Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Networks

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

Hiu Fai CHAN

BBA in Information Systems (City University of Hong Kong)

MSc in Information Technology (The Hong Kong Polytechnic University)

School of Engineering

College of Science, Engineering and Health

RMIT University

July 2020
Declaration

I certify that except where due acknowledgement has been made, the work is that of the
author alone; the work has not been submitted previously, in whole or in part, to qualify for
any other academic award; the content of the thesis is the result of work which has been
carried out since the official commencement date of the approved research program; any
editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics
procedures and guidelines have been followed

Hiu Fai CHAN

20 July 2020
 Acknowledgements

This dissertation would not have been possible without the guidance and the help
of several individuals who contributed and extended their valuable assistance in the
preparation and completion of my PhD study.

Firstly, I would like to express my sincere gratitude to all my RMIT supervisors: Dr.
Akram Al-Hourani, Dr. Karina Gomez, and Dr. Heiko Rudolph, for the continuous
supports of my PhD study and related research, for their patience, motivation, and
immense knowledge. Their guidance helped me in all the time of research and writing
of this Thesis. Although Dr. Heiko Rudolph is now rest in peace in the heaven, his
heartfelt supports encourages me to strive for my PhD study, and these are all deep
in my memories. Without all RMIT supervisors precious supports, it would not be
possible to conduct this research.

Besides my supervisors, I would like to thank the rest of my RMIT academics in

review panel and Thesis committee: Dr Jidong Wang, and Dr Shuo Li, for their in-
sightful comments and encouragements, but also for the meaningful questions which
incentivize me to widen my research from various perspectives. Their immense knowl-
edge, motivation and patience have given me more power and spirit to excel in the
research writing.

Last but not least, I am grateful to my family and friends for their supports and
encouragements during these years of study, especially for my wife who takes care of
our little kid during the years.

Thank you all very much!

 Abstract

The existing electrical power grid is undergoing a revolution and transformed as a

smart grid. With the integration of electrical and communication infrastructure,
the latest information technology is applicable for the existing electrical power grid.
Smart grid is one of the application in Internet of Things (IoT). It can enhance the
data exchange by full duplex communication, data acquisition, automatic metering
infrastructure, renewable energy integration, distribution automation and complete
monitoring and control of entire power grid.

Wireless Sensor Networks (WSNs) are one of the key enabling technologies that
powered the IoT evolution. WSNs provide the cells for data communications among
different IoT applications. These wireless sensors act as the access points for IoT
to enable energy-efficient and low costs connections of all the things. WSNs play
a major role in data communications for applications such as home, health care,
environmental monitoring, smart grids, transportation and many others. WSNs are
used in IoT applications and should be secured and energy efficient in order to provide
highly reliable data communications. Due to the constraints of energy, memory
and computational power of the WSN nodes, clustering and routing algorithms are
considered as powerful energy efficient approaches for resource-constrained WSNs.

One objective of this Thesis is to be used as tutorial for the comparison of the most
relevant baseline hierarchical routing protocols. Thus, we present a comprehensive
study of various routing algorithms and their effects on the WSN performance. Ad-
ditionally, we further discuss and analyze the cluster formation method, hierarchical
structure, and leader selection criteria in a more comprehensive way. Furthermore,
we consider and review the energy consumption by (i) the sending nodes and (ii)
cluster heads separately. Based on our analysis, we provide justification for ranking
the best state-of-the-art routing techniques according to the optimization metrics
such as remaining energy, distance to base station, and density of nodes, etc. We
further provide conclusions of the energy burden and limitations of different routing
algorithms.

Low-energy adaptive clustering hierarchy (LEACH) is a traditional hierarchical pro-

tocol aiming to reduce the energy consumption in creating and forming a cluster.
However, its random method to select cluster head leads to uneven distribution of
cluster heads and high failure transmission rate. Thus, this Thesis also discusses
and compares different parameters used in cluster formation for different enhanced
LEACH versions in order to provide the factors to be considered in forming a cluster
and routing. After discussing the main constraints and design considerations of rout-
ing protocols, this Thesis focuses on the design and implementation of multi-criteria
energy efficient routing protocols. Particular attention is given to the cluster head
selection, which is based on different criteria and parameters in order to reduce un-
necessary redundancy while conserving the whole WSN energy resources. Thus, we
describe in detail the routing protocol principle for saving and balancing energy in
the whole WSN. For multi-hop routing, we adopt a flexible and adaptive approach.
It is proposed to send data to the farthest next cluster head within the energy effi-
cient distance. If there is no next cluster head available within the energy efficient
distance, a temporary routing node can be selected for communication.

