastructure that is used to be present at
data centers traditionally like network hardware, storage, maintenance, privacy,
backup and recovery services, security services, and many more.
• Platform as a Service (P-a-a-S)—This kind of service provides hardware and
software services virtually that are required for developing an application. The
service may involve interfaces, development environments, and databases for
maintaining data. Embedded systems that involve programming interfaces can
also be implemented through this service. P-a-a-s frees the developers from the
responsibility of installing and managing software or hardware services and enable
them to focus on the application development only. The service provider maintains
the services and resources.
• Software as Service (S-a-a-S)—S-a-a-S is a kind of software distribution service
that works on the accomplishment of clients’ demands. In this configuration, there
is no need to install any software physically on the system but services related to
the software can be provided to the clients according to their requirements. Also,
the update or addition of new services in the software is installed automatically
without any intervention from clients (Malik and Bhushan 2022).
178 A. Malik et al.

Application Layer

The application layer works as an interface for integrating the network devices with
network configurations. It provides integration of all the devices and information to
the network or Internet. 5G MTC offers a large variety of services and applications. In
future advanced technologies of wireless networks and communication, machines,
and devices will be able to communicate without any intervention from humans
(Frustaci et al. 2018). Nowadays, there is a variety of applications that require high
latency and speed, high data rates, and connectivity of multiple devices.

Current Resolutions for Communication for IoT in 5G

With the advancement of semiconductor industries, electronics devices, and automa-

tion solutions, there is a rise in the development of communication solutions for 5G-
IoT. These solutions are more reliable, smarter, have high data rates, are robust, and
are energy efficient. So as an outcome, various communication technologies having
low-power configurations are proposed for the 5G IoT, for example, LoRa, SigFox.
The research studies state that low-power technologies have some unique charac-
teristics like large coverage area, low-power consumption, higher data rates, energy
efficient, etc., that are 5G-IoT. The following sections contain a discussion about the
currently developed communication solutions for 5G-IoT (Wang et al. 2019).

Bluetooth Low Energy (BLE)

Bluetooth communication is generally implemented on laptops, PCs, cars, keyboards,

wireless mouses, and earphones. This particular protocol is implemented on a
Personal Area Network (PAN) and is able to establish communication between the
devices over a short range of distances, i.e., almost 10 m. On the other hand, a modified
protocol is required to be integrated into IoT as there is a need of decreasing power
consumption to be able the execution with very short-sized batteries. Special Interest
Group (SIG) of Bluetooth technology has inherited BLE technology while launching
Bluetooth 4.0 version for identifying the market scenario (Chen et al. 2018). The BLE
standard is designed with the ability to transfer small data packets periodically unlike
the traditional Bluetooth technology. The key variation between the traditional Blue-
tooth technology and the new BLE technology is visible in the Physical (PHY) layer.
The standard Bluetooth technology offers 79 channels containing 1 MHz bandwidth
whereas the new BLE standard offers 40 channels from which 37 are data channels
and the remaining 3 are advertising channels with 2 MHz bandwidth. In both the
standards, the Radio Frequency (RF) is classified into two categories, i.e., data RF
channels and advertising RF channels. In the first category, data channels are used
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 179

for transferring data among the Bluetooth linked devices whereas the second cate-
gory of a channel is utilized for connection, streaming, and device acquisition. Both
kinds of channels are activated on an unregistered Industrial, Scientific, and Medical
(ISM) frequency band of 2.4 GHz. In traditional Bluetooth standards, the process
of switching varies from Gaussian Frequency Shift Keying (GFSK) to the Phase
Shift Keying (PSK), i.e., 4-PSK and 8-PSK, whereas, in BLE standard, the GFSK
optimization is done. The GFSK generates a less value of Peak-to-Average Power
Ratio (PAPR), which infers lower power consumption due to the power amplification
(Salimibeni et al. 2020).

SigFox

SigFox is a new technology, invented in the year 2010 with the purpose of connecting
low-powered components or devices like smartwatches, meters of electricity, regu-
lators, etc., which are required to be operated continuously at a very low rate of
data. SigFox utilizes the RF band of ISM. It operates on a frequency of 868 MHz
in Europe and at 902 MHz frequency in the US with 100 MHz channel bandwidth.
SigFox signals can be transmitted easily through dense objects. These are called
ultra-narrow band signals that provide low energy consumption. It is also called
LPWAN technology due to the low power and energy requirements implemented in
a single-hop star topology. SigFox technology is utilized for covering huge areas and
reaching underground devices. The cells of SigFox provide a coverage range of about
30–50 km in less crowded areas and about 20–40 km in crowded areas like urban
areas. Thus, conclusively, SigFox is developed for providing a Wide Area Network
(WAN) with low consumption of power. At present, around 72 countries have been
covered by the SigFox IoT system having a population of almost 1.4 billion (Ikpehai
et al. 2019).
There are various applications as described in this section that is built on the
top of SigFox communication. Mazhar et al. (2021) have developed an independent
SigFox sensor node that is capable of collecting data from the sensors and passing
data to the cloud platform for implementing smart agricultural applications. For
enhancing the system, the sensors were designed in such a way, so as to consume
solar energy. The experimental analysis states that the system enables transmitting
data every five minutes even in cloudy conditions. Mroue et al. (2018) work on the
analysis of the SigFox performance under various scales and density situations of
IoT sensors. By the analysis, it is shown that approximately, a maximum of 100
sensors are able to transmit data at the same time moment. The outcomes show that,
with the increasing number of sensors above 100, the performance of the network
may be reduced. This particular study also presents solutions for the performance
improvement of high-density sensors in SigFox. Lavric et al. (2019) developed an
independent Sigfox-sensor-node which is able to transmit the sensor data to the cloud
server directly. The solar cell is used for providing power backup and it is capable
of transferring data in every 5 min in the cloudy weather at a data rate of around
180 A. Malik et al.

5000 lx. This much of high data transmission rate has not been recorded till now for
a completely autonomous setup. For the actual implementation, two sensor nodes
were placed at the vineyard for collecting atmospheric parameters.

IEEE 802.15.4

IEEE 802.15.4 standard involves the specifications for Medium Access Control
(MAC) and PHY layer. The PHY operates in various ISM groups which permit
it to the region of operation. The worldwide standard band capacity is 2.4 GHz but
there are other bands of data rates also exist such as 915 MHz in North America.
IEEE 802.15.4 standard was intended to be developed for PAN. It was primarily used
for the applications of the organizations such as ecological, agricultural, and engi-
neering. If compared to the IEEE 802.11 standard, IEEE 802.15.4 is not prominent
for higher rates of data, and also it does not emphasize on linking of the devices.
Thus, this provides lower data rates for wireless communication for portable, fixed,
or less battery-moving devices. Zigbee can be an example that effectively used IEEE
802.15.4 standard. The major advantage of Zigbee as compared to others is that it
is proficient in working with multi-hop structures and can also perform well under
network failures (Musaddiq et al. 2021). There are seven different categories of
working modes proposed in the IEEE 802.15.4 standard. The primary methods for
lower energy consumption from the IoT point of view are Offset Quadrature Phase
Shift Keying with Direct Sequence Spread Spectrum (OQPSK-DSSS), Differential
Quadrature Phase Shift keying variation with Chirp Spread Spectrum (DQPSK-
CSS), and Gaussian Frequency Shift Keying (GFSK) with non-virtual distribution
(Aboubakar et al. 2020).

LoRa

The LoRa is an emergent and one of the most important LPWAN communiqué
technology. It has the capability to provide connectivity to the energy-constrained
devices that are distributed over a wide area and that too at lower costs. The LoRa
technology makes use of the LPWAN modulation process and unlicensed bands of
frequency such as 433 and 868 MHz in the Europe region, 915 MHz in North America
and Australia region, and 923 MHz for the Asia region. LoRa provides transmissions
over a wide range with low consumption of power. The LoRa technology is operated
on PHY and the protocols like Long Range Wide Area Network (LoRaWAN) operate
over the network layer. It may achieve the data rates between 0.3 and 27 kbps (around)
dependent on the distribution factor. Though, as per the studies, implementing a
flexible LoRa network with a cost-effective feature is still a significant challenge
(Leonardi et al. 2019).
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 181

Ma et al. (2021) proposed and developed an open-access LoRa framework for

IoT. This particular development process involves the design framework and imple-
mentation of hardware for the LoRa gateway which uses open-source codes of LoRa
available on GitHub. The LoRa server can be improved by the utilization of the
messages system to create interaction among different modules for scalability and
flexibility enhancement. The outcomes of this experiment show that there is a signif-
icant improvement in the performance of the LoRa network when compared to the
traditional LoRa. Lee et al. (2018) analyzed the LoRa mesh network for examining
its ability to be applied in urban areas. For this analysis work, 19 LoRa mesh nodes
were installed in a range of 800 × 600 m in the campus area of a university and a
gateway is also installed which performed the data collection at the time interval of
1 min. The results of the experiment disclosed that the average packet delivery ratio
achieved from the mesh LoRa framework is almost 93.19%, while the star topology of
LoRa was able to achieve only 67.9% delivery ratio under the similar circumstances.
The LoRa is considered to be the most effective out of all the LPWAN technologies.
It is capable of establishing robust communications in IoT applications over large
distances with very low-power consumption. This technology is also promising from
the industrial perspective of IoT. Though the LoRa framework also has a limitation
in that it does not provide support for data flows in real time. For overcoming this
limitation, Senewe et al. (Senewe and Suryanegara 2020) designed a new strategy
related to media access, called Real-Time LoRa (RT-LoRa), which aims to provide
real-time support for IoT applications based on LoRa. The experimental outputs state
that RT-LoRa is capable of supporting real-time data flows.

Wi-Fi

Wi-Fi technology is a very well-acknowledged and well-used technology of wireless

communication based on the IEEE 802.11 standard. It is generally used for accessing
the Internet within the range of 100 m. Its operational frequency band is 2.4–5 GHz.
Since Wi-Fi is appropriate for communication within a short range that’s why it is
the right solution for establishing communication in IoT networks. Jiang et al. (2021)
proposed a solution for smart homes IoT applications based on queue management
where access points are linked together through Wi-Fi. The main goal of this work
was to propose an admission control mechanism at the access point of Wi-Fi for
reducing the time of response. The results of the experiment proved that the new
system based on Wi-Fi is extra reliable and stable than the earlier smart home IoT
applications. Qi et al. (2020) invented a new solution based on WIOTAP to propose an
energy-saving communication for Wi-Fi-based IoT systems. This solution works on
an intelligent access point of Wi-Fi. It presented a mechanism for reducing contention
of downlink channel access and delay in queuing process of stations, called downlink
packet scheduling mechanism. The outputs of the experiment stated that the current
system is an improved one from the old one. Thus, the consumption of energy was
improved by 38% whereas delay was enhanced by 41%.
182 A. Malik et al.

The most crucial challenge of IoT is tracking and locating in real-time scenarios.
Systems or applications based on Global Positioning System (GPS) are very well
recognized for the outside surroundings and it is not appropriate for interior scenarios.
Pokhrel et al. (2020) projected a new Wi-Fi signal-based IoT solution to locate and
track interior environments. The work uses a type of message which is built upon
the 802.11–REVmc2 standard of Wi-Fi. To enhance the accuracy and capability of
the positioning system, the time of roundtrip and signal strength are analyzed. The
results of the experimental setup have presented that the current system improvised
the performance and attained the positional accuracy average of 1.43 m for 0.19 s
update time for interior scenarios.

ZigBee

ZigBee is a technology of wireless communication that is based on the IEEE 802.15.4

standard and is operational in the ISM RF bands. It was designed for providing low-
power and low-cost wireless communication for IoT infrastructure. ZigBee tech-
nology is advantageous over other communication techniques related to IoT networks
due to its reduced costs, simplicity of implementation, and flexibility feature. The
ZigBee is able to transmit data over a distance of 100 m at a data rate of 250 kbps,
depending upon environmental and power attributes. ZigBee technology is typically
applicable in the scenarios of lower data rate networks, long-term battery life, and
short-range communication like smart homes, healthcare devices, manufacturing
equipment control, etc. Franco et al. (Franco de Almeida and Leonel Mendes 2018)
developed a MIMO-based ZigBee receiver for controlling jamming attacks in IoT
networks. This work also proposed a learning method to reduce unfamiliar interfer-
ence. The results of the proposed experiment confirmed that the developed system
may provide the jamming mitigation capacity of 26.7 dB on an average in comparison
to the previous version of ZigBee receiver.
Yu et al. (2019) proposed a time-stamp-based security framework for handling
replay attacks for ZigBee. This proposed solution significantly improvises the
consumption of energy. Also, for enhancement of feasibility, this framework makes
use of powered devices for providing energy to the devices that are power constrained
along with the present time-stamp. The framework is designed in such a way that
it is appropriate for all the ZigBee systems. The experimental setup significantly
enhances the handling of replay attacks in IoT networks based on ZigBee. Karie
et al. (2021) proposed a design of smart sensors by combining two different commu-
nication modules, i.e., LoRa and ZigBee for measuring the factors like humidity and
temperature and humidity for the IoT applications. By using transceiver modules
of LoRa or ZigBee, the sensor data is transferred to the central receiver. The real-
world design and experimental statistics represent the advantages of high range and
low-power communication frameworks for the applications of IoT.
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 183

NarrowBand IoT

NarrowBand IoT (NB-IoT) is a new radio technology based on LPWAN which is

invented by the 3GPP group for supporting a huge number of connections, wide
coverage area, lower cost, and low-power consumption in 5G IoT (Chen et al. 2019).
It is an emerging and evolving communication technique for 5G IoT (Migabo et al.
2018). The main focus of NB-IoT is indoor area coverage, less expense, extended
battery life, and massive connections (Loulou et al. 2020; Ghazali et al. 2021). The
utilized bandwidth is narrow, i.e., 200 kHz. For downlink communication, the OFDM
modulation technique is used and SC-FDMA is applied for the uplink communica-
tion. Thanh et al. (2019) proposed an open-source prototype of an NB-IoT network
for 5G IoT applications. This experiment represents a process of utilizing the already
existing module of commercial NB-IoT for transmitting the sensor data through open-
source NB-IoT. Cao et al. (2020) performed the performance evaluation and modified
the NB-IoT protocol for improvement in 5G IoT. The main motive of this work is
to evaluate the “delay metric” by using the stochastic network and to improvise the
NB-IoT protocol by improving the k means clustering algorithm to categorize the
devices and perform a priority-driven scheduling strategy. The outcomes of the exper-
iment showed that the proposed scheduling method for uplink traffic has improved
the performance over the already existing scheduling schema. Goyal et al. (2021)
proposed a methodology for designing the PHY of the NB-IoT device. The main goal
of this work is to present the features, uplink, and downlink scheduling of physical
channels present at the base station and devices of the user end for helping users to
know the specifications of 3GPP without much reading. Hence, a summarized view
of the above-discussed current solution for communication in 5G-IoT is shown in
Table 9.2.

Challenges and Security Vulnerabilities

This particular study highlights the various innovative contributions of 5G IoT in a

variety of fields for serving humanity. Low power technologies are the main support
system for implementing IoT applications commercially. These applications may
be related to various domains like environmental conditions, smart cities, homes,
buildings, and smart farming (Nurlan et al. 2022). The communication technolo-
gies, increasingly applied in IoT applications have various characteristics like low-
power consumption, wide-area coverage, higher data rates, wide frequency bands,
etc. which make these technologies suitable to be applied in IoT architecture. Exam-
ples of these technologies include BLE, ZigBee, IEEE 802.15.4, SigFox, LoRa, and
many more. The goal of these communication services is to establish connectivity
in 5G IoT applications. Although these technologies are useful, there are a variety
of challenges related to implementation as it connects a billion IoT devices. The
Table 9.2 Difference between current solutions for communication in 5G-IoT
184

Technology (Salimibeni et al. 2020; BLE SigFox IEEE LoRa Wi-Fi ZigBee NB-IoT
Ikpehai, et al. 2019; Mazhar et al. 2021; 802.15.4
Mroue et al. 2018; Lavric et al. 2019;
Musaddiq et al. 2021; Aboubakar et al.
2020; Leonardi et al. 2019; Ma et al.
2021; Lee and Ke 2018; Senewe and
Suryanegara 2020; Jiang, et al. 2021; Qi
et al. 2020; Pokhrel et al. 2020; Franco de
Almeida and Leonel Mendes 2018; Yu
et al. July 2019; Karie et al. 2021; Chen
et al. 2019; Migabo et al. 2018; Loulou,
et al. 2020; Ghazali et al. 2021; Thanh
et al. 2019; Cao, et al. 2020; Goyal et al.
2021)
Range 100 m Many Kms 100 m 2–5 km Many Kms < 1 km 1–10 km
Bandwidth 1–10 Mb/s 250/500 kHz 2.4 GHz, 100 Hz 20/40 MHz 2 MHz 200 kHz
2 MHz
Standardization IEEE Collaboration of LR-WPAN LoRa IEEE 802.11 ZigBee alliance 3GPP
802.15.1 ETSI alliance alliance
alliance
Cost Low Medium Low Medium Low Medium Low
Frequency band 2.4 GHz <1 GHz 2.4 GHz <1 GHz <1, 2.4, 902–928 MHz, 700.800,
5 GHz 2.4 GHz 900 MHz
Maximum data rate 1 Mb/s 100 b/s 0.252 Mb/s 18 b/ 1–54 Mb/s 250 kb/s 200 kb/s
s-37.5 kb/s
Power Low Low Low Low Medium Low Low
Spectrum strategy Wideband Ultra narrow band Wideband Wideband Wideband Wideband Wideband
(continued)
A. Malik et al.
Table 9.2 (continued)
Technology (Salimibeni et al. 2020; BLE SigFox IEEE LoRa Wi-Fi ZigBee NB-IoT
Ikpehai, et al. 2019; Mazhar et al. 2021; 802.15.4
Mroue et al. 2018; Lavric et al. 2019;
Musaddiq et al. 2021; Aboubakar et al.
2020; Leonardi et al. 2019; Ma et al.
2021; Lee and Ke 2018; Senewe and
Suryanegara 2020; Jiang, et al. 2021; Qi
et al. 2020; Pokhrel et al. 2020; Franco de
Almeida and Leonel Mendes 2018; Yu
et al. July 2019; Karie et al. 2021; Chen
et al. 2019; Migabo et al. 2018; Loulou,
et al. 2020; Ghazali et al. 2021; Thanh
et al. 2019; Cao, et al. 2020; Goyal et al.
2021)
Modulation GFSK DBPSK O-QPSK, LoRa 256-QAM BPSK, QPSK QPSK
CCK/DSSS
Sensitivity −95 dBm −126 dBm −97 dBm −149 dBm −95 dBm −85 dBm −141 dBm
1-Hop latency 3 ms 2s 1.5/1./20 ms 500 ms NA 140 ms 1 Mb/s
9 Security Services for Wireless 5G Internet of Things (IoT) Systems
185
186 A. Malik et al.

Fig. 9.4 Issues of 5G-IoT that need to be addressed

two key issues are related to energy efficiency and security (Ahmad et al. 2020).
Additionally, Fig. 9.4 shows the various vulnerable concerns in 5G-IoT technology.

Confidentiality and Safety

The main aim of IoT is to establish connectivity between everything. The invention
of IoT infrastructure has formed an open world in which everything is linked via the
Internet. But there are always cons associated with pros. As everything is connected
to the Internet, the nodes or devices are very much prone to security threats and
attacks. Thus, security and privacy issues are the most crucial factors for promoting
the development of IoT infrastructure to be implemented practically (Arora et al.
2020). The security attacks may be injected into multiple layers of IoT architecture.
• Safety for IoT devices: The devices included in IoT infrastructure are of low
computing capacity and huge in numbers and are not appropriate for framing a
robust and secure system. Therefore, the main focus of attackers is to exploit the
weaknesses of the IoT devices.
• Safety for gateway devices: The gateway is a communication interface between
the PHY devices and the higher layers. That’s why it is called the central part of
IoT infrastructure. The attacks like Denial of Service (DoS) or spoofing of data
generally target the gateway device of IoT.
• Safety for edge devices: The technology of edge computing is a core part of the
newly proposed solutions for reducing the response time of services in real-time
IoT. Therefore, securing the edge servers from attacks is a key challenge.
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 187

• Safety for cloud servers: Cloud technology is a probable solution for storing and
analyzing the vast volume of data generated from IoT devices. Thus, ensuring the
security feature of cloud-based servers is also the main challenge (Malik et al.
2018).

Energy Efficacy

Though the applications of IoT are considered to be energy efficient, the energy
consumption of their own is so much. It is assumed from the study results that as
the 5G IoT applications are becoming widespread, the billions of IoT-connected
devices will be operational and transmitting data endlessly at every moment. So as a
consequence, there will be a massive amount of energy consumption and it will also
increase with every passing moment. Therefore, implementing such solutions that
are feasible and energy efficient is a major challenge (Atakora and Chenji 2018).

Future Research Directions

The need for 5G communication technology at present time is to deliver standards

for establishing communication among a huge number of devices over a wide area
for fulfilling the requirements of industrial as well as social applications related to
IoT. For the successful implementation of such technology, it is vital to identify the
technical and practical challenges along with ensuring the QoS (Generation Partner-
ship Project (3GPP 2018). In particular, the section tries to represent some significant
challenges related to 5G IoT and some ideas for future research, which are shown in
Fig. 9.5.

Structure of Network Based on Big Data

The present structure of wireless communication networks is to facilitate data

transmission and establish communication among the devices of the network. For
accessing the key benefits of BD in the field of 5G IoT, the new solutions and frame-
works considering the BD have to be proposed. This new framework is capable of
accommodating a huge volume of data and integrating that BD into the network
very efficiently. This novel solution focuses on ignoring the unexploited data and
processing the desired information at a suitable location (Ijaz et al. 2018). Another
feature of research related to big-data analytics is personalized networking. This
new approach includes Service-Function Chain (SFC) or network partitioning which
supports various services of BD by developing a service concerned networking on
the top of the physical network. Further customization of network partitioning can
188 A. Malik et al.

Fig. 9.5 Research gaps of 5G-IoT

be done according to the requirements of services. To make the most utilization of

network resources, multiple partitioning or SFC should be used. In 5G technology,
SFC should have the capability of identifying variations in the network status and
service needs (Sharma et al. 2021).

5G New Radio (NR): A New Wave-Form Design Consideration

The selection of wave-form is the most crucial part of the design of 5G NR technology.
The primary choice for designing LTE was OFDM but it is not appropriate for 5G
wave-form due to its higher Inter-Channel Interference (ICI), increased Inter Symbol
Interference (ISI), and higher PAPR. So, all these OFDM wave-form limitations can
be taken as challenges for future research in 5G. The primary design characteristic
of the new wave-form must be less latency (<1 ms) for enabling new services. The
feature of low latency is useful for IoT applications and very less latency is utilized for
enhanced Mobile Broadband (eMBB) and crucial communications such as automatic
driving. Another aspect of designing a new wave is applying a cyclic prefix. It can be
used in both modes, normal as well as extended. To design the wave-form, selection
of numerology is considered and it uses dissimilar numeric values. By considering
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 189

all mentioned aspects of the 5G wave-form, different kinds of the wave-form such as
Filter Bank Multi-Carrier (FBMC) or Generalized Frequency Division Multiplexing
(GFDM) can be generated (Khapre et al. 2020; Alfian et al. 2018).

Energy Efficacy

As per the thorough review studies, the key consideration to design and develop 5G
wireless networks is energy consumption. As the 5G technology evolved, a billion
devices are likely to be connected within a single network having various base stations
in comparison to LTE networks. Thus, to accommodate such a huge number of
devices, there is a need of proposing an energy-efficient solution. The first assumption
for overcoming this problem may be to set up a small-cell base station. Such kind of
base station is able to enhance the capacity in the areas of higher density. It is capable
to improvise coverage area, battery life, and data rates by reducing the consumption
of power. Energy efficacy may be attained through the framework as follows.
• Deployment and energy efficacy trade-off: It is important in achieving reduced
consumption of energy and less cost in the proposed network.
• Spectrum and energy efficacy trade-off: It is utilized for balancing the consump-
tion of energy.
• Bandwidth and power trade-off: For a target rate of transmission, it is utilized for
balancing the bandwidth consumption.
• Delay power trade-off: Use of it is for balancing average service delay with respect
to power consumption (Nie et al. 2020; Latif et al. 2019).

Interchange Between Communication, Gathering,

and Computing

5G wireless communication is a kind of diverse communication. The tasks of

collecting data and computing resources should be performed intelligently for
supporting BD in a heterogeneous network of 5G-IoT. Thus, a balancing factor
is important for communication, catching process, and resource computing. For
reducing the cost of storage, the results of computation must be saved on a tempo-
rary basis. For providing optimal resources, the balance between the heterogeneous
resources is essential. The 5G IoT technology has evolved with the vast volume
of data resources. This data is gathered from various resources that generate data
distribution in a non-uniform manner. Thus, for storing, retrieving, and analyzing
this huge volume of data, the proposed solution is cooperative edge catching. For the
processing of data, edge computing technology is needed (Escolar et al. 2021; Yang
et al. 2020).
190 A. Malik et al.

Design of Synchronized Multi-Band, and High-Power

Effective Amplifier

The importance of a multi-band power amplifier is for reducing the cost and size of
the base station in 5G-IoT. It is capable of supporting multi-band frequency signals
concurrently which enables all the functions to execute at the same time (Failed
2020). The most capable amplifiers are concurrent and parallel single-band power
amplifiers. In 5G NR, the MIMO and mmwave are used for communication at the
base station. The linear RF power amplification is important for the consumption
of energy at the base station. The application of a power amplifier is also helpful to
reduce heat dissipation at the base station. Reduction in energy consumption of radio
stations also tends to decrease the environmental input of RAT (Tikhvinskiy et al.
2020).

Conclusion

The most important vision of 5G-IoT technology is to establish communication

among a various number of devices under a similar network. There is a lot of appli-
cation based on 5G wireless communication such as smart farming smart homes,
smart cars, smart city, and smart medical devices that led to the revolution in IoT.
The implementation of such applications requires the establishment of huge connec-
tivity at a higher speed under 5G wireless technology. On the basis of the analysis
of essential components of 5G-IoT, this review study represents a brief discussion
about power-constrained communication technologies. The IoT will be considered
the new future for the world where everything will be linked together by the Internet
like devices, software, people and systems, etc. The invention of 5G-IoT technology
has developed a variety of applications like intelligent farming, smart cities, smart
homes, smart healthcare devices, green energy systems, etc. This study has presented
a complete overview of all the communication techniques of 5G-IoT that are charac-
terized by huge coverage, increased energy efficacy, and low consumption of power.
By concluding the current research work, the aspects related to energy consumption
and security will be an interesting area of future research and will obtain attention
from industry as well as a research perspective. Research in the field of 5G-IoT may
be proven to be a social service in the development of a better world.
9 Security Services for Wireless 5G Internet of Things (IoT) Systems 191

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Chapter 10
Securing the IoT-Based Wireless Sensor
Networks in 5G and Beyond

N. Ambika

Abstract The previous contribution uses the k-means procedure to create clusters.
It converts into a chain route when the threshold content goes beyond the energy
of the devices in the system. The information transmitter fuel includes the power
of the machine circuitry and the magnitude of facts communication and blowout.
The vibrancy helps in communication circuitry. The knowledge packages ship to the
destination. The architecture has two stages. The groups form during the clustering
stage. The Optimal CBR method uses the k-means procedure to construct groups.
It selects the cluster head based on the Euclidean length and device fuel. The verge
posted by the group head to the individual set associates is the characteristic weight
above which the machine transmits the data to the head. When two-thirds of the
devices are lifeless, the instruments use the greedy procedure to construct a chain-
like multiple-hop methodology to reach the base station. A beacon transmission is
sent by the base station to the active devices in the chaining stage (when the energy of
the nodes is lower). The base station creates the path using multiple-hop chain routing
and the greedy technique. The devices send the notification to the base station using
the chain track. The proposed work increases security by 9.67% when transmitting
data and by 11.38% (device getting compromised).

Keywords KNN algorithm · Security · IoT · 5G communication · Hashing ·

Sensor network · Public key

Introduction

Sensors (Ambika 2020, 2021) are tiny devices deployed to accumulate information
from an object of interest. The number of sensor hubs in a detector organization is
higher than the number of devices in an impromptu organization. Sensing element
hubs are inclined to disappointment. The geography of a sensor network changes
much of the time. Sensor hubs fundamentally utilize a transmission correspondence

N. Ambika (B)
Department of Computer Science and Applications, St. Francis College, Bangalore, India
e-mail: Ambika.nagaraj76@gmail.com

© The Author(s) 2023 197

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_10
198 N. Ambika

worldview, while most impromptu organizations depend on the money-to-point inter-

changes. Sensor hubs are restricted in power, computational limits, and memory.
Detector hubs might not have worldwide recognizable proof (ID) given the measure
of upward and countless tiny devices.
Smart sensors with actuators make Internet of Things (Ambika 2019; Dian et al.
2020). The idea of the Internet is to associate PC gadgets changing to a bunch of
associates. It encompasses things of human residing space, like home apparatuses,
machines, transportation, business capacity, products, etc. The quantity in the living
space is more than the quantity of the total populace. Research continues to make
these things speak with one another using the Internet. The correspondence among
these things alludes to the Internet of Things.
The recommendation (Jothikumar et al. 2021) uses a k-means algorithm. It
converts into a chain route when the threshold content goes beyond the energy
of the devices in the system. The information transmitter fuel includes the power
of the machine circuitry and the magnitude of facts communication and blowout.
The vibrancy helps in communication circuitry. The knowledge packages ship to the
destination. The architecture has two stages. The groups form during the clustering
stage. The Optimal CBR method uses the k-means procedure to construct groups.
It selects the cluster head based on the Euclidean length and device fuel. The verge
posted by the group head to the individual set associates is the characteristic weight
above which the machine transmits the data to the head. When two-thirds of the
devices are lifeless, the instruments use the greedy procedure to construct a chain-
like multiple-hop methodology to reach the base station. A beacon transmission is
sent by the base station to the active devices in the chaining stage (when the energy
of the nodes is lower). The base station creates the path using multiple-hop chain
routing and the greedy technique. The devices (Nagaraj 2021) send the notification
to the base station using the chain track.
The suggestion employs a hashing methodology. The base station broadcasts to
the public for every session. The devices generate the hash codes for the sensed data
and create the outcome using the public key. This methodology secures the devices
and data during transmission.
The contribution of the work:
• The devices send the notification to the base station using the chain track. The
suggestion employs a hashing methodology.
• The base station broadcasts to the public for every session.
• The devices develop the hash codes for the sensed information and generate the
outcome using the public key.
• The proposed work increases security by 9.67% when transmitting data and
11.38% details when the device is compromised.
The work is divided into sections. Literature survey is summarized in segment 2.
IoT-based wireless network is detailed in division 3. Different kinds of attacks in IoT
are explained in section “Different Types of Attacks”. Importance of 5G is briefed in
segment 5. Background is discussed in section “Background”. The proposed work
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 199

is detailed in division 7. Analysis of work is detailed in section “Analysis of the

proposal”. The work concludes in section “Conclusion”.

Literature Survey

The following sections briefs the contribution made by various authors. The recom-
mendation (Jothikumar et al. 2021) uses a k-means algorithm. It converts into a chain
route when the threshold content goes beyond the energy of the devices in the system.
The information transmitter fuel includes the power of the machine circuitry and the
magnitude of facts communication and blowout. The vibrancy helps in communica-
tion circuitry. The knowledge packages ship to the destination. The architecture has
two stages. The groups form during the clustering stage. The Optimal CBR method
uses the k-means procedure to construct groups. It selects the cluster head based
on the Euclidean length and device fuel. The verge posted by the group head to
the individual set associates is the characteristic weight above which the machine
transmits the data to the head. The instruments use the greedy procedure to construct
a chain-like multiple-hop methodology to reach the base station When two-thirds
of the devices are lifeless. A beacon transmission is sent by the base station to the
active devices in the chaining stage (when the energy of the nodes is lower). The base
station creates the path using multiple-hop chain routing and the greedy technique.
The devices send the notification to the base station using the chain track.
It is M2M traffic mode (Fu et al. 2018). It further develops traffic adjusting strate-
gies. This model is reasonable in speaking to the current promising mass of gadgets.
3GPP has made a record 3GPP TR 37.868, which gives a way to deal with demon-
strating M2M traffic in the LTE organization. The existing traffic models depict a
fixed arbitrary process. It has a limited time stretch. M2M gadgets produce traffic.
This approach offers two traffic models and double crosses intervals. The first model
portrays the ordinary condition of the organization, where each M2M gadget for
60 s communicates one message. The subsequent model shows the condition of the
expanded network load. This heap prompts the mass enactment of M2M gadgets.
WSN (Fu et al. 2018) can work at 900 MHz/2.4 GHz to help the 5 GHz, recur-
rence groups. The concentrator contains associate WSN pointing to the 5G versatile
interchanges world. The methodology develops the framework execution. It uses the
UAV as a transfer station. SINK UAV BS improves on the framework model. The
concentrator communicates a sign to the BS with one UAV as the hand-off. This
framework model can more readily uncover the connection between the place of the
UAV-based hand-off and the framework energy consumption. The limited transmis-
sion distance between the Base station and the concentrator can limit the sending
force of the concentrator. The ideal flight path is not set in stone by AI.
The organization (Lynggaard and Skouby 2015) contains an assortment of homes
furnished with IoT. It deals with administration like lighting, warming, security, and
theater setups for its users. These IoT gadgets interconnect with the home organiza-
tion, which associates with the web cloud services. The network interconnects the
200 N. Ambika

brilliant homes and interfaces using cloud administrations that consume the enormous
information produced by the home IoT. The IoT gadgets create a tremendous measure
of data to be handled by the city CoT administrations. It contains an assortment of
associated sensor hub bunches where each gathering ends in a sensor end gadget.
These end gadgets speak with a switching hub which thus courses correspondence
through the network.
The gridlock situation (Sachan et al. 2021) is an examination of two D2D corre-
spondence modes. The work distinguishes the hubs with lesser responsibilities from
the previous information saved in the control unit of the base station. The control-
ling unit has every one of the subtleties of commitment in a specific gadget. It very
well may be distinguished what hubs are with a lesser burden. It infers that such
notes are moving next to zero data. The hubs with low loads can be worked at lower
communication power levels all at once and additionally works on the SINR because
of diminished by and large impedance in the framework and further develops the
battery duration of the portable hubs. The encompassing hubs have a lesser burden
at a specific time. There will be proficient correspondence while decreasing the
impedance.
The work (Sekaran et al. 2021) is an integrated spectrum selection and spectrum
access using a greedy and AI-based framework to allow the forthcoming and subse-
quent demands on 5G and beyond to be presented. A fractional Knapsack Greedy-
based strategy is introduced, and Lagrange Hyperplane-based approach is utilized
to realize the AI-based strategies for spectrum selection and spectrum allocation
for IoT-enabled sensor networks. This framework is called Fractional Knapsack and
Lagrange Hyperplane Spectrum Access (FK-LHSA). The First Fractional Knapsack
Multi-band spectrum selection (FKMSS) model is designed along with an energy
consumption model to optimize channel or spectrum throughput. A Lagrange Hyper-
plane (LH) spectrum access model minimizes spectrum access delay and improves
access accuracy. The simulation results show that the proposed FKM and LH model
can effectively reduce the spectrum access delay (along with the improvement of
throughput and spectrum access accuracy).
The proposal (Shin and Kwon 2020) cures security weaknesses in light of the
framework engineering in WSNs for 5G-coordinated IoT. The proposed conspire
parts into five stages. The framework arrangement stage incorporates the statement of
the framework boundaries and entryway and sensor hub enrollment before sending.
The client enrollment stage starts when a client sends a solicitation message for
enlistment to the confirmation server over a secure channel. The user needs to get
to the WSN responsible for the entryway the accompanying advances perform with
the client, verification server, and passage over a public channel. With the assistance
of the verification server, the client and passage commonly validate one another and
lay out a typical meeting key for future correspondence. The client can acquire the
tangible information progressively from the WSN that matches entrance honors. The
key and biometric update stage permits a client to refresh the secret key and biometrics
without connection with the validation server. The messages communicate over a
channel in the entrance honor update stage.
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 201

It is an energy harvest Markovian battery model (Mahmoud et al. 2017) of 100

states, which addresses the fuel of the optional client, and infers the throughput while
considering detecting a power error reap CRWSN. The energy reaps supplementary
client can send bundles on the channel Whenever the pipeline is passive. The auxiliary
client neglects to communicate information when the channel is inactive because
erroneous detection prompts throughput corruption. The throughput improves the
fruitful transmission boundaries. The conditions address a restricted battery limit. It
is a non-complete M/M/1 model of the energy gather CR-WSN model because of
the inconsistent worth of the primary state. The progress of the states relies upon the
entrance likelihood. It views that there is general information at the optional client to
send. The range states change in light of the traffic of the essential client. The range
state stays unaltered by involving a client with probability or travels to sit with the
likelihood in the past schedule opening.

IoT-Based Wireless Networks

The IoT is reconciliation and correspondence between clever devices. IoT’s incompa-
rability contributes to new advancements and applications. Such detectors and actu-
ators collaborate with different handsets, microcontroller gadgets, and conventions
for the correspondence of control and sensor information. Such constant modules
communicate detected information to the unified storehouses. In contrast with tradi-
tional wired or remote systems administration frameworks, the highlights of IoT
using remote advances are unique as the number of specialized gadgets is very high.
Figure 10.2 is the representation of the same.

Different Types of Attacks

The following are the different security concerns:

(Fig. 10.1).

Perception Layer

• Eavesdropping (Khattak et al. 2019)—Assailants can sniff the traffic produced

by IoT information stream to accumulate client’s data by setting up comparable
IoT gadgets.
• Malicious Data Injection (Alromih et al. 2018)—Bogus sensor information infu-
sion is a type of assault where the sensor information utilized in IoT applications
is produced or altered for malevolent purposes.
202 N. Ambika

Fig. 10.1 Taxonomy of threats in IoT (Krishna et al. 2021)

Fig. 10.2 K-nearest Neighbor algorithm (Pacheco et al. 2021)

• Sybil Attack (Mishra et al. 2018)—The noxious hubs in this can have numerous
personalities of a veritable hub by either imitating it or with a phony character
through duplication.
• Disclosure of Critical Information (Zhang Et Al. 2017)—Sensors utilized in IoT
devices can reveal delicate data, for example, passwords, secret keys, charge
card certifications, etc. These subtleties disregard client security or fabricate an
information base for future assaults.
• Side-Channel Attacks (Kumar et al. 2017)—The aggressor assembles data and
plays out the figuring out cycle to gather the encryption accreditations of an IoT
gadget while the encryption interaction is in progress. This data is not gathered
from plaintext or ciphertext during the encryption cycle. Side-channel goes after
the utilization of information to gain the key the gadget utilizes.
• Malicious Data Injection (Alromih et al. 2018)—Assailants exploit defects in
correspondence conventions to embed information into the organization. The
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 203

gateway will mess with the data expected to control the gadget on the off chance.
The infusion assault might bring about code execution or framework control from
a remote place.
• Node cloning (Khattak et al. 2019)—For unapproved purposes, the gadgets can
be effectively fashioned and recreated. It is called the cloning of hubs.
• Exhaustion attack (Aarika et al. 2020)—Depletion is a spot assault. It is associated
with deactivation attacks. It decreases the size of the organization and eliminates
hubs for all time from the organization.

Abstraction Layer

• Illegal access (Alramadhan and Sha 2017)—The unlawful access and vindictive
difference in information might emerge when handling delicate information.
• Man-in-the-Middle—A framework (Navas et al. 2018) tunes in on rush hour
gridlock between a savvy gadget and an entryway. All traffic steers utilizing the
assailant’s PC using the ARP harming procedure
• Spoofing—To start a caricaturing assault (Mohammadnia and Slimane 2020), an
aggressor can imitate a node. A transmission could record utilizing a convenient
per user.
• Threat to communication protocols (Failed 2017)—OSI layered convention engi-
neering and the actual layer encryption aren’t supported. It requires extra security
techniques in the upper layers.
• Tag cloning—The attack (Dimitriou 2005) can mimic.
• Denial-of-Service (DoS)—It is a kind of assault (Liang et al. 2016) where a gadget
or application is malevolently denied typical activity.
• DDoS—Any IoT gadget, organization, or programming system could be closed
somewhere around a disseminated forswearing of administration (DoS) assault
(Zhang and Green 2015), delivering the assistance out of reach to its shoppers.
• Traffic analysis—Invaders (Hafeez et al. 2019) distinguish the base station, close
by hubs, or bunch heads to uphold forswearing of administration assault or bundle
listening in.
• Sleep deprivation—The forswearing of a rest assault (Brun et al. 2018) on a
battery-fueled gadget will bring about energy consumption.

Network Layer

• Hello flood (Srinivas and Manivannan 2020)—The hubs in the organization

decipher a welcome message coming from the inside and imprint it as a
correspondence course.
• Sinkhole (Pundir et al. 2020)—By utilizing this methodology, an aggressor
compromises an organization’s focal hub and supersedes to deliver it inaccessible.
204 N. Ambika

• Blackhole—Assuming that the noxious hub encounters a Blackhole assault

(Sahay et al. 2018), it will drop all bundles experienced.
• Traffic Analysis—The assailant investigates the traffic and saves a duplicate for
later use in this assault.
• Wormhole—This organization’s assault (Pongle and Chavan 2015) would catch
traffic in one area and divert it to another.
• Selective forwarding—An aggressor dispatches an assault (Hariri et al. 2019) by
entering an organization and dropping parcels.
• RPL exploit (Airehrour et al. 2019)—The angry hubs can try to divert ways when
information is moved.
• Transport Layer
• Desynchronization—Desynchronizing the transmissions between two hubs
permits an aggressor to break real connections between them
• Session hijacking (Humaira et al. 2020)—The aggressor takes the meeting ID and
professes to be the genuine client to assume control over a client’s Internet-based
meeting

Computing Layer

• Malicious Attack (Ahmed et al. 2018)—As laborers in the organization download

vindictive programming programs from the Internet, there is a decent opportunity
for the machine to get hacked. The malware would spread across the organization,
putting the entire organization under its impact.
• SQL injection (Uwagbole et al. 2017)—SQL infusion is a web security blemish
that permits an assailant to interfere with a web application’s data set inquiries.
It allows an aggressor to get to the data that they wouldn’t ordinarily have the
option to recover.
• Illegal Access (Deebak et al. 2019)—If IoT contraptions don’t have an expected
design, the whole organization is harmed. The organizations utilizing cloud-based
registering to need unlimited authority over their organizations, which requires
designing and safeguarding their cloud arrangements on security controls given
by their cloud specialist co-ops.
• Storage Attack—The programmers will dial back the movement of the gadget as
they utilize the distributed storage assets.
• Access Attack—An unapproved individual or foe accesses the IoT network here
of assault.
• Software modification (Wurster et al. 2005)—An IoT gadget can be undermined
by altering its product or firmware by utilizing physical or remote admittance to
make unapproved moves.
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 205

Operation Layer

• Illegal Intervention—Even though cloud specialist co-ops are engaged in deter-

minedly developing APIs and points of interaction, this blast has stretched out
security perils connected with them.
• Unauthorized Access (Failed 2013)—Whenever varied clients can adjust the
plans of various sections of the IoT systems, synchronous execution of arrange-
ment changes and synchronous adjusting of course of action records prompts
unpredictable system status.

Application Layer

• Malicious code—Pernicious codes or focused on malware can undoubtedly take

advantage of the weaknesses of IoT widgets through the Internet, which permits
programmers to think twice about gadgets.
• Software Modification—The assailant will want to reinvent IoT gadgets remotely.
This action could bring about the IoT network being hacked.
• Data tampering (Huang et al. 2021)—During an assault of this kind, the data on
the end gadget is distorted by an assailant.
• Cross-site script (Failed 2006)—It is a procedure aggressors use to embed
vindictive code into a trusted site.
• Identity Thefts—IoT frameworks manage a lot of individual and touchy data. This
information can be taken.
• Virus attack—The target of these assaults is to break the classification of the
framework. The gamble of these assaults is fundamentally higher for cell phones,
sinks, or entryways in IoT organizations.
• Spyware attack (Wazid et al. 2013)—Introduced on IoT gadgets without assent,
spyware is an established program that gathers data.
• Code Injection (Ray and Ligatti 2012)—Assailants ordinarily utilize the most
straightforward method for breaking into a device or organization. Assuming the
gadget is imperiled to resentful contents and confusion, it is the primary mark for
an aggressor.
• Intersection (Berthold and Langos 2002)—Whenever a framework’s trustwor-
thiness is compromised, there is a high gamble of well-being and security
dangers.
• Brute force attack (Knudsen and Robshaw 2011)—A power assault includes delib-
erately trying and speculating each conceivable passphrase or secret key blend to
get sufficiently close to the framework.
206 N. Ambika

Importance of 5G

The Internet of Things (Lee et al. 2017) is a unique paradigm that gives users
access to wireless communication networks and artificial intelligence technology
and is thought to be relevant to a wide range of disciplines and applications. The
development of fifth-generation cellular network technology opens up the possi-
bility of deploying vast sensors in the IoT and processing massive data, testing
communications, and data mining capabilities.
The confluence of the Internet, intelligence, and objects is the 5G IIoT paradigm
(Mavromoustakis 2016). Traditional IoT is a paradigm that integrates large network
connection entities and encompasses the Internet and things. An intelligent individual
combines intellect and objects. It creates high-functioning agents or gadgets to fulfill
complex applications such as object identification.
The advancement of fifth era (5G) networks is more promptly available as a
significant driver of the development of IoT applications. New applications and plans
of action later on IoT require new execution standards such as enormous availability,
security, dependability, the inclusion of remote correspondence, super low idleness,
throughput, super solid, et al. for an immense number of IoT gadgets. The developing
Long-Term Evolution (LTE) and 5G innovations are supposed to give new availability
connection points to the future IoT applications (To meet these prerequisites).

Background

K-Nearest Neighbor (Sun and Huang 2010) is one of the complex Machine Learning
calculations of the Supervised Learning procedure. It accepts the similitude between
the new case/information and accessible cases into the classification. It stores every
one of the accessible information and orders another information point in light of
the comparability. It tends to group into a good suite class by utilizing the KNN
algorithm. It can be utilized for Regression as well concerning Classification. It is a
non-parametric calculation and implies it makes no presumption on the information.
It stores the dataset and plays out an activity on the dataset. KNN calculation at the
preparation stage keeps the dataset. It orders that information into a classification
after getting new information. Figure 10.2 portrays the same. The steps are as follows:
Step-1: Select the number K of the neighbors.
Step-2: Calculate the Euclidean distance of K number of neighbors.
Step-3: Take the K closest neighbors according to the determined Euclidean
distance.
Step-4: Among these k neighbors, count the quantity of the elements in every
classification.
Step-5: Assign the new information focusing on that classification for which the
quantity of the neighbor is most extreme.
Step-6: Our model is prepared.
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 207

Mathematical Explanation for Euclidean Distance

The distance between two points we should subtract the dimensions of each coor-
dinate by each other, sum them all, apply power of two then square root it. Let the
points be A and B. let the coordinates of A be (a1 , a2 ) and B is (b1, b2 )
/
d(A, B) = (b2 − a2 )2 + (b1 − a1 )2
2
(10.1)

Proposed Work

The previous contribution (Jothikumar et al. 2021) uses k-means procedure to create
clusters. It converts into chain route when the threshold content goes beyond the
energy of the devices in the system. The information transmitter fuel includes the
power of the machine circuitry and the magnitude of facts communication and
blowout. The vibrancy helps in communication circuitry. The knowledge packages
ship to the destination. The architecture has two stages. The groups form during the
clustering stage. The Optimal CBR method uses the k-means procedure to construct
groups. It selects the cluster head based on the Euclidean length and devices fuel. The
verge posted by the group head to the individual set associates is the characteristic
weight above which the machine transmits the data to the head. When two-thirds
of the devices are lifeless, the instruments use the greedy procedure to construct a
chain-like multiple-hop methodology to reach the base station. A beacon transmis-
sion is sent by the base station to the active devices in the chaining stage (when the
energy of the nodes is lower). The base station creates the path using multiple-hop
chain routing and the greedy technique. The devices send the notification to the base
station using the chain track.
The contribution is the improvement of the previous suggestion. The dataset is
generated in the trial state. The sink node generates a public key and dispatches it to
the other devices in the network. The devices create the hash code using sensed data.
The code is used with the public key to generate the final outcome. The methodology
secures the data from the hackers. The base station uses KNN algorithm to segregate
the data into groups. The method detects the security breach at an early stage.

Assumptions

• The nodes are assumed to be static by nature. They are deployed to track an object
of interest. The same is communicated to the devices before deployment.
• The IoT device is designated base station.
208 N. Ambika

Table 10.1 Generation of

Step 1—Input the sensed data (16 bits)
hash code
Step 2—Insert ‘0’ bits in even positions (32 bits)
Step 3—Apply right circular shift
Step 4—Divide the bits into two halves
Step 5—Insert the odd position bits of second halve into the
even position of first halve
Step 6—Apply left circular shift
Step 7—Apply random symbol value to the hash code (only
base station has the rights to generate the symbol)

• The nodes are embedded with a set of algorithms and credentials before
deployment.
• The nodes use multi-hop methodology to transmit messages to the base station
(IoT device) or the predestined location.
• The cluster heads communicate with the store nodes after authenticating them-
selves.
• The base station broadcast the public key to its network.

Creating Trial Data Sets

• The nodes after deployment are into trial state, where the trial readings are gathered
from the nodes. This creates the trial dataset. This dataset is stored in the base
station for reference.
• It generates hash code and the same is used along with public key to generate the
final outcome.
• It uses KNN algorithm to classify the data sets into subsets (Table 10.1).

Transmitting the Messages

• The nodes sense the environment and generate the hash code. The public key is
used to generate the final outcome.
• Any new value is recognized at an early stage.
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 209

Analysis of the Proposal

The previous architecture (Jothikumar et al. 2021) has two stages. The groups form
during the clustering stage. The Optimal CBR method uses the k-means procedure
to construct groups. It selects the cluster head based on the Euclidean length and
devices fuel. The verge posted by the group head to the individual set associates is
the characteristic weight above which the machine transmits the data to the head.
When two-thirds of the devices are lifeless, the instruments use the greedy procedure
to construct a chain-like multiple-hop methodology to reach the base station. A
beacon transmission is sent by the base station to the active devices in the chaining
stage (when the energy of the nodes is lower). The base station creates the path
using multiple-hop chain routing and the greedy technique. The devices send the
notification to the base station using the chain track.
A hashing computation (Pieprzyk 1993) is a cryptographic hash work. A numer-
ical calculation maps information (of erratic size) to a hash of proper size. A hash
work calculation intends to be a one-way work, infeasible to modify. Nonetheless,
as of late, a few hashing calculations have been compromised.
A public key (Ambika and Raju 2010) encodes a message with the authenticity of
a computerized signature. It joins a relating private key. It is known exclusively
to its proprietor. Public keys are accessible from a declaration authority, which
issues advanced testaments that demonstrate the proprietor’s character and contain
the proprietor’s public key. Public keys utilize irregular calculations. It matches the
shared key with a related private key. A public key is given to any individual with
whom a singular need to convey, through a private key has a place with the singular it
was made for and isn’t shared. The public key is commonly put away on a public key
foundation server and scrambles information safely before being sent on the web.
The suggestion employs a hashing methodology. The base station broadcasts
public for every session. The devices generate the hash codes for the sensed data and
generate the outcome using the public key. This methodology secures the data in the
devices and data during transmission. The work is simulated using Python. Table
10.2 portrays the simulation parameters used in the proposal. In simulation, we have
considered temperature as the parameter.

Table 10.2 Simulation

Parameters used Description
parameters
Number of temperature variants 15 * 10 groups are created
used
Number of groups formed 15 groups
Length of hash code 32 bits
Length of input (sensed data) 16 bits
Length of public key 16 bits
Total length of the output 256 bits
Simulation time 60 ms
210 N. Ambika

Fig. 10.3 Data security

Security

The IoT is where the Internet meets the actual world. The new aspect of protec-
tion ought to be explored as the going after danger moves from controlling data to
controlling incitation. The worldview makes many worries over the securing infor-
mation, benefits, and, surprisingly, the whole IoT framework. The attributes like
secrecy, uprightness, verification, approval, accessibility, and protection should be
guaranteed for the IoT framework to ensure security in IoT. The confidential data
are necessary to be secured. Hence different kinds of security measures (Varshney
et al. 2019; Sharma et al. 1286) are to be adopted. The proposed work increases
security by 9.67% compared with previous work (Jothikumar et al. 2021). The same
is represented in Fig. 10.3.
The nodes will get compromised, if the devices are not able to defend themselves.
Sensors are cheap devices. Hence protection is a must. The proposal generates hash
codes, followed by the generation of outcome based on the public key. If the adversary
captures the nodes, it will not be able to figure out anything out of it. The data in
the devices are 11.38% secure compared to Jothikumar et al. (2021). Figure 10.4
represents the same.
10 Securing the IoT-Based Wireless Sensor Networks in 5G and Beyond 211

Fig. 10.4 Data secure in nodes

Conclusion

Smart sensors and actuators work together and send data to IoT devices. These
devices communicate over a common platform. The instruments use 5G Internet
facility to communicate with other devices or same/different caliber. The recom-
mendation uses k-means algorithm. It converts into chain route when the threshold
content goes beyond the energy of the devices in the system. The information trans-
mitter fuel includes the power of the machine circuitry and the magnitude of facts
communication and blowout. The vibrancy helps in communication circuitry. The
knowledge packages ship to the destination. The architecture has two stages. The
groups form during the clustering stage. The Optimal CBR method uses the k-means
procedure to construct groups. It selects the cluster head based on the Euclidean
length and devices fuel. The verge posted by the group head to the individual set
associates is the characteristic weight above which the machine transmits the data to
the head. When two-thirds of the devices are lifeless, the instruments use the greedy
procedure to construct a chain-like multiple-hop methodology to reach the base
station. A beacon transmission is sent by the base station to the active devices in the
chaining stage (when the energy of the nodes is lower). The base station creates the
path using multiple-hop chain routing and the greedy technique. The devices send
the notification to the base station using the chain track. The suggestion employs
a hashing methodology. The base station broadcasts public for every session. The
devices generate the hash codes for the sensed data and generate the outcome using
the public key. This methodology secures the data in the devices and data during
transmission. The proposed work increases security by 9.67% when transmitting
data and 11.38% data when the device is compromised.
212 N. Ambika

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Chapter 11
5G and Internet of Things—Integration
Trends, Opportunities, and Future
Research Avenues
A. K. M. Bahalul Haque, Md. Oahiduzzaman Mondol Zihad,
and Md. Rifat Hasan

Abstract The Fifth Generation Communication System (5G) has revolutionized

data (voice, text, and hybrid) transmission and communication. Advanced communi-
cation protocol and sophisticated technology open up the opportunity to integrate 5G
with other state-of-the-art technologies. Similarly, the Internet of Things combines
sensors, actuators, and other devices that network together to collect contextual and
environmental data for application-specific purposes. Nowadays, the applications
of IoT need a fast data transfer to ensure smooth service. 5G has the potential to
achieve this function for IoT. However, the energy-efficient architecture and easy-
to-manage 5G-enabled IoT are still developing. Hence, the potential vulnerability
issues of 5G-enabled IoT architecture need to be studied. In this paper, firstly, we
have comprehensively discussed the fundamental architecture and characteristics of
the 5G ecosystem. Later, the paper comprehensively outlined the characteristics and
layered architecture of the internet of things. Then, this chapter also explores the
requirements of 5G-enabled IoT, Blockchain-based 5G IoT, and 5G with artificial
intelligence. Followed by this discussion, the chapter investigates the opportunities of
5G IoT in different domains. Finally, this paper investigates and analyzes the research
gaps, challenges, and probable solutions comprehensively in a tabular format.

Keywords 5G · IoT · Opportunities · Challenges · Architecture

A. K. M. Bahalul Haque (B)

LUT School of Engineering Science, LUT University, Lappeenranta, Finland
e-mail: Bahalul.haque@lut.fi
Md. Oahiduzzaman Mondol Zihad
Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
e-mail: mondol.zihad@northsouth.edu
Md. Rifat Hasan
Department of Computer Science & Engineering, Fareast International University, Dhaka,
Bangladesh
e-mail: rifat.cse@fiu.edu.bd

© The Author(s) 2023 217

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_11
218 A. K. M. Bahalul Haque et al.

Introduction

New technologies, such as Big data, Blockchain, Communication systems, the

Internet of Things, Virtual Reality, etc., have revolutionized the world (Li et al.
2018a). The Internet of Things (IoT) facilitates information collection using various
devices, exchanges data with the help of communication protocols, and transmits
data from real-life situations for different IoT applications. By 2030, IoT modules
will have connected more than 80 billion people. It creates a massive volume of data
generated from different system nodes. 5G network enables IoT to transfer this data
with higher speed and data quality irrespective of the data structure differences.
The IoT systems for future applications will require enormous capabilities
regarding the data receiving and processing mechanism. 5G can provide the plat-
form to achieve these capabilities. Implementing a 5G network in IoT infrastructure
will pose a new challenge and open many research windows. At the same time, it
will provide unrivaled potential in modern times. 5G network aims to improve draw-
backs of prior communication networks including 2G/3G/4G. It will also add new
capabilities to the mentioned network system forming an architecture for heterogenic
use. 5G IoT applications support a variety of QoS criteria, wide spectrum of wire-
less connections, and large quantities of data traffic. There are various benefits and
drawbacks due to the gap in convergent technologies. The installation value as well
as the growth of accomplishments for these drawbacks restrains this advancement.
5G is configured to improve the speed and security of our connections. So, 5G will
enable modern enterprises and applications due to security, high speed, adaptation
of new technologies and applications with a low-cost deployment.
5G is envisioned as a future cellular network technology with a 99.99999 percent
dependability and a 100Mbps data transfer rate. It can ensure a consistent high-speed
experience for users projecting an estimated one millisecond of round-trip delay. The
three primary sorts of applications that make up 5G are Ultra-Reliable Low Latency
Communication (URLLC), Massive Machine Type Communication (mMTC), and
Enhanced Mobile Broadband (eMBB). Haptic communication and automation inte-
gration are made possible using fixed access bands of sub-GHZ mm-Wave embedded
with tiny Base Stations (BTS) and mm-Wave local nodes. Furthermore, 5G cellular
networks can interface with UAVs in various ways. Blockchain technology, according
to recent research, increases the security, privacy, and transparency spectrums of 5G
(llah et al. 2019). In compliance with blockchain and AI, 5G can change the world
of communication (Haque and Rahman 2020).
The Internet of Objects (IoT) is based on the concept of intercommunica-
tion amongst heterogeneous things that use a variety of communication standards,
stations, sensors, nodes, data centers, and artificial intelligence (AI) capable devices.
As a result, the next generation 5G network will be connected to billions of devices,
resulting in a super IoT infrastructure.
5G integrated IoT is an idea to develop the communication and transmission
process of data in the IoT environment (Arora et al. 2020). Implementation of 5G
in IoT architecture will change tour lifestyle bringing a vast amount of IoT devices
11 5G and Internet of Things—Integration Trends, Opportunities … 219

in one place within a second. It will provide the applications that we believe to
be backdated due to response time as the fastest ones. The 5th generation wireless
system is a driver for modern-day IoT applications. Shortly, the 5G will become
inevitable for the advanced devices used in IoT systems. Even there is a chance of
needing advanced 5G for more advanced applications of IoT like satellite research
or worldwide wireless data transmission (Popovski et al. 2018). Nevertheless, along
with these, there come some issues regarding the architecture. It is still a new concept
and consistently evolving. Getting a low latency in a wide range is not an easy task.
5G itself is developing and has not yet been implemented worldwide. Hence, there
remains a vast domain of security and structural challenges.
This chapter explains the opportunities and challenges of the 5G integrated IoT
ecosystem. The domain can be extended to blockchain and artificial intelligence
perspectives. The outline of the contribution of this work can be briefed as follows:
. The fundamental concept, architecture, and characteristics of the 5G ecosystem
and its evolution are comprehensively discussed.
. Discussed an overview of IoT, along with its characteristics.
. The three-layered architecture for IoT systems has been speculated.
. Requirements to build a 5G integrated IoT ecosystem are outlined.
. Presents a holistic description of 5G-enabled IoT application scope in different
domains.
. Finally, delves deep into analyzing the research gaps, challenges, and tentative
solutions of 5G IoT.
The rest of the paper is structured as follows: section “Overview of 5G”
provides an overview of 5G and its evolution and general architectural requirements.
Section “An Insight into IoT” details the IoT characteristics and the layered architec-
ture. Section “Requirements for 5G Integrated IoT Architecture” illustrates the basics
of the 5G integrated IoT ecosystem in different domains. Section “portunities of 5G
Integrated IoT” illustrates the opportunities of 5G IoT in various domains. Finally,
section “Challenges of 5G Integrated IoT” speculates the challenges of the 5G IoT
ecosystem for future research, followed by the conclusion in section “Conclusion”.

Overview of 5G

5G enables the next generation of mobile networks to make a quantum leap forward
in wireless communication. Day by day, the demands of many applications are
increasing. The rapidly changing wireless technology is constantly trying to keep up
with this evolution. Applications that are dependent on big data and other networks
of multiple things like quick money transfer, detecting critical diseases, inventory
management etc. use the characteristics of 5G to enhance the system increasing effi-
cacy (Ficzere et al. 2021). 4G and prior generations cannot support the latest apps that
require high data processing ability with high quality of service (QoS) and quality
of experience (QoE). The transmission rates are likewise in Gbps, and the typical
220 A. K. M. Bahalul Haque et al.

transmission rate is 100+ (Mbps). The tests show that 5G can give a transmission
rate up to 20 Gbps, which is 100 times faster than 4G. So, introducing 5G in the
network system can change the communication process entirely.

Evolution of 5G

In the 1950s, the first rudimentary portable communications were introduced in the
United States. The first (1G) portable was introduced after three decades. The second
generation of radio communication (2G) was spurred by digital technologies that used
one’s SMS efficiency with the invention of the microprocessor. After a few years, the
GPRS combined with 2G network provides the benefits of sharing voice calls, MMS,
pictures, etc. more smoothly. For improved systems, the third era (3G) was adopted
in the sound and notifying equipment like live TV and fast internet in the twenty-first
century. The Fourth Generation (4G) was developed in response to the excessively
high momentum of online connection in 4G-enabled gadgets. Modern-day commu-
nication exceeds the barrier of mobile phones including iceboxes, computers, auto-
mobiles, and other modern gadgets to the architecture. A fast site and data transfer
feature is necessary to understand ongoing device engagement and access additional
devices. This fact also inspires the innovation of fifth era (5G) of communication.
We can simply put it, 5G is a 5th generation mobile network. Here, we will discuss
three types of 5G in general (Waring 2018).

Low Band 5G

Low-band 5G uses frequencies less than 2 GHz. These are the radio and television
frequencies that have been around the longest. They can cover vast distances, but there
are no particularly large channels. The Low Band 5G indicates the lowest possible
data rate. As a result, 5G with limited bandwidth is sluggish. These channels vary
from 5 to 15 MHz for different cellular networks like AT&T, T-Mobile, and Verizon.
It is considered to be the worst case of 5G, which is somewhat faster than 4G.

Medium Band 5G

Mid-band 5G uses 2.5 GHz and 3.5–3.7 GHz frequencies in most countries. It is
faster than Low-band 5G using less than 6 GHz frequency. It can cover the majority
of frequencies used by the mobile and networks connected to Wi-Fi, as well as
several frequencies slightly above them. Because these networks can cover a radius
of several miles from towers built-in not more than half a mile across, they are the
functioning networks with the highest 5G traffic in most nations.
11 5G and Internet of Things—Integration Trends, Opportunities … 221

High Band 5G

Compared to the 5G mentioned above, short-distance tower-enabled millimeter wave

spectrum brings high-band 5G. The airwaves have mostly been in the 20–100 GHz
band till now. These frequencies have never been used for consumer purposes previ-
ously. They’re only used across short distances. Therefore, running the tests on several
platforms suggests roughly 800 feet away in a dense urban environment from the
towers. However, there are many unoccupied signals, allowing for extremely high
speeds of hopefully up to 800 MHz at a time.

Characteristics and Requirements of 5G Ecosystem

5G can increase its speed up to 20 times than 4G. It is expected to offer 20 GB per
second speed whereas 4G is only promised 1 GB per second. Varying to the network
infrastructure and service operator, the speed can vary. According to Qualcomm,
5G has shown a speed of 4.5 GB per second in its tests and an average of 1.4 GB
per second (Qualcomm 2020). This is at least 20 times improved speed than the
fastest 4G network. Enabling 5G to reach that speed will change the form of HD
streaming, making the ‘download’ button a ‘play’ button. For the high latency, delay
time will be significantly reduced and browsing will faster than ever before. Some
of the significant characteristics of the 5G network are:
. Very low latency (around 1 ms).
. Speed up to 100 Gbps (10–100× than 4G and 4.5G).
. Availability of 99.99% over the world.
. Cover 100% of the places.
. Reduction of energy up to 90%.
. Increase battery life for IoT devices with low power up to 10 year.
To have a 5G network with these characteristics in association with IoT, there
should be some specific capabilities provided to the architecture (Kozma et al. 2019).
Such as:
. Efficient resource management for IoT and bulk operations.
. Prioritize the quality of service and standard control.
. Network slicing and exposure.
. Energy efficacy.
. Application in cyber-physical domain.
. Positioning availability.
High dependability is a fundamental differentiator as compared to non-licensed
radio spectrum designs or traditional, evolutionary-engineered heterogeneous
networks (Bose et al. 2011). So, it is especially critical for 5G.
222 A. K. M. Bahalul Haque et al.

5G Architecture

The network layer, controller layer, management, and service layer are the four levels
of the 5G architecture paradigm. The 5G protocol stack has two sublayers: Radio
Link Control (RLC) and Pocket Data Convergence Protocol (PDCP). Instead of base
stations (BS), 5G’s network architecture uses adaptive, virtual, and flexible radio
access network (RAN) points and a sophisticated dispersed design. To establish
various data access points, these virtual RANs incorporate additional interfaces,
components, and compositions (Ngo 2021). A generic architecture for 5G ecosystem
must have the following function:
. Radio Access Network (RAN): 5G uses RAN to connect many technologies
providing FDD frequency.
. Data Network: It provides operator services third-party services for internet
access.
. Access and Mobility Management: This function ensures integrity protection,
authorizes access, manages mobility, links among devices, connect ability, etc.
. Network Slice Selection: This function decides instances for user equipment and
information for the assistance function.
. Server Authentication Function: It does the work of authentication for trusted and
untrusted 3GPP access.
. Control Policy: This function initiates policy frameworks to control network
behavior.
. Network Exposure: It exposes the network application and manages external and
internal communication securing information.
There are many more functions of 5G architecture varying from application to
application. However, the basic scenario of every function tends to achieve more
speed, low latency, proper management, and security (Haque and Bhushan 2021a).

An Insight into IoT

IoT or the Internet of Things is a networked digital system of various electronic

devices like sensors, activators, receivers, nodes that compute data, etc. By elim-
inating human involvement, IoT devices have transformed the data collecting and
processing system. From top to bottom, IoT devices enhance the development of
concepts like smart home, smart vehicle, smart agriculture (Pranto et al. 2021),
smart health care, communication, cybersecurity and many more systems (Haque
et al. 2021a). They have been used to conduct, monitor, and produce reactions based
on the information gathered. People have been thinking of connecting devices to
the Internet for a long time. The Internet of Things, on the other hand, enhances
and extends network technology based on existing internet technology, allowing
computing and smart objects to connect and communicate with one another. The IoT
11 5G and Internet of Things—Integration Trends, Opportunities … 223

can be broadly defined as any object that communicates, produces, and interchanges
data with other objects via the Internet to perform orientation tracing, tracking, intel-
ligent recognition, and management. This process is conducted by various sensors
or peripherals such as GPS, thermal sensors, RFID, etc. (Yang et al. 2011).

Characteristics of IoT

There are many functional and non-functional IoT needs for creating the infrastruc-
ture. We will discuss some of the most valuable characteristics of IoT here.

Availability

To provide customers with facilities wherever and whenever they need them, IoT
availability must be implemented at the hardware and software levels. The capacity
of IoT systems to give functionality to anybody in any location is referred to as
software availability (Mistry et al. 2020a). The nature of computers that are always
compatible with IoT features and protocols is referred to as hardware availability.
To allow IoT capabilities, protocols like IPv6, 6LoWPAN, RPL, CoAP, and others
need to be implemented inside the restricted devices of the single board resource.
One technique for achieving high IoT service availability is to ensure the availability
of critical hardware and facilities (Bahalul Haque 2019).

Mobility

Although most utilities are designed to be delivered via Smartphone devices, IoT
implementation is hampered by accessibility. A key IoT premise is to keep customers
connected to their preferred resources when moving. When mobile devices are relo-
cated from one gateway to another, service interruptions may occur. Caching and
tunneling for service continuity allow apps to access IoT data even if the internet is
down for a short time. The vast number of smart devices available in IoT systems is
usually included in any solid framework for mobility control.

Scalability

Scalability in the Internet of Things refers to the ability to accept new client equip-
ment, software, and capabilities without compromising the efficiency of existing
systems. It is not straightforward to add new processes and manage extra devices,
especially when there are several hardware platforms and communication proto-
cols to contend with. IoT applications must be built from the ground up to enable
extendable services and operations.
224 A. K. M. Bahalul Haque et al.

Security and Privacy

On diverse networks, such as the Internet of Things, ensuring user security and
privacy is strict. The fundamental functioning of the Internet of Things is built on
data transmission between billions, if not trillions, of Internet-connected items. One
great problem in IoT security left out of the standards is the key distribution between
devices. The growing number of intelligent objects around us with sensitive data
necessitates transparent and simple access control management, such as enabling
one vendor to view the data. In contrast, another controls the device (Bahalul Haque
et al. 2022).

Performance

The performance of IoT services is difficult to evaluate since it is based on the

performance of many components and the underlying technology. The Internet of
Things, like other programs, must constantly develop and expand its offerings in order
to meet user expectations. To give the most value at the lowest cost to customers,
IoT solutions must be monitored and validated. IoT performance may be measured
using various criteria, including processing speed, connection speed, system form
factor, and cost.
IoT also needs to manage the larger amount of information or data created in the
ecosystem, ensuring the interoperability and quality of service.

Layered Architecture of IoT

Various designs have been suggested for IoT worlds. In general, such structures
are divided into three categories. There are three types of architecture: three-layer
architecture, four-layer architecture, and five-layer architecture. In this chapter, we
will look at the three-layered architecture. It is organized keeping mid some specific
tasks to accomplish by the system like executing service functions, transmitting data,
and connection among service devices. It results in three layers, Application layer,
Network/Transmission layer, and Perception/Edge layer.

Application Layer

In different implementations, this layer may include various services. Smart grids,
healthcare, and autonomous automobiles are examples of IoT deployment in smart
cities and homes. Because the application layer might serve as a service support
middleware, a networking standard, or a cloud computing platform, security consid-
erations vary depending on the application’s environment and industry. The applica-
tion layer provides customers with the services they require. The application layer,
11 5G and Internet of Things—Integration Trends, Opportunities … 225

for example, should give temperature and relative humidity values to the client who
has requested the information. This layer is critical for the IoT because it allows for
creating high-quality smart services that fulfill user demands.

Network Layer

Acting as a bridge, the network layer controls data transfer to subsequent layers.
This layer connects to the visual layer. Different smart devices are connected to the
network layer following control function protocol (IEEE 802.x) and authentication
standards (GPS, and Near-Field Connectivity (NFC)). A server backend architecture,
smart devices, and the Internet protocol contribute to this tier. In addition, the network
layer can be handled according to the peculiarities of the deployed environment. The
transmission of data is highly prone to cyber-attacks. Intelligent intrusion detection
key encryption with secured management-based IoT security framework is the most
popular along with the latest adoption of blockchain technology.

Edge Layer

Edge layer manages the IoT devices or sensors like RFID, different actuators,
cameras, intensity detectors, moisture and pressure sensors, etc., using gateways
in a coordinating function to connect with the client or their working domains. Its
main task is to collect data from the environment and transfer them forward for
further processing. IPs like IPv6 or gateways can transmit this to follow protocol
translation and traffic management. The sensors and actuators prohibit a common
and standard security mechanism from protecting these devices. Hence, the inter-
operability among devices and physical accessibility expose a handful of security
threats. Researchers have proposed security solutions for this layer based on machine
learning, multi-stepped authorization, secure channeling through anti-malware, etc.

Requirements for 5G Integrated IoT Architecture

5G-enabled IoT needs special attention for its heterogeneity, advancement, and appli-
cation. However, there are some requirements that all the architecture should follow
(Li et al. 2018b):
. 5G IoT must ensure a low latency of 1 ms considering the sensitive internet system
and medical perspective.
. The architecture must ensure low energy consumption for low-battery life IoT
devices but enough for 5G to transfer data.
. An advanced application like Virtual Reality or Augmented Reality needs a high
speed of 25 Mbps, so the architecture must follow with the future needs.
226 A. K. M. Bahalul Haque et al.

. Security must be top-notch, considering massive data transmission at a very high

speed.
. The devices with mobility factors will get priority for the 5G IoT infrastructure.
The fundamental 5G IoT architecture consists of five steps in general: sensors,
IoT Gateway, 5G-based station, cloud storage, and application (Arsh et al. 2021).
These steps can be comprised in IoT layers to bring up a general 5G IoT architecture.

Edge Layer of 5G IoT

The sensors and gateway of IoT can be comprised of 5G in this layer. For example,
sensors for wearable ECG, temperature, smart manufacturing etc. will use this layer
to transmit and process information using 5G technology (Shdefat et al. 2021).

Network Layer of 5G IoT

The network layer will hold the 5G base station and cloud storage to process data
using IoT devices.

Application Layer of 5G IoT

The application layer will provide all the support for the end system like smart home,
smart supply chain, etc. (Haque et al. 2021b).
Following the above-mentioned general architecture, 5G IoT can support
millimeter-wave (Rahimi et al. 2018), D2D communication, nano-chip, wireless soft-
ware (Huang et al. 2020), mobile edge computing, data analytics cloud computing
(Mudigonda et al. 2020), and many more technologies and application. In Fig. 11.1,
we have shown a generalized architecture for the 5G integrated IoT ecosystem.

Blockchain-Based 5G IoT

Blockchain (Haque and Bhushan 2021b) can bring trust and improved security to 5G
IoT. It can accelerate data exchange at a lower cost by implementing a cryptographic
encryption system to the architecture. The immutability and accountability that
blockchain can ensure for the system are marvelous (Hewa et al. 2020). Blockchain
integrated 5G IoT can bring revolution to industrial IoT, Unmanned Autonomous
Vehicle (UAV), and so on (Haque et al. 2020). Blockchain and 5G IoT can also be
11 5G and Internet of Things—Integration Trends, Opportunities … 227

Fig. 11.1 A general 5G IoT-layered architecture

integrated with deep learning (Kaur and Shalu 2021). The architecture consists of the
device layer, blockchain network, 5G mobile network and cloud network (Satpathy
et al. 2021). It provides a data transmission using a smart contract with a 5G speed.
Again, 5G IoT can be embedded with 5G mm-wave technology to build its processing
center, object processor, sensing regions and application layer (Haque et al. 2021c).
These layers work together using cloud storage and a 5G network to provide services
like education, fire station, transportation, factories, etc.

5G IoT with Artificial Intelligence

Adversarial artificial intelligence can provide great security support towards 5G IoT
(Bohara et al. 2021). It can enable technologies like massive MIMO, cloud RAN,
multi-RAT to prevent security threats like fast gradient sign method, one-pixel attack,
DeepFool etc. The architecture accepts machine learning methodology like logistic
regression, naïve Bayes, Q learning, K-means, Markov decision model etc. (Haque
et al. 2021d).
228 A. K. M. Bahalul Haque et al.

Opportunities of 5G Integrated IoT

5G, is a booming technology that has opened many windows of opportunities. High-
speed and large-bandwidth capabilities will support more than 60,000 connections.
Furthermore, 5G brings all networks together on a single platform. It also gives
subscribers the ability to monitor their accounts and take swift action. 5G is back-
ward compatible with earlier generations of networks. Moreover, 5G is designed to
deliver the globally uninterrupted and constant connection. Enabling 5G with IoT
will accelerate the development in many other sectors including technology, business,
industry, etc.

Technological Advancement

IoT includes many aspects of technology that 5G can make the best use of. 5G
can make these technologies overcome their shortcomings providing remarkable
achievements. Here, we will discuss some technologies that 5G will change forever.

Network Function Virtualization (NFV)

NFV is used to develop network service resiliency and lessen the time it takes to
adopt new systems and technologies. It separates the hardware and software require-
ments for complicated operations (Han et al. 2015). The NFV performs the role of
a virtualization enabler and facilitates the dissemination of 5G-IoT. Virtualized load
balancers, intrusion detection systems, and firewalls are all instances of NFV. Inte-
grating 5G with IoT will allow NFV to detect threats more accurately and provide
network services with flexibility and developed scalability.

Mobile Cloud Computing (MCC)

Cloud computing is a service that allows one to outsource his or her processing
resources (Failed 2020). End-users can get authorized access to databases, datasets,
and information over the Internet, including the ability to analyze and transfer with
the power of 5G networks. Cloud computing is a new fundamental technology in IT
architecture that allows users to compute or store data without building up an exten-
sive infrastructure. The MCC combined cloud computing with mobile computing to
give consumers elasticity and on-demand services. Many services now allow users to
connect their mobile devices to the cloud. Data transmission using edge computing
with 5G decreases the transfer time. Moreover, the inclusion of these technologies
guarantees that context information has reduced latency and is more accessible.
11 5G and Internet of Things—Integration Trends, Opportunities … 229

Device-To-Device Communications (D2D)

5G network enables smooth D2D communication implementing direct communica-

tion without any intermediary (Goyal et al. 2021). There are four types of this D2D
communication, including transmission among devices using the operator-controlled
link, transmission among devices with the device-controlled link, direct D2D
communication with an operator-controlled link and direct D2D communication
with a device-controlled link (Mani Sekhar et al. 2021).

Software-Defined Network (SDN)

SDN opens up new network administration and design (Abdelwahab et al. 2016).
It is emerging as the most promising answer for the Internet’s future. It has two
distinguishing features: data plane separation and advanced application development
programming. This allows for more effective configuration, efficiency, and flexibility
when creating network architectures (Xia 2014). The SDN prototype was used to
create 5G networks in order to retain a flexible and quicker 5G-IoT topology (Xie
et al. 2019).

Millimeter Wave Communication

Mobile networks continuously need increased retention to improve frequency. 5G

mm wave can pave the way for newly developed radio wave frequency. It will enable
IoT to work more efficiently providing high speed (up to 20Gbps) and high avail-
ability accompanied by a mobile network than 4G network. Moreover, the enhanced
broadband in mobile networks due to this 5G evolution will initiate applications like
VR and augmented video, live streaming, UHD video, etc.

Mobile Edge Computing (MEC)

A significant portion of cloud services has been possible because of the development
of several advanced computer applications such as artificial intelligence and smart
environments. Cloud computing has various needs, including low latency, location
awareness, and mobility support. The MEC (Ahmed and Rehmani 2017) can bring
the mentioned operations and resources nearer to the network edge. Because of MEC
in 5G IoT, applications such as VR and AR will grow.
In addition to these technologies, many more are constantly being added to our
day-to-day life all over the world. The network capability and coverage must be
developed to mitigate this global traffic. 5G has the ability to evolve with these new
innovations as well as enhance its ability (Nguyen et al. 2020).
230 A. K. M. Bahalul Haque et al.

Smart Cities

From supply chain management of all the necessary goods and needs of daily life
to home automation system to improved communication, 5G will have a broader
use in smart city programs. Smart Cities will benefit from 5G network with devel-
oped sensors advancing the urban infrastructure. 5G will be able to manage massive
amounts of data and combine a variety of intelligent technologies that are contin-
uously connecting with one another to bring a genuinely linked city even closer
together (Minoli and Occhiogrosso 2019).

Smart Healthcare

Because 5G will have an impact on IoT, it will also have an impact on the areas
touched by IoT. The Internet of Medical Things, or IoMT, is the most important of
them. Rural and other comparable isolated places that lack proper health services
can tremendously benefit from the Internet of Things connectivity. After a long crave
of world-class health services to be remotely achievable like distant operations are
becoming a possibility (Ahad et al. 2020).

Smart Vehicles

Automated cars collect various data on temperature, weather, traffic, GPS position,
and other factors using modern sensors, resulting in a significant volume of data.
A lot of energy is expended in the generation and processing of so much data. To
deliver best services, these vehicles depend largely on real-time data transmission.
The system that is built-in these vehicles can be initiated to collect every kind of
data that are required including the crucial ones with high-speed connectivity and
minimal latency. Eventually, it will enable the vehicles to autonomously monitor its
operation and enhance future models including the system algorithm (Mistry et al.
2020b).

Smart Logistics

Advanced IoT tracking devices that can execute logistical activities will be able to
use 5G connectivity. The real-time data transmission will be faster than ever before
with the efficacy of high speed and low latency of 5G. Moreover, it will be energy
efficient in case of long supply chain that takes time. For example, a consumer may
learn where the fruit is grown, at what temperature it is kept during transit, and when
it is delivered to a retailer (Wang et al. 2020).
11 5G and Internet of Things—Integration Trends, Opportunities … 231

Smart Grid

In day-to-day operations, the need for power is rapidly growing. Demand manage-
ment can be aided by smart grids and virtual power plants. 5G technology is appro-
priate for real-time management in the energy and utility industry providing solutions
that would ensure optimal operations and maintenance by quickly recognizing and
responding to grid faults (Shahinzadeh et al. 2020). 5G can renovate the smart grid
replacing wired technology establishing better deployment flexibility and cheaper
expenditure.

Business

5G-enabled IoT is predicted to deliver more than just technical advancement; it is

also estimated to support 22 million employments globally. The modernization of
transportation, industry, agriculture, and other physical industries is likely to drive
this rise. Areas like construction of oil miners, freight fleets, and railway that need
faster transmission due to the nature of the product will be affected positively (Rong
et al. 2020). 5G has the ability to advance smart manufacturing and intelligent equip-
ment. In near future, 5G will enable IoT to do near-instantaneous network traffic
management, resolution, increase security and public safety, and operate remotely.

Aerial and Satellite Research

The advancement of 5G network also opens the window of vast aerial and satel-
lite communication and research. High Altitude Performance system (HAPs) is
being investigated in accomplice with satellite. Modifying the 5G network with
Narrowband-IoT (NB-IoT) makes it a seamless integration (Gineste et al. 2017).
It can enable moderately structured satellites to communicate at low bitrate. It is
also possible to enhance the 5G mobile network with combined satellite-terrestrial
networks (Fang et al. 2020).

Mitigating Pandemic Situation

5G can renovate the technologies to mitigate issues in pandemic situation. The inte-
gration of 5G and IoT improves the telehealth service to check patients remotely
through massive Machine Type Communication (mMTC). Moreover, 5G-supported
Bluetooth Low Energy (BLE)-based IoT devices can manage COVID-19 patient
detection and monitoring (Haque et al. 2021e). The massive connectivity of data
232 A. K. M. Bahalul Haque et al.

does not need any gateway and provides long-time battery support for low-power
IoT devices (Siriwardhana et al. 2020).

Industrial Usage with Other Technologies

5G-enabled IoT can revamp many industries by developing specific technologies as

well as mitigate some of their core issues. Some of the technologies that will enable
industrial IoT significantly are discussed here.

Blockchain Integrated 5G IoT

5G IoT can be integrated with decentralized blockchain technology to ensure better

privacy and security of data in industrial IoT (IIoT) (Haque et al. 2022). Supply
chain industries that store data on blockchain require real-time data tracking like
timestamp, origin, shipment, amount, etc. (Haque and Bhushan 2021c). 5G-enabled
IoT can ease the traceability ensuring public verifiability for stakeholders. In addition
to this, combined with deep reinforcement learning (DRL), blockchain integrated
with 5G IoT can provide a secure sharing scheme for IIoT (Liu et al. 2019).

Big Data Processing with Machine Learning (ML)

Various machine and deep learning approaches used for big data analysis like viz,
Convolutional Neural Network, Recurrent Neural Network, Deep Neural Network,
etc. need higher optimization and processing time. Along with them, complex math-
ematical models are used that take higher execution time. 5G-enabled IoT can reduce
this time greatly and save energy ensuring efficient industrial resource management
(Dai et al. 2019).

Artificial Intelligence (AI)

To build an AI-based industry, a lot of data are needed to train and test AI algorithm
model. 5G-enabled IoT has the ability to provide the developed infrastructure to
collect this huge amount of data (Kumari et al. 2020). Using this data, AI can generate
valuable insights for the industry as well as give clear context of the network to further
develop it.
11 5G and Internet of Things—Integration Trends, Opportunities … 233

Optimization System

5G-enabled IoT addresses network-related issues in IoT using many procedures

of optimization. These optimization procedures include stochastic, heuristic, and
computational approach along with genetic and evolutionary algorithms, etc. This
aids in the effective monitoring and minimization of IoT device-generated network
traffic.

Video Surveillance

Video surveillance is another application that is projected to operate well in the

5G ecosystem. Many industries as well as private sectors throughout the world are
using surveillance systems. Wired connection is still used by many video surveil-
lance systems today. Wireless communications like Wi-Fi and cellular are gaining
popularity due to their ease, speed, and low cost of implementation. In near future,
it is expected to increase at a higher rate to ensure better security. The utilization of
5G will support the necessary peed boost required for real-time video data analytics
and the propagation of a large number of surveillance (Kumhar and Bhatia 2021).

Challenges of 5G Integrated IoT

5G integrated with IoT has provided us with extraordinary features ubiquitously.

State-of-the-art research has shown that more applications and advancements are
achievable through extensive connectivity ensuring reliable Quality of Service (QoS).
With these advancements, come a lot more challenges that need to be taken care of.
Here, we will discuss some of the crucial challenges of 5G integrated IoT.

General Challenges

5G IoT can be molded with other technologies but posed similar type of challenges
irrespective of their applications.

Low Control on Data Usage and Storage

A massive quantity of data are created in a 5G IoT network. It is created through

devices that are unique to numerous businesses. As a result, these data are frequently
out of the control of all the stakeholders or users concerned (Sinha et al. 2017).
The data can be subject to all suppliers whose equipment creates a network node,
234 A. K. M. Bahalul Haque et al.

all service providers who use a common 5G network architecture, or all users who
share a single cloud platform when used by all parties (Jaitly et al. 2017). As a result,
it is a complex task to track which data come from whom, who is the creator and the
processing system.

Scalability

Cloud-based architecture enables 5G IoT to control and manage the overall network.
To put it another way, the nodes in the network create data for processing to a
common cloud. Then, the network nodes send back the control signals to carry out
tasks like storage reallocation, traffic management, fault management, routing, and
so on. However, as the number of connected devices grows, so does the volume
of data they generate, making scaling up the capacity and computational power of
centralized cloud servers an approaching problem. Furthermore, devices connect to
the cloud nodes via a gateway or an edge node in 5G and IoT ecosystem (Mehbodniya
et al. 2022). Due to the large number of devices attempting to connect to the cloud, the
fronthaul, midhaul, and backhaul networks directly close to gateway nodes frequently
become narrowed reducing the scalability of the overall network.

Complicated Interaction Among Devices

The technological standards embedded in the devices that are used in 5G IoT
ecosystem have varied signaling wave, different data bits, different PHY and MAC
protocols, coding structure, user interfaces, etc. The operation of these devices is
also backed by different operating systems. So, it is a very complicated task to
initiate a mutual communication standard that will be followed throughout the 5G
IoT infrastructure. Again, a common program or operating system that can keep
up to different communication protocol for multiple devices is also very difficult to
introduce. Hence, it can prohibit the extension of certain application and even restrict
some devices to use in 5G IoT environment (Singla et al. 2021).

Data Auditability

Data created in 5G IoT networks have several owners and are extremely noncompli-
ance, making it difficult to trace and audit. There are also situations when there are
no common standards or protocols in place for data as discussed before to be shared
across devices owned by various businesses (Bhushan and Sahoo 2017). Further-
more, data may be non-processable or not transmittable across various divisions of
an organization due to differing communication protocols and also because of trust
difficulties (Bhushan and Sahoo 2019). Similar sorts of data like meteorological and
environmental data are sometimes not being impart or inter-operated by multiple
entities in order to arrive at an agreed reasoning.
11 5G and Internet of Things—Integration Trends, Opportunities … 235

Heterogeneity of 5G and IoT Data

Both 5G and IoT ecosystem has a varied nature causing several compatibility
concerns while implementing distinct applications. Because the data created in IoT
networks and 5G cellular networks are diverse and multidimensional, it is almost
impossible to forecast exact characteristics and outcome. As a result, early opera-
tions like as cleaning, ordering, and preprocessing are required to train this type of
data since the integration of such a diverse set of data leads to incorrect calculation.
Hence, test of datasets in diverse situations needs to give more attention to this aspect
for dataset training and feature selection. Sensors and humans, for example, create
data for IoT networks in smart homes. However, a common or central server must
contain all the data utilized in this application (Palanisamy and Bhatia 2022). This
server will be responsible to pool and train data from many sources so that it can
cope with data variety and achieve higher prediction accuracy.

Blockchain Integrating 5G IoT Issues

Blockchain, integrating with 5G IoT, opens opportunities for many applications.

But many of them come up with different challenges too. Some big challenges for
blockchain integrated 5G IoT ecosystem are discussed here.

Processing Time

A few self-executing tasks like transaction and block verification are required to build
the chain in the blockchain ecosystem. These computations follow some specific
cryptographic procedures to maintain the authenticity of the blocks in blockchain
that takes a lot of time. It can be a solution to lessen the amount of training data
but there are specific computing restrictions in the IoT context that could lead to
security problems. As a result, a big concern for the implementation of blockchain
in the context of IoT is it needs fewer resource-intensive replacements to reduce
processing time.

Privacy and Security

IoT alone brings many privacy issues to the blockchain integrated 5G network for
its vast number of devices using numerous sensors connected worldwide. Moreover,
blockchain prefers to public verification of transaction. Keeping up the encryption
procedure of blockchain along with 5G data protection carries a significant challenge.
There are some studies to overcome this issue proposing homomorphic computation
to cover up the data at the time of access of any user (Zhou et al. 2018). But controlling
236 A. K. M. Bahalul Haque et al.

over 51% of data from 5G IoT ecosystem can lead to reverse transaction or double-
spending problem. So, combined solution is still a big challenge.

Storage Scalability

A major need of blockchain technology is the constant storing of transactions and

blocks. Each node should theoretically have a copy of the ledger that grows in
tandem with the transactions. From a scalability standpoint, the IoT ecosystem’s
storage effect will have an influence on the overall system’s operation. The changing
transactions that come with scaling up the system, in particular, need a lot of storage.

Cost

Blockchain has its own scalability issues, but on the other hand, throughput or cost
is another big difficulty with this technology. For IoT, it is difficult to keep up with
the growing number of transactions and their size. Two more issues that arise often
are latency and transaction throughput. Due to the less data generation of private
blockchain than public blockchain, it is more preferable to IoT environment. But
5G IoT generates big data and the analysis of this huge throughput by blockchain
increases computational cost.

5G mm-Wave Issues

5G mm-wave application has promising significance in increasing robustness of

the IoT ecosystem, low latency, and higher capacity allocation for Multiple Input
Multiple Output (MIMO) technologies. But it has a range limitation in communica-
tion. Moreover, the core of 5G mm-wave is combined with devices that are different
in characteristics like LTE and mm-wave band. Again, it is susceptible to blockage
restricting its mobility for its rich scattering environment. It also consumes a lot
of power due to extensive use of hardware. Hence, disturbance in connection for
weather condition makes it challenging to use with IoT infrastructure (Dang 2022).

Threat Protection of 5G IoT

Even after mitigating issues of 5G and IoT distinctively, security threats will come
again in a 5G integrated IoT infrastructure. There are lots of security threats like DoS
attack, signaling storms, slice theft, penetration attacks, Man-in-the-middle attack
(MITM), TCP level attack, security key exposure reset and IP spoofing attack, etc.
are intended to technologies like SDN, NFV, Cloud server, etc. (Bhushan and Sahoo
11 5G and Internet of Things—Integration Trends, Opportunities … 237

2020). These attacks can not only expose the overall 5G IoT application but also
make a big impact on privacy of data (Ahmad et al. 2020). There are some solutions
to these attacks like usage of low-powered nodes and sensors, cloud and application
security for SDN and NFV, etc. But it is still a concern for future prospective 5G IoT
("5G support for Industrial IoT Applications 2020).
Table 11.1 summarizes and provides a short overview of the prominent challenges
and their possible solutions of 5G integrated IoT system.
Apart from the stated issues, there will come more shortcomings and challenges
in 5G IoT systems and applications. Hence, it will provide future directions for a
large number of domains of research and development.

Conclusion

This paper is focused on the opportunities and challenges of 5G IoT integration in

several domains of application along with their possible solutions. 5G network has
the ability to connect more than 100 billion of devices and exchange data at least
10 times quicker than LTE resulting a game changer in IoT environment. The aim
should be the introduction of scalable, affordable, and efficient network architecture
for 5G IoT connecting vast amounts of devices. The applications of 5G IoT in several
domains to increase efficacy can bring a lot of opportunities along with challenges.
The architectural and security aspects are needed much concern. Furthermore, manu-
facturers must conduct quality and maintenance testing to guarantee the adoption of
future software and hardware to perform their functions under a variety of scenarios
(Li et al. 2018c). 5G IoT can give huge economic benefits to enterprises eager to
embrace this new world when combined with important technologies like cloud secu-
rity, artificial intelligence, and remote computing. In this chapter, we try to analyze
the opportunities of 5G integrated IoT ecosystem for eradicating the shortcomings
of IoT describing the features of 5G. The 5G IoT architecture also shows that there
are many challenges and forthcoming research direction. We hope these illustrations
will provide thorough insights and inspire the development of 5G IoT applications.
238 A. K. M. Bahalul Haque et al.

Table 11.1 Summary of challenges and state-of-the-art solution of 5G IoT

Domain Challenges Description Solution References
General Low control on Different types of A BICM-ID-based Fang et al.
challenges data usage and data are used by NAND flash (2021)
storage different entities memory system
Difficult to track optimize with
down source of EPEXIT algorithm
malicious data to manage data
storage
Scalability Ability and A cost-efficient Okwuibe
computational power SDN with data et al. (2020)
to scale up data nodes optimization in
decreases with the several sectors of
growth of data routes industrial IoT and
Attempt of numerous 5G
data to access cloud
results in narrowed
gateway
Complicated Lack of common A protocol named Jin et al.
interaction operating system oneM2M using (2021)
among devices among interacting ontology
devices
Data auditability Lack of compatibility Kernel bypassing Varga et al.
among entities to using RDMA and (2020)
process, transfer and DPDK
share data
Heterogeneity Data disparity Variation in 5G and Proposed possible Gures et al.
IoT technologies solutions for 5G (2020)
poses extra attention HetNet mobility
to preprocess data management based
Dataset testing and on paging,
feature selection get registration, and
compromised access procedure
AI integrated Bandwidth Requires high Suggested a B5G Hossain et al.
system demand bandwidth to manage framework capable (2020)
complex algorithm of high-bandwidth
functionality
Cost Optimization cost A VNF-optimized Ibrahimpašić
tends to be higher low-cost adaptation et al. (2021)
due to usage of vast for AI operations is
amount of data for proposed
training and testing
Blockchain Processing time Requires fewer Smart Mehta and
integrated resource-intensive 5G contract-based Gupta (2020)
System IoT structure to blockchain
reduce overall integration
processing time
(continued)
11 5G and Internet of Things—Integration Trends, Opportunities … 239

Table 11.1 (continued)

Domain Challenges Description Solution References
Privacy and Encryption process Combined with Rathore et al.
security for 5G data is deep learning, a (2021)
difficult to establish blockchain-based
four-layered
framework is
proposed
Storage Large chain of blocks Utilizing fog Hewa et al.
scalability reduces storage computing and (2020)
scalability cloud
manufacturing
equipment, a
Hyperledger Fabric
framework is
introduced
Throughput Less data generation Blockchain with Hakiri and
is preferable for VNFs following a Dezfouli
private blockchain novel consensus (2021)
but 5G IoT requires algorithm
big data increasing
computational cost
Spectrum 5G mm-Wave Lack of consistency An antenna design Chen (2020)
issue among LTE and is proposed using
5G-enabled devices hybrid beam
Affected by weather forming for mobile
condition transmission
Device Common Scarcity of common A ZigBee-based IoT Wang et al.
standard protocols protocols and system is proposed (2021)
standards irrespective for multi-device
of devices and operation platform
application
Security Cyber-attacks Threatens privacy of A convolutional Anand et al.
threats like TCP level data and exposes neural (2021)
attack, IP overall system network-based
spoofing, etc malware detection
system is introduced
Infrastructure Network Limitations of Multiple frequency Sharma et al.
scalability and hardware and antennas with the (2021)
interoperability advanced technology power of front-end
to combine 5G with radio frequency,
IoT diplexer, and
triplexer are
designed
240 A. K. M. Bahalul Haque et al.

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Chapter 12
Post-Quantum Cryptographic Schemes
for Security Enhancement in 5G
and B5G (Beyond 5G) Cellular Networks

Saurabh Bhatt, Bharat Bhushan, Tanya Srivastava, and V. S. Anoop

Abstract 5G is the fifth generation of broadband cellular network and beyond 5G

can be the 6G, which will be the sixth generation of broadband cellular network.
Even though studies about 5G are still evolving, 6G has become a hot topic for
cellular researchers these days. The expansion in the field of 5G and 6G is still in
infancy stage as many problems still need to be solved. Out of these, security of data
transmission is a premier concern. Therefore, cybersecurity is becoming increasingly
important for these cellular networks. This paper is focused upon providing the in-
depth overview of 5G and B5G networks. The paper aims to evaluate the insights of
the security services of 6G networks and outlines various data security techniques
used by 5G networks. The paper also provides introduction to quantum computing for
cryptography and evaluates various post-quantum cryptography techniques. Finally,
some novel research trends and directions in correlation of security of 5G and beyond
5G networks are listed to guide further research in the area.

Keywords Cellular networks · 5G · Quantum cryptography · Security

S. Bhatt · B. Bhushan (B) · T. Srivastava

Department of Computer Science and Engineering, School of Engineering and Technology,
Sharda University, Greater Noida, India
e-mail: bharat_bhushan1989@yahoo.com
S. Bhatt
e-mail: 2019002150.saurabh@ug.sharda.ac.in
T. Srivastava
e-mail: 2019622455.tanya@ug.sharda.ac.in
V. S. Anoop
Smith School of Business, Queen’s University, Kingston, ON, Canada
e-mail: Anoop.vs@queensu.ca

© The Author(s) 2023 247

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_12
248 S. Bhatt et al.

Introduction

Cellular network also known as mobile network is a type of communication network

in which the to-and-from nodes are connected to each other wirelessly. Cellular
network has played a very important role in the development of humankind and
the evolution of the technologies used nowadays. Moreover, it plays a very signifi-
cant role in day-to-day life of individuals nowadays, from day to day calling to using
social media on cellular networks, it plays a very important role. As per time, cellular
networks have evolved and are still evolving, from 1G to current 4G LTE, advance-
ment towards the commercial rollout of 5G and then beyond 5G. Every successor of
the cellular network generation evolves in such a way that it provides us with a lot
more what its predecessor couldn’t offer. From 4 to 5G, there’s a huge change, from
high data transfer speed to high level security to connectivity between objects and
machines (Sutton 2015). 5G communication has been formally dispatched in July
2020, and the connected 5G industry is relied upon to drive the vivacious advancement
of communication-related parts at home and abroad. At present, the 5G communica-
tion in work has a place with the FR1 frequency band, which is likewise a sub-6 GHz
frequency band (Ahmad et al. 2020). The communication frequency band is around
1 GHz more than that of 4G LTE communication. In any case, the advancement of
the connected communication design has caused incredible contrasts in communica-
tion hardware. 5G being still not commercially out yet, the research and study about
beyond 5G that is 6G has already begun. Many countries have aimed for commer-
cially rolling out 6G technology by year 2030. As expected, the 6G network is to
coordinate the aerial, water and terrestrial communications into a powerful network.
This network would be quicker, solid and could uphold an enormous number of
gadgets with super low latency prerequisites (Akhtar et al. 2020). Throughout the
world, researchers are proposing state of the art advancements as the key technolo-
gies in the acknowledgment of beyond 5G and 6G communications. Some of these
technologies are tactile internet, mall cell communication, artificial intelligence (AI)
and machine learning (ML), edge computing, quantum communication, etc.
In addition to all these features and benefits, cellular networks are susceptible to
attackers causing security issues in the communications. With change in generation
of cellular network, their security features also change. As the cellular networks
evolve, they work on new technologies and these new technologies require new
security features. With the advancement in generation security also increases. Cryp-
tography, also referred as the foundation of modern security system, is widely used
for the security of both 5G and beyond 5G systems. Many different cryptography
algorithms are used for providing confidentiality, authentication and integrity for
wireless networks (Qadir and Varol 2019; Arora et al. 2020).
So far only limited use of quantum computing is being studied for 5G networks
and as it is expected that 6G will be working on quantum computers providing us
with quantum communications, it is essential to keep an eye for quantum technology
as it is expected to be the future of computing. Quantum computers, referred by
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 249

many researchers as un-hackable computers, have proved to be the best replace-

ment for the classical computers due to their high security features (Stubbs 2021).
Post-quantum cryptographic algorithms will be used for maintaining the security in
devices, communications and other technologies in the future (Malina et al. 2021).
The main aim of the paper is to present an updated conception of current scenario
about the security features and review of 5G and B5G networks. The contribution of
the paper is as follows:
• This work provides an in-depth overview as well as recent advancements in 5G
and 6G networks.
• This work provides comparative study about the methodologies and security
services related to B5G/6G networks.
• This work analyzes various data security techniques used by 5G cellular networks.
• This work highlights the role of quantum computing in securing 5G and B5G
cellular systems.
• Finally, the work evaluates various post-quantum cryptography techniques that
are suited for 5G and 6G networks.
The remainder of this work is organized as follows. Section 12.2 gives the depth
overview of 5G and 6G networks highlighting the security features that are expected
to be provided. Section 12.3 discusses the data security techniques used by the
current 5G networks. The section highlights different cryptographic algorithms and
key management techniques used by 5G networks. Section 12.4 is dedicated towards
the introduction of quantum computing in cryptography for 5G and B5G networks.
It provides an overview of quantum computing and discusses various associated
attributes. Furthermore, Sect. 12.5 describes the various post-quantum cryptographic
schemes highlighting their mathematical background. Finally, the conclusion is
presented in Sect. 12.6.

Emerging Cellular Networks

Telecommunications and networking are few of the key technologies responsible for
the evolution of human kind and technologies. Wireless communication and cellular
communications have played a huge role in daily lives of people. In last few decades,
the wireless cellular networks have evolved in various forms from 1G to current 4G
LTE and 5G of near future. As we’re advancing towards the commercial rollout of
5G all over the world, we’ll be introduced with high data rates to low latency even
these high speed and low latency will be dwarfed in front of the super high data rates
and ultra-low latency of beyond 5G (B5G)/6G.
250 S. Bhatt et al.

5G Overview

The world has observed a quick development of cellular communication technolo-

gies in last few years. From 2 to 3G to 4G, the cellular comminutions technologies
have been advancing in a fast pace. The fifth-generation mobile technology standard
is known as 5G. It is the next-in-line of the 4G network, which offers connectivity
in majority of mobile phones these days. As compared to its predecessors, the 5G
connection has broader bandwidth ultimately providing us with great download speed
up to 10–20 Gg/s, because of this high speed 5G network will give a huge competi-
tion to internet service providers (ISP) of laptop and desktops such as cable internet
providers (Malik and Bhushan 2022). Similar to its precursors, 5G also works on
different cells. Cells are just the smaller divided geographical areas of the service
area. Through a local antenna, these 5G devices are connected to the telephone
network and internet. As per the International Telecommunications Union Radio-
communication Sector (ITU-R), 5G has three major applications: Massive Machine
Type Communications (mMTC), Enhanced Mobile Broadband (eMBB) and Ultra
Reliable Low Latency (URLLC) (Zhang et al. 2019). As of now, only eMBB is
deployed in real world. It provides faster download speed as it uses 5G as a develop-
ment of 4G LTE communications having much higher capacity, quicker connections
and higher throughput. The URLLC will be used for latency delicate applications
or devices such as remote surgery and autonomous driving, as these applications
require super low latency with errors lower than a single packet. As for the mMTC,
it can support high connection mass of online devices (Popovski et al. 2018).

Evolution of 6G/B5G Overview

B5G (Beyond 5G) or 6G will be the sixth generation of mobile technology standards
and will probably work over 6 GHz. 6G/B5G is currently under development and as
being the successor of the 5G it’ll be significantly faster than all of its predecessors.
Similar to its predecessors, it’ll be a broadband network working under cells. As
compared to the 10–20 Gb/s download speed of 5G, 6G is supposed to have download
speed around 1 Tb/s. The lowest latency a 5G network can get is in milliseconds (ms)
level as compared to its predecessor, which is supposed to have latency below 1 ms.
The traffic density of 10 Tb/s/km2 of 5G will be diminished as compared to 1000 Tb/
s/km2 density of 6G. The energy efficiency of 6G will be 10 times relative to that of
5G. In almost every aspect 6G will be better than 5G network whether its spectrum
efficiency, end-to-end reliability requirements, processing delays, mobility or radio
only delay requirements (Khan et al. 2020). As expected, the 6G network will likely
be able to support applications or devices beyond current situations even beyond 5G
limits. The 5G network system will open our gates even further 4G with Augmented
Reality (AR)/Virtual Reality (VR) and smart cities, but with 6G we’ll be introduced
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 251

to Internet of Everything (IoE), which is beyond IoT (Bhushan 2022), Edge AI

(Chaccour and Saad 2021), AI-enabled smart cities and so on.

State-Of-The-Art

There have been various recent advancements in the fields of 5G and beyond 5G/6G
networks. Zappone et al. (2020) suggested many different ML approaches that can
help in supporting each target 5G network requirement by emphasizing its specific
use cases, moreover, they also proposed future research directions on how ML can
contribute for B5G networks. Ahmed et al. (2021) devised an incentive framework
based on deep learning known as Deep-CRNet for detecting opportunistic spectrum
access (OSA) problem in 5G and B5G cognitive radio. The accuracy of the proposed
framework was calculated via simulated results and achieved 99.74%. Sekander et al.
(2018) projected a brilliant study on multi-tier drone architecture for 5G and B5G
networks by analyzing challenges related with multi-tier drone networks and their
current advancements. The result of their study has shown the beneficial network load
condition for drones. Huang et al. (2021) proposed the very first true data testbed for
5G and B5G intelligent network full for TTIN. It consists of 5G/B5G on location test
networks, information obtaining and information distribution center. Mishra et al.
(2021) envisaged a framework known as IoT High-end Autonomous Cooper-Ative
framework (ITHACA) for 5G networks and communications beyond 5G. Further-
more, Letaief et al. (2019) have discussed the various probable technologies, which
will allow mobile AI applications with AI-enabled technologies for 6G networks.

Taxonomy of 6G/B5G Wireless Systems

The taxonomy for 6G network includes key enablers, use cases, emerging ML
schemes, communication technologies, network technologies and computing tech-
nologies.

Key Enablers

6G uses various types of technologies for operating and offering different appli-
cations. The key enablers of 6G are Homomorphic Encryption, Blockchain, AI
and Photonics-based Cognitive Radio, Edge intelligence, Network Slicing, Ubiqui-
tous Sensing and Space-Air-Ground Integrated Network (SAGIN) (Mahmood et al.
2020). Out of these Blockchain, Network Slicing, SAGIN and Ubiquitous Sensing
are considered the major key enablers (Khan et al. 2020). Blockchain is simply a kind
of a database, which makes it hard or even impossible to change alter or hack the
data. It’ll basically allow the 6G network to exchange huge amount of data securely.
252 S. Bhatt et al.

With blockchains being one of the key enablers of 6G, it’ll face few difficulties like
high energy consumption and high latency (Hewa et al. 2020). Network Slicing is a
process of creating logical and virtualized networks on a common physical infrastruc-
ture. As network slicing is already proposed via 5G technology, its actual working
or realization will be shown off in 6G. SAGIN as the name suggests consists of
satellite communication networks, aerial networks and ground networks. Few of the
advantages of SAGIN are high throughput, much better resilience than its counter-
parts and large coverage areas. Finally, the ubiquitous sensing uses video-captured
information for enabling smart decision-making and automated sensing.

Use Cases

5G networks provide us with many applications form AR/VR to smart cities. Gener-
ally, the use cases of 5G are divided into three main classes: eMBB, mMTC and
URLLC. Several few new technologies require more than these so new use cases
are defined for 6G connections. The use cases of 6G apart from that of 5G are:
Human-centric services, Holographic communication-based services, Nano-Internet
of things (N-IoT), Bio-Internet of things (B-IoT), Massive URLLC (mURLLC),
Haptics communications and unmanned mobility (Khan et al. 2020). There is the
need to implement more human centrical services than 5G such as the brain-computer
interface, for which human physiology is used to measure its performance. The
holographic communication-based services are totally based on super high accuracy
remote connection. These cannot be obtained from a 5G network as these require
high data rates than 5G can offer. The N-IoT and B-IoT as the name suggested are
based on the communications of nanodevices and biodevices over a network. Much
like 5G, the 6G network will also use URLLC but on a massive scale thus having
mURLLC (Zhang et al. 2021a). Based on URLLC, mURLLC denotes IoE applica-
tions. It’ll basically merge the 5G URLLC with the machine massive machine-type
communications (Mahmood et al. 2021). Last but not the least, Haptics communi-
cations are a type of non-verbal communications which works from a remote place
with enabling sense of touch.

Emerging Machine Learning Schemes

Machine Learning (ML) is contemplated to play an important role in development

and working of 6G network. Recently, in past few years, ML has evoked great atten-
tion in various smart applications from self-driving cars to voice assistants. As for
6G, ML is not only expected to provide smart applications but also smart transceivers
and smart access control techniques and schemes. This makes ML a fundamental
pillar of 6G network. For 6G purposes, ML is basically divided into three cate-
gories: Federated learning (Yang et al. 2021), Meta-learning (Jung and Saad 2021)
and Quantum Machine Learning (Kashyap et al. 2022). To overcome challenges
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 253

of the original ML processes, recently federated learning is being adopted. Feder-

ated leaning applies ML via a distributed means by allowing on-device ML without
drifting data through end devices to the cloud. Meta-learning helps the models to
learn with complex designs. Quantum ML is the combination of ML and quantum
physics, which ultimately results in fast raining speed of models.

Communication Technologies

6G communications will use many different communication technologies for

providing a wide variety of smart applications. The communication technologies that
the 6G network will use are quantum communications (Kashyap et al. 2022; Wang
and Rahman 2021), visible light communications (Ariyanti and Suryanegara 2020),
terahertz communications, 3D wireless communications, holographic communica-
tions and nanoscale communications (Khan et al. 2020). Quantum communications
is the field of communication that uses the fundamentals of quantum physics and
quantum computing for providing protection to data. Basically, the photons of light
are used for transmission of data through optic cables. This security feature of
quantum communication makes it suitable for 6G networks. Visible light communi-
cation full for VLC is a type of data transmission method, which uses visible lights
for the transmission of data. It uses the visible light spectrum from 430 to 790 THz.
The transmission of data through illuminous sources is the main advantage of VLC.
With addition of mmWave bands as used in 5G networks, 6G will use Terahertz
communications too. Terahertz communication is also a different type of wireless
data transmission technology which provides ultra-high-speed wireless extensions
of fiber optics for 6G networks. Another technology that 6G will use is the nanoscale
communications, as the name suggests this technology is used for communication as
nano level distances of 1 m or cm. It uses very short wavelengths for the transmission
of data (Yuan et al. 2020).

Network Technologies

Networking technologies that will be used in 6G networks are bio-networking, 3D

networking, nano-networking and optical networking. The N-IoT relies on molecular
communication to function. Nanometer-scale devices can be made with a variety of
material, such graphene and metamaterials. B-IoT is a type of IoT technology that
utilizes biological cells. B-IoT and N-IoT appear to be critical components of future
6G smart services but they face a number of implementation hurdles. Physical layer
technology design for molecular communication is a difficult task. Because B-IoT
and N-IoT are basically different from traditional IoT, unique routing algorithms must
be planned in addition to physical layer techniques. Similarly, new 3D communica-
tion models must be developed as they are different in nature than 2D communication
network (Calvanese Strinati et al. 2020).
254 S. Bhatt et al.

Computing Technologies

As the 6G system will include a huge variety different smart applications and devices,
it’ll require different types of computing technologies as well for generating the
humongous amounts of data. Quantum computing, high-performance computing and
intelligent edge computing will be used for the analysis of such data. The quantum
computing is said to change the whole field of computing by providing with much
higher speeds we haven’t experienced yet. The key factor of quantum computing is
the security it provides. As for huge amount of analyzing and computing of huge loads
of data high-performance computing is required (Blog: Samsung Research 2021).
Apart from these, intelligent edge computing is required for providing intelligent
on-demand computing and storage abilities (Hui et al. 2021) (Table 12.1).

Security Services in 6G/B5G Wireless Networks

The 6G/B5G is a new technology with new use cases, features and architecture with
these it also brings necessities for new security services. Basically, there are four
security services required for 6G network, they are: Confidentiality, Availability,
Authentication and Integrity (Bhushan and Sahoo 2017).

Confidentiality

Confidentiality consists of two things: privacy and data confidentiality. Privacy helps
in the protection of the traffic flow from an attacker as an attacker can study the
traffic flow and can identify sensitive information. As 5G and B5G, both will be
used throughout various applications loads of user’s data will be associated with
their privacy (Saxena et al. 2021). Data confidentiality on the other hand limits the
data access only to the authorized users and prevents the data leakage or disclo-
sure to unauthorized users. Data encryption is widely used for securing the data
confidentiality by stopping unlicensed users from gaining sensitive information.

Availability

Availability as the name suggests defines, to which extent some data or service is
available or accessible. It basically estimates the strength of the system or network.
Availability attacks are most common types of attacks happening over systems or
networks. Denial of Service (DoS) is the most common type of availability attack. In
this, a particular service or series or service made inaccessible to the user by flooding
the network resulting in crashing of the service. As a huge number of IoT and IoE
devices will be connected to the 6G network, it’ll be a challenge for the network to
Table 12.1 Various B5G/6G taxonomies
Key Enablers Use Cases ML schemes Communications Network Technologies Computing Technologies
Technologies
• Homomorphic encryption • eMBB • Federated learning • Quantum communications • Bio-networking • Quantum computing
• Blockchain • mMTC • Meta-learning • Visible light • 3D networking • High-performance
• AI and photonics-based • URLLC • Quantum Machine communications • Nano-networking computing
cognitive radio Learning • Terahertz communications • Optical networking • Intelligent edge
• Edge intelligence • 3D wireless computing
• Network slicing communications
• Ubiquitous sensing • Holographic
• SAGIN communications
• Nanoscale
communications
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G …
255
256 S. Bhatt et al.

prevent availability attacks such as DoS and Distributed Denial of Service (DDoS)
(Bhushan and Sahoo 2017).

Authentication

Authenticity is of two types: Message authenticity and Entity authenticity. Message

authenticity makes sure that the message has not been changed or modified, while in
between the transmit and that the receiver is getting the same message as which the
sender has sent. On the other hand, entity authenticity makes sure that the sending
party or the receiving party is the same that they claim to be and not someone
else. 6G having data speeds up to 1 Tb/s with ultra-low latency will have much faster
authenticity as compared to its predecessors. Various public key-based Authenticated
Key Agreements (AKA) are proposed for better security (Goyal et al. 2021).

Integrity

Integrity means protection against inappropriate modification or deletion of informa-

tion or data. Authentication makes sure about the source of the message is not altered,
integrity protects from the alteration or replication of the message by unauthorized
entities. As cellular networks are aimed towards more and more connectivity easing
human lives by supporting and connecting to applications used by humans in daily
lives, integrity of user’s data is a key security necessity as more and more of user’s
data is being used. Integrity security can be delivered by the mutual authentication
of mobility management entity (MME) and user equipment (UE).

Data Security Techniques for 5G Heterogeneous Networks

As we are moving towards a more digital era, come digital attackers. No network
is safe from cyber-attackers. Security is of the data that is very substantial. Hence,
numerous data security techniques are used in 5G networks for providing the safest
and securest communication of data.

Visual Secret Sharing

Cryptography is an art of sharing data secretly. Visual secret sharing also known as
visual cryptography is a method of sharing data by encysting visual media such as
text, image, etc. in a way such that the final decrypted data are in the form of a visual
image. In this, the secret data are divided into many different shares or parts, for
decryption and getting the secret message the user must have all the shares of the
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 257

original image. This technique provides high security as all the shares are required
and even if one share is missing no information or the data can be decrypted. Another
advantage of visual secret sharing is that it requires low computational complexity
(Liu and Chang 2018).

Steganography

Steganography is the method of obscuring data or a message inside another message

or file like an image, audio or video file to avoid exposure of the data. The data
are extracted by the one for whom the data are intended to receive. For enhanced
protection, steganography is used with encryption techniques for concealing and
protecting of the data. It basically works by replacing some useless or vacant parts
of the file with the bits of data which is to be hidden. There are five major types of
steganography: Steganography in images, videos, audio, text and network. Three of
the most used approaches for steganography are: Least Significant bit (LSB) (Singh
et al. 2016), secure cover selection (Qin et al. 2021) and palette-based technique
(Hao et al. 2021). In LSB, the useless bits of the transporter file are identified, which
are then replaced with the secret data. In secure cover selection, the blocks of carrier
files are compared to find the perfect match to carry the secret data. Lastly, in palette-
based technique, digital images are used. First, the secret data are encrypted then it
is hidden among the wide palette of cover image. In network steganography, the data
are hidden in the network control protocols like UDP, TCP, etc.

Cryptographic Algorithms

Cryptography is a method of securely transmitting the data from sender to receiver

with the use of secret writing. The data are sent to the receiver in form of cipher
text, for which cryptographic algorithms are used for conversion pf the pain text to
cipher text. The main applications of these algorithms are digital signatures, data
encryption and authentication.

Elliptic-Curve Cryptography

Elliptic-curve cryptography (ECC) is a type of public key cryptography method or

cryptosystem based on mathematical elliptic curves. ECC is popular for creating
smaller, quicker and more efficient cryptographic keys (Wikimedia Foundation
2021a). ECC has all the properties that an asymmetric cryptosystem has from encryp-
tion decryption to key exchange and signatures. Mostly the ECC is used for encryp-
tion of internet traffic. Private Keys in ECC are integer values mostly a 256-bit integer.
While the public keys are the coordinates of the curve, these points are known as EC
258 S. Bhatt et al.

points. The key generation in ECC is very simple and easy as just random integers
within a range are generated, any integer within that range can be used as a valid
private key. There are significant overheads in ECC. The size of the blocks is also
key dependent here. ECC also does provide resistance towards mutual authentication
and replay attacks. It also provides differential fault analysis. Apart from these, ECC
also provides various different features like key provisioning, key monitoring, key
maintenance and management. ECC is also resilient and scalable method of cryp-
tography. Different algorithms are used by ECC such as EdDSA and ECDSA for
digital signatures, FHMQV, X25519 and ECDH for key agreement and EEECC and
ECIES for encryption (ECC keys 2021).

RSA

RSA stands for Rivest-Shamir-Adleman. It is also a type of public key cryptography

system used for secure transmission of data. It is one of the oldest cryptosystems.
As being a traditional public key cryptosystem, it can perform various tasks such
as encryption, decryption, key exchange and signatures. Similar to ECC, it’s also a
type of asymmetric cryptography (Wikimedia Foundation 2021b). The block size is
of 86 bytes. There are less overheads in RSA cryptosystem. It also provides partial
resistance towards mutual authentication and replay attacks. Similar to ECC, the RSA
is also provided key provisioning, key monitoring, key maintenance and management
and is also scalable and resilient. RSA provides partial analysis of differential fault.
In RSA, the keys are generated usually using two large prime numbers. RSA is
used in many services like VPN, web browsers, email services and many more
communication services. As compared to ECC, RSA is slow thus, it is not used
directly for encryption of data.

Diffie-Helman

Diffie-Helman is a technique of exchanging cryptographic keys securely. It is the

first broadly used technique of exchanging keys over an insecure channel. Hence,
it is called Diffie-Helman key exchange. As two different organizations or parties
need to exchange keys for a successful encrypted communication (Li 2010). The
channel between these parties needs to be secure for usual security reasons, here
the Diffie-Helman exchange is used for exchange of keys between two unknown
or known parties by forming a shared secret key in an uncertain channel. Here the
cipher overheads are significant. Diffie-Helman also shows resistance towards mutual
authentication and replay attacks up to some degree. The cipher block size here is
variable, as it is dependent upon the selected prime number. As compared to ECC
and RSA, the Diffie-Helman is partially resilient and scalable, and it also provides
partial key provisioning, key monitoring, key maintenance and management.
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 259

ElGamal

ElGamal is a type of asymmetric encryption system used for public key cryptosys-
tems. It is based on Diffie-Helman key exchange system (Wikimedia Foundation
2021c). It comprises of three parts the key generation, encryption and decryption
algorithm (Tsiounis and Yung 1998). This cryptosystem depends on the trouble of
discovering discrete logarithm inside a cyclic group. ElGamal is a type of proba-
bilistic encryption, it means that many different ciphertexts of a plaintext can be
generated. As compared to RSA and ECC, they both worked on integer factoriza-
tion while ElGamal works with discrete logarithm. There are moderate number of
overheads in ElGamal. The block size of ciphertext is variable and depends upon
the key length. This doesn’t show resiliency and scalability. Key provisioning, key
monitoring, key maintenance and management are also absent here. The resistance
towards replay attacks and mutual authentication is not effective as much. Further-
more, the differential fault analysis provided by ElGamal is not as effect as much
too.

DES

DES, short for Data Encryption Standard, is a type of symmetric cryptosystem

method meaning it requires a single key for encryption and decryption of data. It is
based on LUCIFER (Wikimedia Foundation 2021d), a Feistel block cipher. It has
a 64-bit block size. Here, 64-bit blocks of ciphertexts are transformed into 48-bit
keys. There are total of 16 rounds of encryption in DES and each round executes the
essential attributes of cryptography which are transposition and substitution. This
offers important and generic tools for watermarking digital videos and streams for
tamper detection and protection against watermark copy attacks. There are very few
overheads of ciphertexts here. DES is not scalable or resilient and doesn’t provide
differential fault analysis, key provisioning, key monitoring, key maintenance and
management. Furthermore, it doesn’t show resistance towards mutual authentication
and replay attacks. Because of these problems, there are concerns about security and
speed of DES is making users to shift towards newer block cipher designs or reusing
DES like Triple DES (TDES).

AES

AES stands for Advanced Encryption Standard. Similar to DES, it is also a type of
symmetric block cipher system. It is well known for its use by the U.S. government
for the protection of classified data (Bernstein and Cobb 2021). It was developed
as an alternative for DES. It has the cipher blocks of 128-bit and key lengths of
128, 192 and 256 bits. The cipher overhead is also noteworthy in AES. As being
an improvement of DES, it does have resistance against mutual authentication and
replay attacks and also does provide users with differential fault analysis and was
260 S. Bhatt et al.

Table 12.2 Different types of cryptographic algorithms used in 5G networks

Functionality ECC RSA Diffie-Helman ElGamal DES AES
Block size Depends on 86 Variable Variable, 64 128 bits
key bytes depends upon key length bits
the selected dependent
prime no
Number of keys 1/1 1/1 1/1 1/1 1/1 3/3
Overheads Noteworthy Less Noteworthy Moderate Very Noteworthy
less
Resilience, Yes Yes Partially No No Partially, key
scalability, key provisioning
management and depends upon
provisioning computing
speed
Resistance Yes Yes Up to an extent Not No Yes
towards mutual effective
authentication and
replay attacks

also faster and more reliable. It does also provide partial resiliency and scalability
with key management and provisioning but the key provisioning is dependent upon
the computational speed of the system. Due to so many advantages and high security,
it is one of the most popular encryption algorithms used today, used in many ways
from wireless security to web browsers (Table 12.2).

Key Management

CKSM stands for Cryptographic Key Management System. It is a system that is

used to protect key data. Key management as the name suggests is the process of
managing or handling the cryptographic keys inside a cryptosystem. It basically
includes storing, generating and exchanging keys as per user’s requirement.

Key Escrow

Key escrow is a method in which keys that are required to decrypt an encrypted
data are stored in an escrow. Escrow is basically a bond kept in safekeeping of
third party and engaged only when certain conditions are met. In simple words, key
escrow is nothing more than a process of storing cryptographic keys. As a third
party is involved key escrow system is not much on the safer side and does include
some risks (Sugumar and Ramakrishnan 2018; Foundation 2020). Apart from this,
there are other problems related to this too like the mutual authentication of both
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 261

parties is not always satisfactory and key escrow also have shown some problem
with identity-based encryption.

Identity-Based Encryption (IBE)

IBE is public key-based encryption (PKE) that uses identifiers as the source for
encryption. In this, the public key of the user is the distinctive detail about the
identity of the user. Here, the public key of the user is created with the public key of
a third party. Similarly, the private key of the user is also computed in conjunction
of the private key of the third party, making it secure as no one else than the third
party can access the user’s private key (Khan and Niemi 2017). These third parties
are known as private key generators (PKG).

Attribute-Based Encryption (ABE)

ABE is also a type of PKE in which the secret key and the ciphertext both are reliant
upon attributes such as the country of the user or the particular type of services
they’ve enrolled for. Here, decryption of cyphertext is only possible if the attributes
of the user key match that of the ciphertext (Zhang et al. 2021b). ABE is divided
into two different types those are: Key-policy attribute-based encryption (KP-ABE)
and Ciphertext-policy attribute-based encryption (CP-ABE) (Wikimedia Foundation
2021e). User’s secret keys in KP-ABE are produced by an access tree that describes
the user’s privilege and encrypts data over set of attributes. CP-ABE, on the other
hand, encrypts data using an access tree and secret key is encrypted based on set of
attributes.

Overview and Fundamentals of Quantum Computing

In causal and simple words, the use of computers is known as computing. There
are two major types of computing: classical and quantum. Classical being the one
which we all use in our day to day lives. Other is quantum computing, it is a type
of computing that combined concepts of computer science and quantum physics.
Quantum computing is still a progressively growing research area (Elsevier. (n.d.).
2021).
262 S. Bhatt et al.

Architecture of Classical Computing Versus Quantum

Computing

There are huge differences between classical computing and quantum computing.
With the increment of quantum properties in quantum computers, it makes the
difference between the two even greater.

Classical Computing

Classical computing also known as binary computing is the traditional approach in

computing. In classical computing, the information is kept in bits. Everything words,
integers, audio, video, image, all are represented in the form of bits. These bits are
represented either in 0 or 1. The information is fragmented into simple Boolean
logical gates. Every logical gate in classical computing takes input as 1 or 2 bits
and as an output, it gives new bits as a result (Navaneeth and Dileep 2020). There
are a total of seven logical gates in classical computing, each takes and gives output
in unique way. The computation is done by specially arranging the logical gates
accordingly.

Quantum Computing

Quantum computing is the type of computing as the name suggests which uses the
properties of quantum mechanics to provide a huge leap over classical computation
for solving problems and calculations. Quantum computers follow the probabilistic
approach for calculations, meaning they solve problem upon the most probable
outcome, simultaneously using several other dimensions. As compared to classical
computing which uses 0’s and 1’s for representing the data, quantum computing
offers many new ways of data representation. In quantum computing, quantum bits
are used. These quantum bits are known as qubits. The operations which the qubits
contain are sensitive and are unstable (Haller 2021), due to which the qubits require
very specific requirements for working correctly. For functioning efficiently, vacuum
and temperature very close to absolute zero are required by the qubits. Furthermore,
they endure no interference, which turns out to be exceptionally muddled while
working on a nanoscale individual electrons and photons. For differentiating bits
and qubits, lets take an example—as classic computer uses 8 bits to denote a number
between 0 and 255, instead of bits 8 qubits can represent all the number between 0
and 255, that too simultaneously.
Quantum computing offers superposition (Khrennikov 2021), due to which
between these 0’s and 1’s, infinite other states can also be present there. In these states,
there are infinite number of qubits. Apart from superposition quantum, computers
also offer other quantum mechanics-based phenomenon such as quantum entangle-
ment (Duarte 2019). It is a phenomenon that occurs when particles are created. It
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 263

is a property between two or more qubits, which allows the qubits to have higher
amount of correlation with each other. These properties like superposition, quantum
entanglement, quantum interference, No-cloning theorem and destructive measure-
ment are not present in the classical computing. Hence, making a huge difference
between quantum computing and classical computing.

Mathematical Representation for Quantum Computing

In quantum physics, the qubits work upon the spin of the qubits and their direction
of spin. Mathematically, the use of calculus is not required for qubits. Hence, the
concept of vectors is highly useful for describing and analyzing the spin of qubits.
Apart from this, fundamentals of matrices are also used for the measurement of qubit
spin in some cases (Lam 2019). The likelihood of getting a particular estimation for
spin, as far as one might be concerned, can be depicted utilizing probability. In this
way, a comprehension of probability hypothesis is somewhat valuable as it relates
with quantum mechanics. Hence, matrices, complex number, vectors and probability
play an extremely huge part in mathematics and calculations of quantum computing
(Chamola et al. 2021). The states zero and one in qubit are presented by |0 ⟩ and |1 ⟩,
respectively. Entanglement of two qubits can be represented as:

|0 ⟩ ⊗ |1 ⟩ = |01 ⟩ (12.1)

In quantum computing, vectors are represented as a list of numbers. A single qubit

is considered a two-dimensional vector. The dimension of vector is represented as
the rows, i.e., the dimension of the vector is labeled as number of rows of the vector.
Representation of qubit in vector is as follows:
[ ]
1
|0⟩ : (12.2)
0
[ ]
0
|1⟩ : (12.3)
1

While in the state of a superposition, the qubit has 50–50% probability of

becoming a zero and one respectively. For mathematical representation, let’s take
an example of superposition:
[√ ]
√ √ 2/3
2/3 · |0⟩ + 1/3 · |1⟩ → √ (12.4)
1/3
264 S. Bhatt et al.

Above, superposition is shown in which |0⟩ has the probability of 2/3 and the
probability of |1⟩ is 1/3. The square roots are there because the unit circle where the
sum of probabilities is always one can be represented by vectors.

The Impact of Quantum Computing on Modern Cryptography

The rapid increment in the field of quantum computing can also disturb many big and
small organizations. As quantum computing is a lot different and superior than that of
classical computing in many different aspects, it has several impacts on cryptographic
algorithms made for classical computers.

Cryptanalysis

As quantum computers are expected to provide a huge number amount of leap over
the common classical computers, it will be very easy for the quantum computers to
break the security provided to us by the classical methods of cryptography. With the
large-scale rollout of quantum computers for people, havoc will be caused regarding
the security, as majority of the technology uses the classical cryptography algorithms.
Quantum computing algorithms have proved to be efficient against both symmetric
and asymmetric cryptography algorithms (Mitchell 2020; Schanck 2020). As asym-
metric algorithms rely on the huge amount of time taken by classical computers to
factorize huge integers for their security, this can easily be tackled with use of Shor’s
algorithm (Devitt et al. 2005). On the other hand, for finding the key of symmetric
cryptographic algorithms, computers take around k/2 operations, with quantum algo-
Grove’s algorithm (Mandviwalla et al. 2018), this time could be further
rithms like √
reduced to k (k being the size of key) operations.

Security Impacts

Cryptography is one of the most significant techniques used in modern technology

for securing a communication either between two users or a user and its machine. As
current cryptographic algorithms are considered insecure in comparison to quantum
computers, it can cause catastrophic impacts on the digital world. For algorithms
that are only used for affirming the integrity of sent data, for instance whose use
has no extended-out influence, there will no big issues, to the extent that until new
and secure algorithms are introduced. Comparing to key establishment algorithms
and encryption algorithms, here the impact will be tremendous (Fernandez-Carames
2020).
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 265

Replacement Algorithms

As these quantum computers will have such a destructive impact on the security
given by current cryptographic algorithms, many organizations have already started
development, research and studies on new cryptographic algorithms and standards.
As for symmetric cryptographic algorithms, no promising new technologies and
advancements are made as current symmetric cryptographic algorithms work on
using 265-bit keys apart from moving towards using more longer keys for encryption.
As for asymmetric cryptographic algorithms, it relies on the factorization of huge
integers. It has been proved to be a piece of cake for the quantum computers to
factorize huge integers. So many other factors are also needed to be taken care for
the development of new asymmetric cryptographic algorithms, which can withstand
the quantum computers. Many agencies such as ISO, NIST, IEC, etc. have already
started their development of quantum-resistant asymmetric cryptographic algorithms
(Mitchell 2020).

Quantum Algorithms Affecting Cryptosystems

As stated above that the quantum algorithms will have a significant amount of impact
upon the current cryptographic systems. Whether its symmetric or asymmetric cryp-
tographic algorithms, some quantum algorithms with the help of quantum computers
can easily surpass them.

Shor’s Algorithm

Peter Williston Shor, in 1994, created an algorithm for integer factorization known
as Shor’s algorithm. It’s a polynomial-time quantum computer algorithm. In his
research, he proposed that large factorization of large integers can be done via
quantum computers (Wikimedia Foundation 2021f). Modern cryptographic algo-
rithms such as RSA provide security on the basis that classical computers have slower
computational speed for factorizing huge integers and could take huge amount of
time basically millions or billions of years for the factorization (Devitt et al. 2005;
Bhatia and Ramkumar 2020). Shor guaranteed that it is feasible to change the factor-
ization issue over to another issue of finding the time of an integer 0 < x < N . A
periodic function where a ≥ 0, x is an integer coprime to N:

F(a) = x a mod N (12.5)

Shor’s Algorithm works as:

• Firstly, the factorization problem is converted into the problem of period finding:
Since period is r and it is periodic with F(a)
266 S. Bhatt et al.

x 0 mod N = 1 (12.6)

So,

x r mod N = 1 (12.7)

Then,

X r = 1mod N (12.8)

Mathematically,

(x r/2 + 1)(x r/2 − 1) = 0mod N (12.9)

Following equation is a multiple of N

r r
(x 2 + 1)(x 2 − 1) = 0 (12.10)

Between (x r/2 + 1) and (x r/2 − 1) at least one of these should have a non-trivial
factor common to N, for not being a multiple of N.
Now obtaining a factor of N using:
r
GC D((x 2 + 1), N ) (12.11)

GC D((x r/2 − 1), N ) (12.12)

Above GCD stands for Greatest Common Divisor.

• Now finding the period using Quantum Fourier Transformation.
Initialize the qubits in superposition and compute the modular exponentiation.
Now perform the Quantum Fourier Transformation multiple times for getting the
good probabilistic result. The Quantum Fourier Transformation uses amplitude
amplification.
• Finally, finding with the use of periods finding the factors, after r is recognized.
Out of:

GC D((x r/2 + 1), N ) (12.13)

GC D((x r/2 − 1), N ) (12.14)

At least, one factor of these equations will be a non-trivial factor of N. Hence, the
factor is found.
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 267

Quantum Annealing

Quantum annealing is a process of finding the most efficient solution. It is a process

used for solving optimization problems. It normally gives low-energy solutions for
applications that necessitate the genuine least energy and others require great low-
energy tests. Several companies use quantum annealing such as IBM, Microsoft,
D-Wave, etc. (Mitchell 2020; Hauke et al. 2020).
As for cryptography, quantum annealing is used for factorizing integers into prime
numbers, same as Shor’s algorithm. Similarly, like Shor’s algorithm, it can be used for
factorizing large integer values using quantum computers. The quantum annealing
approach is as follows:
Take N = pq, here both p and q are the prime numbers. These two prime numbers
can be written into binary form as,

p =1+ 2i pi (12.15)
i=1...s p

and,

q =1+ 2i qi (12.16)
i=1...sq

So, the function can be,

 
f p1, p2, . . . , psp , q1, q2, . . . qsp = (N − pq)2 (12.17)

If the pi , q i are found for which the value of f is minimum or 0, the problem for
factorization is solved. For such situations, quantum annealing such as D-Wave is
used for finding the minimum value.
For example, suppose,

N = 15 = pq (12.18)

And let the binary depiction of p has 2 bits: (x1 , 1)

So,

p(x1 , 1) = 2x1 + 1 (12.19)

Similarly,

q(x2 , x3 , 1) = 22 x2 + 2x3 + 1 (12.20)

Likewise, the binary depiction of a prime number will always comprehend a 1 as

its least important number. The function to minimize is f ,
268 S. Bhatt et al.

f (x1 , x2 , x3 ) = (N − pq)2 (12.21)

2
= (15 − (2x1 + 1)(22 x2 + 2x3 + 1)) (12.22)

Further solving for (x1 , x2 , x3 ) and we will get the factors.

As quantum computing is based upon the probabilistic approach, it is always a
must to run these algorithms multiple times for getting the best probabilistic result.

Grover’s Algorithm

In 1996, Lov Grover came up with a quantum search algorithm called as Grover’s
algorithm. It is used for improved efficiency of searching data over an unstructured
database. For a database of N number of items in it and we want to search for a
specific item, it would take a classical computer N /2 opertaions to find the specified
item at average case and at worst case it would take N operations (Devitt et al. 2005;
Brickman et al. 2005). For quantum
√ computers, the time required for searching is
very less as it would only take N opertains to find the specified item.
For example, we have to find an item s from a set S having 2n items in it.
Denoting every time in the set with a number so,

x ∈ 0, 1, . . . 2n − 1 (12.23)

Let f (x) be a function in which f (x) checks for the required item and checks if
it is or not,

1 if x = s
f (x) = (12.24)
0 other wise

For computing the problem in a quantum computer, few changes are needed to be
made, such as:
x converted to a qubit, x → |x⟩
f converted into an operator, f → θ̂
Δ

Now, θ |x⟩ can be represented as,

Δ
|x ⟩ i f x = s
θ |x ⟩ = (12.25)
−|x ⟩ other wise

In a simplified manner, it can be written as,

Δ

θ |x ⟩ = (−1) f (x) |x ⟩ (12.26)

12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 269

Now, we can modify the problem and say that we want to find the qubit |s ⟩ from
the set of qubits S = {|x ⟩ : x ∈ {0, 1, . . . , }}.
As for solving the above problem, we will have to start with from superposition
of all the possible solutions:

2 −1
1 
n

|E ⟩ = √ |x ⟩ (12.27)
2n x=0

The |E ⟩ contains all the solutions including |s ⟩. There are 21n probability that |E ⟩
will give the required solution. As all the solutions have same amplitude, we have to
grow the amount of amplitude for |s ⟩ in |E ⟩. For that we have to take an intermediate
state. So,
Let us take |ψ1 ⟩ as intermediate state.

|ψ1 ⟩ = |E ⟩ (12.28)

= θ |E ⟩ (12.29)

= (2|E ⟩|E ⟩ − 1)θ |E ⟩ (12.30)

Similarly,

|ψ2 ⟩ = |ψ1 ⟩ (12.31)

= θ |ψ1 ⟩ (12.32)

= (2|E ⟩|E ⟩ − 1)θ |ψ1 ⟩ (12.33)

Generally,
Δ
t
|ψ1 ⟩ = ((2|E ⟩|E ⟩ − 1)θ ) |E ⟩ (12.34)

Hence, with each iteration of |ψ ⟩, amplitude of the solution also increases.

Post-Quantum Cryptography

Post-quantum cryptography also known as quantum resistant or quantum proof cryp-

tography is a type of cryptography, which is thought to protect us from attacks
conducted via quantum computers (Wikimedia Foundation 2021g; Bernstein n.d).
Cryptography is one of the most important techniques used in modern technology
270 S. Bhatt et al.

for secure communication either between two users or a user and its machine.
According to several surveys, more than 90% of the currency is digital, all of it
uses the concept of cryptography for security purposes. As quantum computers are
the possible future, they offer huge advantages and new technologies as compared
to classical computers. They will break the current infrastructure of computations
and current cryptographic techniques will not be powerful enough to counter them.
Hence, we’ll be requiring new cryptographic techniques or algorithms to protect
us from such scenarios, where we’ve to protect our data from hacker or attacks
coming from quantum computers. The National Security Agency (NSA) of USA has
already transitioned to post-quantum cryptographic algorithms as it is not known
when a powerful enough quantum computer will be there which can break current
cryptographic techniques. Dissimilar to quantum-based cryptography, post-quantum
cryptosystems depend on some numerical issues that are not difficult to figure out
for the receiving end, however, harder for the attacker (Chen et al. 2016).

Mathematical View

There are various quantum resistant cryptographic techniques that have already been
proven to work effectively to provide security that current cryptography schemes
couldn’t provide while facing quantum computers. The working of these techniques
is heavily relied upon their mathematical backgrounds.

Lattice-Based Cryptography

Lattice-based cryptography (Pradhan et al. 2019; Yao et al. 2021) as the name
suggests is a type of cryptographic primitive, which uses the lattices for creation
of cryptographic algorithms. Unlike common cryptographic schemes, some lattice-
based constructions seem, by all accounts, to be impervious to assault by both tradi-
tional and quantum computers (Nejatollahi et al. 2019). Moreover, numerous lattice-
based developments are viewed as secure under the supposition that specific all
around concentrated on computational lattice issues can’t be tackled proficiently.
• SVP (Shortest Vector Problem) is the widely used Lattice-based cryptosystem.
The key generation in SVP is done:

( p, q, n, m), B ∈ Z n×m (12.35)

Theencryptioniscarriedoutby : H B : 1, . . . , d m → Z np (12.36)

Thedecryptionofkeyisdoneby : H B (x) = Bx modp (12.37)

12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 271

• DGS (Discrete Gaussian sampling) is another type of lattice-based scheme. Key

generation in DGS is done using:
   
M ≥ u 2 × Q D 2 Q −1 = u 2 V .V c , a∈Ʌ + Pc = (P − a2) (12.38)

The encryption equations are : a2 ← DɅ2+P2,√ M2 (12.39)

a1 ← DɅ1+P1−a2 ,√ M1 +a2 (12.40)

M = (M1 + M2 ) > 0 (12.41)

√
V2 = M2 (12.42)

a2 → D√ M2 = V2 × S1 (12.43)

Decryption is done by:

a = a2 + (P + a2 ) − V1 V1−1 × (P − a2 ) u (12.44)

a = a2 + c − V1 [Va−1 × c]u (12.45)

• NTRU is another cryptosystem based on Lattice-based cryptography. It is a public

key-based cryptosystem. The key generation in NRTU is done:

K ≡ p Iq × J (modq) (12.46)

Encryptionofthedataisdoneby : C ≡ R × K + M(modq) (12.47)

Finally, thedecryptionofthedataisdoneby : u = I × C(modq) (12.48)

u = I (Rp Iq J + M)(modq) (12.49)

u = [Rp Iq J + M](modq) (12.50)

v = I p I M(modq) = M (12.51)
272 S. Bhatt et al.

Multivariate Cryptography

Multivariate cryptography (Ding and Petzoldt 2017; Carenzo and Polak 2019), as the
name suggests, is a cryptographic primitive that used multivariate polynomial equa-
tions. Multivariate means multiple variables. Multivariate public key cryptosystems
have an arrangement of nonlinear multivariate polynomials.


n 
n 
n
p (1) (x1 , x2 , . . . xn ) = pi(1)
j · xi x j + ·xi + P0(1) (12.52)
i=1 j=1 i= j


n 
n 
n
p (2) (x1 , x2 , . . . xn ) == pi(2)
j · xi x j + ·xi + P0(2) (12.53)
i=1 j=1 i= j


n 
n 
n
p (n) (x1 , x2 , . . . xn ) == pi(n)
j · xi x j + ·xi + P0(n) (12.54)
i=1 j=1 i= j

The size of the public key is roughly around: m · ( n+d d

).
Here d is the degree of the polynomials in the equation. Most of the time d is
taken as 2 for better efficiency. The security of the system is based on the problem:
The m multivariate quadratic polynomials p (1) (x), . . . , p (m) (x). Now we have to
find the vector x = x 1 , . . . , x n such that p 1 (x) = p (m) (x).
This problem is ended up being a NP-hard and resolving system of multivariate
polynomials is demonstrated as NP-complete (Garey and Johnson 2009). This being
the reason it is viewed as a good contender for post-quantum cryptography. The
above-stated problem is supposed to be hard for classical as well as quantum
computers. For the construction of multivariate cryptosystem, let F be an effectively
invertible quadratic such that,

F : Fn → F m (12.55)

Two invertible linear maps,

S : Fm → Fm (12.56)

T : Fn → Fn (12.57)

Public key will be,

P :S ◦F ◦T (12.58)

Private key will be,

S , F, T (12.59)
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 273

The F is combined with T and S and is well hid in the public key. If the public
key is P, find linear maps S and T as well as a simply invertible quadratic map
F such that P : S ◦ F ◦ T .
The encryption will be:

w = P(z) (12.60)

z ∈ Fn (12.61)

w ∈ Fm (12.62)

Here, z is the message and the decryption is done by:

x = S −1 (w) (12.63)

y = F −1 (x) (12.64)

z = T −1 (y) (12.65)

And m ≥ n, as it ensures that the ciphertext has only one possible plain text.

Hash-Based Cryptography

Hash-based cryptography (Potii et al. 2017) is the cryptographic primitive that uses
hash functions for security of the message. As of now hash-based cryptography is
used for creation of digital signatures. It is used in almost all the digital signature
algorithms (Wikimedia Foundation 2021h). The hash-based cryptography algorithm
is as follows.
Let H be the hash function so that,

H : {0, 1}∗ → {0, 1}n (12.66)

Make 2 random strings X 0 and X 1

S = (X 0 , X 1 ) (12.67)

Here S is the secret key. Let P be the public key,

P = (H (X 0 ), H (X 1 )) (12.68)

Now the public key is published. Now to sign a bit let’s say 0 the signer has to
make the string X 0 public. Then the verifier will calculate the H (X 0 ) and match
274 S. Bhatt et al.

it with the value of public key. Similarly, to sign the bit 1 the signer has to make
the string X 1 public then the verifier need to calculate the H (X 1 ) and match the
value with the public key. Bit string of length b, for signing it, the secret key will be
generated by the signer of length 2b.

S = (X 10 , X 11 , X 20 , X 21 , . . . , X m0 , X m1 )

The public key will be:

P = (H (X 10 ), H (X 11 ), . . . , H (X m0 ), H (X m1 ))

Some of the hash-based cryptography schemes are newer versions of XMSS,

Lamport signatures, SPHINCS and Merkle signature scheme.

Code-Based Cryptography

Code-based cryptography (Cohen et al. 2021; Samokhina and Trushina 2017) uses
error correctional and detection algorithms for security. In these, the errors are used
for encrypting the message and for decryption the errors are removed from the
message. While moving data, at least one or more bits may get flicked. To recu-
perate the original message, the error detection and corrections are utilized. Linear
error correction codes are widely used code-based cryptography schemes as they can
also be used for creating one-way functions (Alagic et al. 2019). A bounded distance
decoding problem is as follows:
Linear code:

C ⊆ F2n

y ∈ F2n

t ∈N

In this linear code, we’ve to find,

x ∈ C such that dist (x, y) ≤ t

This problem is proved to be a NP-Complete problem. McEliece encryption

scheme can be used as a probable solution for the above-stated problem. In this
scheme, the sender adds up error in the message with the help of receiver’s public
key. The errors are added in such a way that only the receiver can find and correct
them as they have the private key.
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 275

Mathematically, let’s take S, G and P be the matrices over F. G here is the generator
matrix for the Goppa code. These codes are used for error correction efficiently.
So, public key is
,
G = S ◦ G ◦ P, t (12.69)

And secret key,

P, S, G (12.70)

Encryption can be done by,

,
c = mG + z ∈ F n (12.71)

Here, the message is multiplied using the receiver’s public key and z is the error,
which has been added. Now for decryption,

x = c P −1 = m SG + z P −1 (12.72)

That’s how the receiver detects and corrects the error.

Post-Quantum Analysis

It is a must to analyze how security of the 5G and B5G systems would affect in the
post-quantum era. The 5G systems will have huge impact on their security schemes
as they follow modern/classical cryptography. The USIM and unique identifier of 5G
which is Subscription Permanent Identifier (SUPI) will majorly affect as these will be
the focus of the attackers. As for B5G or 6G networks, the quantum computers won’t
have much effect as of now because these networks will use quantum computing
approaches for development. Hence, providing security in post-quantum era.

Authentication and Key Establishment

In post-quantum era, the 5G systems will be heavily under vulnerability. If an attacker

interrupts the matching pair of authentication response message and authentication
request of the authentication and key agreement, both the messages will be carrying
128-bit values. For cracking them, Groover’s algorithm can be used as it will reduce
the time to 264 operations only. This can provide enough data to the attacker about
the key of the USIM. Furthermore, if the SUPI of the network is also discovered,
it can lead to cloning of the whole USIM (Wang et al. 2021). As for B5G or 6G
networks, the studies are still undergoing and so far, these networks are supposed to
276 S. Bhatt et al.

be working fine in the post-quantum era because of much superiority as compared

to their predecessors and higher security measures.

Symmetric Encryption and Integrity Protection

In case of symmetric encryption, when an attacker interrupts the traffic which is

sent over the network, he gets 32-bit MAC that addresses along with encrypted
protocol data units. Using this information, an attack could be initiated, though it
will be harder as compared to when the attacker has both the authentication and key
agreement messages (Munilla et al. 2021). Here, the attacker would also need to know
other information such as the plaintext, ciphertext and counter values. Thus, due to
these many complications, conducting attacks using the information of authentication
and key agreement message is much easier as compared to conducting attacks by
intercepting the traffic of the network.

Asymmetric Encryption

As for security of asymmetric encryption, it is used for the protection of the user
equipment’s permanent identity, which, in the case of 5G, is SUPI. Usually, the
USIM stores the home network public key that can be obtained using the user equip-
ment (What next in the world of post-quantum cryptography 2021). As stated above
that the Shor’s algorithm has already proved to be decimating the modern asym-
metric cryptographic schemes easily, the private key can be easily discovered just by
knowing the public key in post-quantum era. This can cause menace by canceling
the service of mobile identity confidentiality in all the USIMs having public key.

Conclusion

As an evolving study of 5G and beyond 5G networks, it has proved to be the tech-

nology of future which can revolutionize our daily lives. The 5G systems will open
gates to new technologies such as AR/VR and smart cities. Furthermore, Internet
of Everything (IoE) that is beyond IoT, Edge AI, AI-enabled smart cities and so on
will be brought to us by 6G or beyond 5G networks, resulting in the total change of
the way of living for humans. With these functionalities, the network systems will
also need proper data security standards. By applying proper security techniques into
the cellular networks, the users can be rest assured about security, and these future
cellular networking technologies will come out to be an outstanding communica-
tion technology. In this paper, the evolution of 5G and 6G networks is discussed.
Further, this paper explores the taxonomy used in 6G networks, security services
provided by 6G networks and data security methods used in 5G networks such as
steganography, RSA, AES, etc. Furthermore, the paper describes the architecture of
12 Post-Quantum Cryptographic Schemes for Security Enhancement in 5G … 277

quantum computers and presents the mathematical background of various quantum

cryptographic schemes. Finally, the work describes the post-quantum cryptographic
algorithms such as lattice-based cryptosystems, code-based cryptography, etc.

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Chapter 13
Enhanced Energy Efficiency
and Scalability in Cellular Networks
for Massive IoT

Husam Rajab and Tibor Cinkler

Abstract The significant expansion of cellular networks has increased their poten-
tial to support a wide range of use cases beyond their original purpose of providing
broadband access. One such development is using cellular networks to support the
Internet of Things (IoT), called Cellular IoT (CIoT). The growth of CIoT is an
important trend in the evolution of cellular networks, it leads to broader and more
comprehensive ecosystem circumstances. The extensive IoT business evolution is
transforming a diverse sector, including health, smart cities, security, and agriculture.
Nevertheless, a large scale with very different characteristics and use cases struggle
with connectivity challenges due to the unique traffic features of massive IoT and the
tremendous density of IoT devices. This study aims to identify the critical obstacles
that hinder the widespread deployment of IoT over cellular networks and suggest an
innovative algorithm to mitigate them effectively. We discovered that the primary
challenges revolve around three specific areas: connection setup, network resource
management, and energy consumption. In this regard, we investigate the integration
of massive Machine-Type Communication (mMTC) into cellular networks, focusing
on the performance of Narrowband IoT (NB-IoT) in supporting mMTC.

Keywords IoT · CIoT · mMTC · NB-IoT

Introduction

Wireless communication networks have significantly impacted various aspects of

our lives, including healthcare, professional networking, and accessing information.
Over the past few decades, these networks have evolved from being expensive to
becoming pervasive and accessible to many of the population. The development of

H. Rajab (B) · T. Cinkler

Budapest University of Technology and Economics, Budapest, Hungary
e-mail: husamrajab@tmit.bme.hu
T. Cinkler
e-mail: cinkler@tmit.bme.hu

© The Author(s) 2023 283

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_13
284 H. Rajab and T. Cinkler

cellular networks has been particularly revolutionary, leading to the emergence of

new use cases and challenges. There are four generations of cellular networks (1G–
4G) shown in Fig. 13.1, and the fifth generation has recently been introduced. The
first and second generations were primarily focused on voice-based communication.
The first and second generations of cellular networks were aimed to perform voice-
centric operations. Whereas the third and fourth generations of cellular networks
involved data packets with new data rate and frequency. Each new generation of
cellular networks has built upon the services of the previous generation and has
seen significant technological advancements. However, one drawback of the first
four generations was that their communication design was primarily focused on
human-centric services.
In other words, the first four generations of cellular networks were developed
primarily to support voice and data communication between individuals. While these
networks have evolved to offer faster speeds and more advanced features, their basic
design remained centered around human communication needs. With the advent of
the fifth generation of cellular networks, there is a greater emphasis on designing
networks to support a broader range of use cases, including machine-to-machine
communication and the Internet of Things (IoT). Before the 5G, cellular networks’
concentration was to perform more services to human users (Liberg et al. 2017).
The Internet of Things (IoT) has emerged as a significant technological revolution in
recent years. IoT refers to the interconnection of many devices embedded in everyday
objects, enabling them to access the internet and share data without requiring human
intervention. This idea embodies the concept of everything being connected. Any
smart device can connect to the internet through physical items such as machines,
devices, and vehicles.
IoT devices can serve various applications, including healthcare, agriculture, secu-
rity, smart cities, industrial automation, and autonomous driving. New applications
are emerging daily, showcasing this technology’s vast potential. The growth of IoT
has led to the development of new business models and opportunities for innovation.

Fig. 13.1 Cellular generation

13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 285

With the increasing use of connected devices, IoT is set to become an integral part
of our daily lives in the years to come. This diversity of IoT applications has caused
transcendent freedom to users, and recently we have seen an enormous accession
in their numbers. Massive devices are already implemented, and these numbers are
expected to grow shortly, with predictions reporting that most equal 29 billion IoT
devices will be functioning by 2023 (Ericsson Mobility Report). To adapt to the new
requirements for device connectivity to further assist IoT, previous cellular networks
must be restructured.
This chapter will focus on identifying the core issues that limit IoT implemen-
tation over cellular networks at a large scale and a novel solution to mitigate them.
The majority of the problems arise in three distinct aspects, i.e., the establishment
of connection, utilization of network resources, and efficiency. In this context, we
examined the containment of massive Machine-type Communications (mMTC) into
cellular networks. Apart from that, the performance of Narrowband-IoT (NB-IoT)
within cellular networks will be improved.
This chapter is divided into several sections. Firstly, we provide an overview
of related work in “Literature Review” section. Next, in “Narrowband-Internet of
Things (NB-IoT)” section, we discuss the Narrowband-Internet of Things (NB-IoT)
and explain the Power Saving Mode (PSM) and extended Discontinuous Reception
(eDRX). We then present our methodology and performance analysis in “Method-
ology” section. Validation results for the proposed algorithm and analytical NB-
IoT model are provided in “Result” section and “Discussion” section, respectively.
Finally, we offer concluding remarks and outline future work in “Conclusion” section.

Literature Review

In this section, we examine the literature related to the issues discussed in this chapter.
We review the significant works related to each key challenge and explore how
research in this area has evolved in recent years.
The diverse range of IoT applications has varying requirements, such as stringent
latency, unbiased transmissions, static or large mobility devices, and small or high
volumes of data. Therefore, only some approaches can cater to all IoT applications. In
such cases, Low-Power Wide-Area Networks (LPWANs) are becoming the preferred
option for many IoT use cases (Masoudi et al. 2021). A Low-Power Wide-Area
(LPWA) wireless IoT radio access network faces four Performance Indicators (KPIs)
inconsistencies: Coverage Area A, Battery Lifetime, Device Capacity, and Estimation
Cost. Traditional cellular networks cannot meet these KPIs. As IoT devices started
to be deployed in large numbers, various surveys were conducted to address these
issues (Langat and Musyoki 2022; Singh et al. 2021a; Amodu and Othman 2018).
Numerous surveys have identified significant inefficiencies in LPWA networks and
emphasized critical research directions. One of the primary concerns highlighted in
most research is the high number of collisions in the random-access channel (RACH)
during the RA process. As a result, several surveys have been conducted to address
286 H. Rajab and T. Cinkler

this issue (Andrade et al. 2018; Althumali and Othman 2018; Kafi 2021) concen-
trated mainly on the RA process, and the consequence of IoT traffic on HTC. At that
time, IoT devices were mostly recognized of lower priority and importance corre-
sponded to HTC devices. Recently, deploying IoT devices is massively increasing
over cellular networks, recent surveys (Mahmood et al. 2020; Wu et al. 2020; El-
Tanab and Hamouda 2021) exposed additional issues and recognized new research
directions.
In recent years, the research community has recognized the equal importance of
IoT devices compared to HTC devices due to their significant development. As a
result, several recent surveys have been conducted to address this issue (Al-Dulaimi
et al. 2018; Li et al. 2021; Suma 2021). Recent research has focused on addressing
issues affecting IoT and HTC devices in 4G and upcoming 5G networks. One
proposed solution is the Enhanced Access Barring (EAB) scheme, which dynam-
ically adjusts the preventing parameters to balance the number of collisions and
network access delay for both devices (El-Tanab and Hamouda 2021; Vidal et al.
2019; Bui et al. 2019; Tello-Oquendo et al. 2018; Zhan et al. 2021; Leyva-Mayorga
et al. 2019; Haile et al. 2021; Singh et al. 2021b). Nonetheless, few research has
focused on the broadcast transmission functionality and network utilization, and
energy consumption for IoT devices. Previous suggestions aimed to improve the
efficiency of multicasting transmissions by modifying the Modulation and Coding
Scheme (MCS) used (Zuhra et al. 2019; Fuad et al. 2021; Rinaldi et al. 2020; Chen
et al. 2020). As the number of devices using multi-cast services increased, previous
schemes were found to need to be more sufficient in providing satisfactory services
without affecting unicast traffic or adding significant processing at the base station. As
a result, parallel research was conducted to improve the quality of service experienced
by users.
In Ghandri et al. (2018), The authors distinguish between two types of services:
bandwidth-intensive streaming services and file delivery, while in Park et al. (2018)
and Guangzhi (2021), the devices are categorized into different groups based on the
services they receive. The authors in Guangzhi (2021) users were categorized into
groups based on the feedback received about the channel quality. Similar approaches
have been proposed in related works, where devices are categorized into groups
based on their experienced Quality of Service (QoS) (Zuhra et al. 2019; Chen et al.
2021; Li et al. 2018; Gong et al. 2022; Saily et al. 2019).
Since the advent of the IoT era, energy conservation has been recognized as a
crucial objective for both device and network aspects in current and future cellular
networks. Several research studies (Liu et al., 2019; Tang et al., 2020; Ferranti et al.,
2019; Jahid et al., 2019) have highlighted the challenging issues and addressed
various research directions, while other research (Uppal and Gangadharappa 2021;
Pham et al. 2020) Initial research on energy consumption focused on the network
side, with efforts to investigate proposed energy-saving practices and approaches and
identify suitable parameters based on the development prospects of both the network
and devices. Reducing the energy consumption of both the network and the devices
has been a crucial goal since the beginning of the IoT era. Initially, most research
on energy consumption focused on the network side, aiming to reduce the BS’s
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 287

energy consumption due to the large number of devices they had to serve simulta-
neously. Several parallel research areas followed, which can be broadly divided into
the following categories:

1. Resource allocation schemes

Researchers have investigated various transmission strategies and resource allocation

schemes that depend on the transmission models of the devices (either HTC or IoT)
and the total traffic load in the cell (Wu et al. 2015; Hou et al. 2020).

2. Dynamic adaptation of transmission parameters

These proposals (Bonnefoi et al. 2019; Jahid et al. 2021; Ozyurt et al. 2021; Lassoued
and Boujnah 2020; Lv et al. 2020) aim to optimize the transmission and operation
parameters of the BSs dynamically to minimize their energy consumption by utilizing
on/off switching and irregular transmission schemes.

3. Collision mitigation methods

These approaches aim to reduce the energy consumption of BSs by mitigating inter-
ference from different transmissions and preventing the retransmission of previous
messages. Several approaches have been proposed in this area, as highlighted in
Khazali et al. (2018), Nikjoo et al. (2018) and Ghosh (2020).
With the massive increase in device numbers, the research community began to
focus on the energy consumption of the devices. This led to the development of
many parallel directions in research. Several works (Chang and Tsai 2018; Sehati
and Ghaderi 2018; Verma et al. 2019, 2018; Bithas et al. 2019; Li and Chen 2019;
Mughees et al. 2021; Sanislav et al. 2018) aimed to optimize the Discontinuous
Reception (DRX) configuration requirements of IoT devices to raise the sleeping
period to minimize the energy consumption.
Several studies (e.g., Sanislav et al. 2018; Al Homssi et al. 2018; Chen et al. 2018;
Malik et al. 2018; Wang et al. 2020; Tsoukaneri et al. 2020; Liang et al. 2018) have
proposed optimizing transmission parameters to reduce the energy consumption of
IoT devices during operation, by adjusting settings such as duty cycles or transmission
numbers (Himeur et al. 2020). Another direction in reducing energy consumption
of IoT devices is optimizing resource allocation and data transmission parameters,
such as adjusting the data rate (Chen et al. 2018; Malik et al. 2018). Most energy-
related research hasn’t focused on cellular network technology in recent years. As
a result, the unique characteristics of individual devices were not considered, and
some studies attempted to establish general models for energy consumption in IoT
devices (Tsoukaneri et al. 2020; Finnegan and Brown 2018; Azoidou et al. 2018;
Sadowski and Spachos 2020; Duhovnikov et al. 2019; Lan et al. 2019).
As a result, various works such as Finnegan and Brown (2018), Andres-
Maldonado et al. (2017), Yeoh et al. (2018), Lauridsen et al. (2018), Bello et al.
(2019), Soussi et al. (2018) and Sinha et al. (2017) have focused on evaluating the
impact of NB-IoT technology on energy consumption, examining its distinct oper-
ating modes and associated energy costs. In addition, Yeoh et al. (2018) conducts
288 H. Rajab and T. Cinkler

experimental research on the energy consumption of a commercial prefab NB-IoT

module for necessary communication services.

Narrowband-Internet of Things (NB-IoT)

NB-IoT is a Low Power Wide Area Network (LPWAN) radio technology licensed
and designed for enhanced indoor coverage for many low-cost, low-capability, and
low-power IoT devices. It eliminates dual connectivity and mobility features, further
reducing device costs.
Currently, two significant cellular IoT technologies are NB-IoT and LTE-M, which
target IoT use cases.
NB-IoT is designed to cater to low-cost Machine-Type Communication (MTC)
UEs with lower power consumption and higher coverage area than conventional
enhanced Mobile Broadband (eMBB) UEs. This is achieved by utilizing a small
portion of the spectrum, a distinct radio interface design, and simplified LTE network
functions. NB-IoT is a new 3GPP radio-access technology that is partially backwards
compatible with previous generations of cellular networks, meaning existing devices
cannot immediately use it. NB-IoT has been designed to be backwards compatible
with previous generations of cellular networks, leveraging the existing physical layer
design to a great extent for coexistence with legacy designs (Wang et al. 2017).
NB-IoT physical channels utilize the exciting cellular network to extensive
coverage that allows seamless coexistence and interoperability. NB-IoT is a half-
duplex technology and supports OFDMA transmissions in the downlink and
SCFDMA transmissions in the uplink, similar to 4G. The technology requires a
minimum channel bandwidth of 180 kHz, equivalent to one Physical Resource
Block (PRB). This means the UE does not need to listen to the DL while trans-
mitting in the UL and vice versa, regardless of the deployment mode. Figure 13.2
illustrates the design of NB-IoT subframes, which support half-duplex operation
and use OFDMA transmissions in the downlink and SCFDMA transmissions in the
uplink. The technology requires a minimum channel bandwidth of 180 kHz, equiv-
alent to one Physical Resource Block (PRB). The physical channels specified in the
NB-IoT standard include the Narrowband Physical Broadcast Channel (NPBCH),
which is used for broadcasting master information for regularity access (i.e., Master
Information Block or MIB), the Narrowband Physical Downlink Control Channel
(NPDCCH) for uplink and downlink scheduling information, the Narrowband Phys-
ical Downlink Shared Channel (NPDSCH) for downlink dedicated and standard data,
the Narrowband Physical Random-Access Channel (NPRACH) for uplink dedicated
and standard data, and the Narrowband Physical Uplink Shared Channel (NPUSCH)
for uplink data. The NPUSCH channel has two formats: NPUSCH format 1 for UL
data transmissions and NPUSCH format 2 for Hybrid Automatic Repeat Request
(HARQ) feedback for NPDSCH.
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 289

Fig. 13.2 NB-IoT in-band physical channels time multiplexing (Rastogi et al. 2020)

Power Saving Features

To ensure an extended battery life of more than 10 years on a single battery charge,
NB-IoT employs two power-saving techniques:
1. The Power Saving Mode (PSM)
2. The extended Discontinuous Reception (eDRX)
Both approaches enable the UE to enter a power-saving mode in which monitoring
for paging/scheduling information is not required.
1. PSM: The Power Saving Mode (PSM) in NB-IoT allows devices to enter a deep
sleep mode by disconnecting from most of their connections while remaining
connected to the network, which can be seen in Fig. 13.3. This mode allows
the device to save power while not connected to the network, but still wake up
whenever necessary to send data. The PSM technique is specifically designed to
help IoT devices conserve battery power and potentially achieve a battery life of
over 10 years.
PSM is a power-off mode that keeps the device connected to the network,
according to the 3GPP TS 23.682 specification. Curiously, the PSM mode seemed
in 3GPP specifications earlier than the NB-IoT in 3GPP Release 12. In PSM, the
device turns to a sort of power-off mode for a suitable amount of time. If the
device needs to transmit data, it can wake up without required to register in the
network and the necessary signaling.
290 H. Rajab and T. Cinkler

Fig. 13.3 Power saving mode (PSM)

2. eDRX: NB-IoT employs an extended discontinuous reception (eDRX) tech-

nique that puts devices in an idle mode where they do not receive radio signals
for a specific period. This allows the devices to conserve power and prolong
battery life. Periodically, the devices wake up to receive paging messages from
the network and check for incoming information before returning to deep sleep
mode. The period of intermittent eDRX reception in the NB-IoT mode ranges
from 20.48 to 10485.76 s, with the eDRX mode allowing the receiving path of
the device to remain shut off for a more extended period. Including eDRX in
the 3GPP Release, 13 specifications enable an additional power-saving mode for
IoT devices.
In summary, after the Active Timer expires, the UE switches to PSM mode,
completely disconnecting the radio and only maintaining a primary oscillator for
time reference. In PSM, energy consumption is similar to the power-off state.
eDRX is a technique that prolongs the sleep time of the I-DRX. When using
eDRX, an active phase is controlled by a Paging Time Window (PTW) timer
during each eDRX cycle, where the UE can be reached using I-DRX cycles,
followed by a sleep phase for the rest of the eDRX process (Fig. 13.4). This
cycle continues until the Active Timer expires.

Methodology

In a computer system, all components require energy to function. While desktop

computers have Power Supply Units (PSUs), laptops typically rely on batteries.
This chapter explicitly focuses on how LPWA technologies optimize power usage,
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 291

Fig. 13.4 Extended discontinuous reception (eDRX)

particularly NB-IoT. Micro-controllers and sensors in LPWA networks are small

computer systems prioritizing energy consumption optimization to achieve longer
battery life rather than performance analyses. While components utilize power to
perform computations, some energy dissipates from the system. This chapter will
provide a basic understanding of energy (E) and power (P) concepts and how they
relate to consumption.
To discuss the concept of energy in computer systems, it is essential to understand
the two primary forms of energy—potential and dynamic. The law of conservation
of energy states that the total energy in a closed system remains constant and cannot
be created or destroyed; instead, it transforms into other forms of energy. Computer
systems change the energy required to perform tasks into different shapes, mainly
heat. Energy is also measured as the amount of work done on an object per unit of
time, referred to as the energy consumption rate. The primary parameters used to
measure energy in computer systems include voltage (V), current (A), power (W),
energy (Wh), and time (s).
A fundamental formula for calculating power will be used and modified to
compute these factors. Equation 13.1 is used to calculate the average power by
dividing the energy by the time elapsed:

E
P= (13.1)
t
292 H. Rajab and T. Cinkler

To calculate power, we need to measure the voltage provided by a constant source

like a battery and the current flowing through it. This can be done using the following
equation, which is a modified form of Ohm’s law:

P(t) = V (t) × I (t) (13.2)

It is important to note that power consumption can be optimized by controlling

either the voltage, current, or both. In the case of battery-powered devices, reducing
the voltage or current can help to extend the battery life. However, this may come at the
cost of reduced performance or functionality. Therefore, finding the optimal balance
between power consumption and performance is crucial for designing efficient and
effective computer systems.
When using a computer system, the energy consumed can be calculated by deter-
mining the power usage over a specific period. Equations 13.1 and 13.2 can be used
to derive a function that calculates the energy consumed by a computer system based
on the voltage, current, and time elapsed. This can be expressed as:

(t2 (t2
E= V (t) × I (t) × dt = P(t) × dt (13.3)
t1 t1

Power Management in 3GPP Standard

LPWA technologies are designed to optimize power usage and ensure a longer device
battery life. In the 3GPP standard, power management techniques sustain low energy
consumption while maintaining a reliable connection. Some of these techniques
include:
• Low power mode allows devices to enter a deep sleep state while still connected
to the network, thereby conserving power.
• Lightweight MAC protocols: These protocols are designed to be simple and
efficient, reducing the energy required for communication.
• Topology: The topology of LPWA networks is optimized to reduce the energy
required for communication by using fewer hops and minimizing interference.
• Utilization of more complex base stations: By using more complex base stations,
LPWA networks can achieve better coverage and reduce the energy required for
communication.
To conserve power in LPWA technology, User Equipment (UE) does not require
continuous data transmission. Instead, it wakes up from sleep mode to send requested
data and utilizes power-hungry components for a short time. Lightweight MAC proto-
cols are also needed to reduce complex overhead for LPWA UEs. Network topology
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 293

options include Mesh topology, commonly used in standard cellular networks and
WLAN. UEs should aim to connect directly to the base station to avoid unnecessary
jumps, which can improve battery life. In 3GPP standardized technologies, only the
user can initiate the low power mode. Discharging unnecessary operations on base
stations can further extend the battery life of UEs.

Low Power Mode

LPWA technologies, including cellular networks, use a low-power mode to optimize

power consumption and extend battery life. This mode involves powering down heavy
elements like the processor. The low-power mode can be implemented differently,
depending on the application. For example, a device that only transmits information
using the uplink can be scheduled to send information twice a day or manually trigger
transfer messages. If the device can receive notifications through the downlink, it
must listen to the network for these messages. There are several ways to achieve this,
and the most suitable approach depends on the use case and how often the device
wakes up from low-power mode. If the device periodically transmits messages, it
can simultaneously listen for messages on the downlink.
In eMTC and NB-IoT, the low power mode is implemented differently, but both
use power-efficient techniques like PSM and eDRX. The difference between these
techniques is that eDRX allows the modem to listen to incoming signals, whereas
PSM requires the modem to wake up to send data before receiving any data. Although
energy-efficient communication is crucial for the successful deployment of MTC
over existing cellular networks, there needs to be more research focused on energy-
efficient uplink MTC scheduling.
Algorithm 13.1 illustrates the procedure to apply the Prediction Energy Saving
Technique (PEST) on the User Equipment (UE) side.
To improve energy efficiency, the UE in cellular networks can store a
prescheduling command transmitted via a narrowband physical uplink shared
channel (NPUSCH) and check it when an uplink packet occurs. The UE follows the
legacy scheduling request procedure if there is no prescheduling request. However,
if there is a prescheduling request, the UE delays the uplink packet transmission until
the scheduled time without triggering the scheduling request procedure.
In some cases, there may be a radical change in traffic arrival, such as when
some micro-BSs are turned off to save energy during low-traffic periods. This
situation requires neighboring BSs to cover the coverage areas of the turned-off
BSs, which is called cell zooming. During low-traffic periods, the density of active
BSs decreases, and communication distances increase. Legacy UEs, such as smart-
phones, are designed for daily recharging and are more efficient in such patterns.
This proposed algorithm will be evaluated using analytical and simulation models.
294 H. Rajab and T. Cinkler

Algorithm 13.1 Proposed UE procedure in an NB-IoT network

Battery Lifetime Estimation

We adopt a methodology similar to that used to assess the UE battery life by

measuring energy consumption. Our study considers a smart utility sensor, which
sends periodic uplink reports with a predetermined inter-arrival time (IAT) as per the
traffic profile. Before the start of periodic reporting, the UE needs to re-establish the
RRC connection and thus perform the CP procedure.
We divided the battery lifetime approximation into four phases for modeling the
periodic traffic pattern:
• P1: The UE wakes up from Power Saving Mode (PSM), establishes the RRC
connection, and transmits data using the CP procedure.
• P2: The UE continuously monitors the Narrowband Physical Downlink Control
Channel (NPDCCH) until the RRC connection is released.
• P3: The UE utilizes extended/enhanced Discontinuous Reception (eDRX) until
the Active Timer expires.
• P4: The UE enters sleep mode using PSM until the next transmission period
begins.
To estimate the energy consumption for transferring one UL report, denoted as
E report , we used a similar methodology to the one described in. This method assumes
a smart utility sensor and periodic UL reporting with a predefined Inter-Arrival Time
(IAT). Before the periodic reporting, the UE must reestablish the RRC connection,
which involves performing the CP procedure.
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 295

E report = E conn + E rel + E idle + Pstandby + Tsleep (13.4)

Tsleep = IAT − Tconn − Trel − Tidle (13.5)

The energy consumption for transferring one UL report, E report , was estimated
using a similar methodology. As described above, the four phases for modeling the
periodic traffic pattern were P1, P2, P3, and P4. The energy consumed in joules
within the phases P1, P2, and P3 is denoted as E conn , E rel , and E idle , respectively.
Pstandby represents the average power consumption in PSM, and T conn , T rel , T idle , and
T sleep represent the duration in seconds of the phases P1, P2, P3, and P4, respectively.
Finally, the energy consumed per day, denoted as E day , and the battery lifetime in
years indicated as Blife can be determined as follows:

Dday
E day = × E report (13.6)
IAT
BatC
Blife = E day
(13.7)
3600
× 365 × 25

In our simulation, we consider periodic UL reports as UDP packets with 50 B of

payload and a battery capacity of BatC = 5 Wh (Wang et al. 2017). We use the value
of Dday to represent the duration of 1 day in seconds.

Results

This section presents the validation results of our proposed analytical NB-IoT model.
We used Eqs. 13.1, 13.2, and 13.3 to calculate the modules’ energy consumption and
average power. The validation was performed based on two metrics: battery lifetime
and latency for performing Control Plane (CP) optimization. Our proposed algorithm
aims to reduce the total energy consumption of NB-IoT devices by exploiting their
lack of mobility and minimizing the number of costly and unnecessary procedures.
The module was tested with a voltage of 3.7 V and a signal strength of −75 dBm.
The energy consumption and average power were calculated based on the voltage
and current for a particular period. It should be noted that the module is still under
development.

PSM

Figure 13.5 displays the results of a 1-h Power Saving Mode (PSM) test where the
system only woke up from sleep once. The initial peak in the current was measured
296 H. Rajab and T. Cinkler

Fig. 13.5 1-h PSM test

Table 13.1 PSM active and

Average working current 101.498 mA
sleep
Average working power 375.544 mW
Average standby current 143.428 µA
Average standby power 530.684 µW

before the system entered sleep mode for the first time. It is worth noting that the
module used in the test is still under development.
Furthermore, we performed Timer and Button analyses, looking at the cycles
individually to achieve better results for both modes. Table 13.1 shows the results of
these analyses and is illustrated in Fig. 13.6, indicating a significant decrease in both
current and power when beginning the sleep mode.

eDRX

Figure 13.7 displays the results of a 20-min eDRX test, where a request was sent to
the system at the beginning of the analysis, resulting in the first spike in current. This
spike reached a peak of over 0.25 A. Following this, the system woke up periodically
to listen to the downlink, with an average current of approximately 0.2 A. During
idle cycles, the system was in sleep mode, with the current varying between 0.1
and 0.15 A. Figure 13.8 shows multiple spikes in the current readings during each
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 297

Fig. 13.6 PSM active and sleep cycles

Fig. 13.7 20 min eDRX test

cycle, which may be because the current is not measured from the system alone, as
applications are also running on the application processor. The results for each cycle
are provided in Table 13.2.

Discussion

This section describes a comprehensive evaluation of energy consumption in NB-

IoT devices based on measurements, focusing on identifying optimization targets for
network operations. Furthermore, we proposed an energy consumption model that
298 H. Rajab and T. Cinkler

Fig. 13.8 eDRX active, idle, and sleep cycles

Table 13.2 The result of

Average working current 183.313 mA
each separate cycle
Average working power 678.257 mW
Average standby current 144.356 mA
Average standby power 534.118 mW
Average sleep current 129.786 mA
Average sleep power 480.206 mW

we utilized in simulation experiments and our empirical observations to estimate the

battery requirements for NB-IoT devices to achieve the desired battery life.
Estimating an NB-IoT device’s lifetime can be accomplished by using the battery’s
capacity and network configurations. In the case of Power Saving Mode (PSM), we
can calculate the battery life using four different timers: 1 h, 1 day, 1 week, and
1 month (30 days). Table 13.3 provides each cycle’s average power and respective
times. To determine the battery’s lifetime, we divide the battery’s energy by the
average power according to the following formula:

5 Wh
Lifetime = (13.8)
Ptot
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 299

Table 13.3 Lifetime of 5 Wh battery (PSM)

Once/hour Once/day Once/week Once/month
Operator A 2528 h 40,463 h 93,449 h 112,239 h
NB-IoT (105 days) (1686 days) (3894 days) (4677 days)
Operator B 3443 h 49,351 h 99,301 h 114,060 h
NB-IoT (143 days) (2056 days) (4138 days) (4753 days)
Operator A 398 h (17 days) 8614 h (356 days) 42,130 h 83,905 h
eMTC (1755 days) (3496 days)

Table 13.4 Average power

Average power (mW) Time (s)
summary (eDRX)
Operator A NB-IoT 210.729 11.41
(active)
Operator A NB-IoT 96.088 15.36
(idle)
Operator A NB-IoT 59.039 148.56
(sleep)

Table 13.3 illustrates a 5 Wh battery life when utilizing Power Saving Mode (PSM)
with NB-IoT and eMTC. As anticipated, the battery life is considerably shorter for
eMTC compared to NB-IoT. The battery life varies from 17 to 3496 days, depending
on the duration of the sleep mode.
Table 13.4 depicts the power usage for each eDRX cycle and their respective times
for the same battery with 5 Wh. Similar to PSM, we can utilize the same equations
to estimate the battery’s lifetime.

Conclusions

The proliferation of the Internet of Things (IoT) has revolutionized various domains
of our lives by extending network connectivity to everyday objects, enabling them to
communicate with each other without human intervention. IoT devices have a wide
range of applications, including digital health, smart homes, autonomous driving,
and industrial automation, with new applications being developed daily.
Cellular networks have emerged as a strong candidate to support IoT devices, mainly due
to their extensive deployment, large coverage area, and varying data rates. However, tradi-
tional cellular networks were historically designed to help high-throughput communication
(HTC) devices, exhibiting considerably different traffic patterns than IoT devices, leading
to inefficiencies at both the network and device levels. These challenges are not limited to a
single area but span various operational areas of cellular networks, such as the connection
establishment process, network resource utilization, and device energy consumption.
LPWAN technology selection for IoT applications should be determined case-by-case,
considering the device’s data transmission requirements, desired lifetime, and access to
300 H. Rajab and T. Cinkler

a charging source. PSM may be more appropriate for devices that only need to send data
infrequently, while eDRX may be more suitable for devices that listen to incoming informa-
tion frequently. Considering the power-saving feature utilized is crucial since neither PSM
nor eDRX is a one-size-fits-all technology.

The current chapter focuses on the challenges IoT devices face in cellular
networks, focusing on their unique communication patterns and requirements. To
address these challenges, the chapter presents an analytical model that enables esti-
mating the energy consumption of an NB-IoT device. The results obtained from
the analysis indicate that the use cases for eDRX and PSM differ and that devices
that need to listen to the downlink frequently may require more frequent battery
recharging.

Future Studies

The rising traffic generated by emerging IoT applications presents a significant chal-
lenge for cellular networks. Machine-to-Machine (M2M) communications, known
as MTC, are crucial for current and future cellular networks. Therefore, cellular
networks must continually evolve and adapt to new requirements. NB-IoT is an
example of this evolution as it leverages LTE technology to provide IoT support.
However, new technologies like NB-IoT and eMTC still present unresolved research
issues and uncertainties. Further research is needed to address these challenges and
support the growth of IoT in cellular networks.
Various unknown factors, such as range and configurations, make it difficult to
provide accurate details on the energy consumption of NB-IoT devices. Additionally,
no experiments were conducted to analyze how the amount of data sent affects energy
consumption, an area for future development.
Based on the findings of this chapter, there are several open issues and potential
improvements that need to be addressed, including:
1. Expanding the NB-IoT model proposed in this study to analyze the enhanced
Discontinuous Reception (eDRX) and PSM performance.
2. Extending the analysis to cover extended coverage areas.
3. Conducting experimental measurements of battery lifetime, considering the non-
ideal characteristics of actual batteries, such as self-discharge and temperature
variations.
4. Investigating alternative antenna schemes that can improve the Signal-to-Noise
Ratio (SNR) without increasing the User Equipment (UE) complexity.

Acknowledgements This research is supported by Tempus Public Foundation, Stipendium

Hungaricum Scholarship Programme and High Speed Networks Lab, Department of Telecom-
munications and Media Informatics, Budapest University of Technology and Economics. “This
work was supported by the CHIST-ERA grant SAMBAS (CHIST-ERA-20-SICT-003) funded by
FWO, ANR, NKFIH, and UKRI.”
13 Enhanced Energy Efficiency and Scalability in Cellular Networks … 301

Funding This work was supported by the Ericsson—BME 5G joint research and cooperation
project, partly funded by the National Research, Development and Innovation Office, Hungary with
project number 2018-1.3.1-VKE-2018-00005.

Declaration Availability of Data and Materials The data used to support the findings of this
study are available from the corresponding author upon request.

Competing Interests The authors declare that they have no competing interests.

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Correction to: Wireless Backhaul
Optimization Algorithm in 5G
Communication
Astha Sharma, Mukesh Soni, Abhaya Nand, Suryabhan Pratap Singh,
and Sumit Kumar

Correction to:
Chapter 4 in: B. Bhushan et al. (eds.), 5G and Beyond,
Springer Tracts in Electrical and Electronics Engineering,
https://doi.org/10.1007/978-981-99-3668-7_4

The original version of the book was inadvertently published with incorrect author
name and affiliation in chapter 4. The erratum chapter and the book have been updated
with the changes.

The updated version of this chapter can be found at

https://doi.org/10.1007/978-981-99-3668-7_4

© The Author(s) 2023 C1

B. Bhushan et al. (eds.), 5G and Beyond, Springer Tracts in Electrical
and Electronics Engineering, https://doi.org/10.1007/978-981-99-3668-7_14