We also discuss several security aspects to be considered in routing protocols. In

order to probe the effectiveness of the proposed routing algorithm, we explain how
the simulations of the proposed routing protocols implemented as well as scenarios
and simulation parameters.

In order to verify our results, we utilize the well-known open-source network sim-
ulator Omnet++. The characteristics and behaviours of sensor communications are
well-defined in the simulation model which allows the simulations to operate similarly
to a real situation. The performance of various hierarchical routing algorithms for
WSNs are compared using different metrics. Our simulations show that the proposed
clustering and routing algorithm can result in a higher successful data delivery rate
while maintaining a lower energy consumption for cluster formation and data trans-
missions at the same time, which are suitable for the applications of WSNs in IoT.
The results presented in this Thesis demonstrate that the choices made in the design
of the proposed energy efficiency routing protocols improve the lifetime of the whole
WSN, while keeping a simple implementation. The novelty of the contribution in
this Thesis is the design of clustering and routing algorithms to balance the energy
loading of all sensor nodes while maintaining high successful data transmission rate.
This is achieved by developing smart and adaptive clustering and routing algorithms
in WSN. The algorithms can also adapt to the changes of environments and be suit-
able for various scales of networks. Finally, we conclude this Thesis pointing out the
most relevant challenges and future research trends of WSN.
Contents

1 Introduction 1
1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Research Questions and Methodology . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Research Question 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Research Question 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.3 Research Question 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Overview of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Publication List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Literature Review and Comparative Analyses 9

2.1 Routing Protocols Surveys: A Review . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 WSN Routing Protocols Classification . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Hierarchical Routing Algorithms Background . . . . . . . . . . . . . . . . 15
2.3 Hierarchical Chain-Based Routing Algorithms . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Chain-Based Routing Algorithms Comparison . . . . . . . . . . . . . . . . 21
2.3.1.1 Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.1.2 Stability and Node Failure . . . . . . . . . . . . . . . . . . . . . 22
2.3.1.3 Suitability for Large Area Network . . . . . . . . . . . . . . . . . 22
2.4 Hierarchical Tree-Based Routing Algorithms . . . . . . . . . . . . . . . . . . . . . 23
2.4.1 Tree-Based Routing Algorithms Comparison . . . . . . . . . . . . . . . . 25
2.4.1.1 Centralized or Decentralized Organization . . . . . . . . . . . . . 25
2.4.1.2 Residual Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.1.3 Shortest Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Hierarchical Grid-Based Routing Algorithms . . . . . . . . . . . . . . . . . . . . 26
2.5.1 Grid-Based Routing Algorithms Comparison . . . . . . . . . . . . . . . . 28
2.5.1.1 Time Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5.1.2 Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.1.3 Shortest transmission distance . . . . . . . . . . . . . . . . . . . 30

v
 CONTENTS

2.6 Hierarchical Area-Based Routing Algorithms . . . . . . . . . . . . . . . . . . . . 30

2.6.1 Area-Based Routing Algorithms Comparison . . . . . . . . . . . . . . . . 33
2.6.1.1 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6.1.2 Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm . . . . 36
2.7.1 Latest Improvements on LEACH . . . . . . . . . . . . . . . . . . . . . . . 38

3 Energy Efficiency Routing Protocols: Design and Considerations 44

3.1 Design Considerations on Routing Protocols for WSN . . . . . . . . . . . . . . . 45
3.1.1 Criteria of a Robust Routing Algorithm for Wireless Sensor Network . . . 45
3.1.2 Constraints of Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . 47
3.2 Simple Energy Efficient Routing Protocol . . . . . . . . . . . . . . . . . . . . . . 48
3.2.1 Assumptions of Our WSN Model . . . . . . . . . . . . . . . . . . . . . . . 48
3.2.2 Cluster Formation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.3 Multi-Hop Routing Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3 Multi-criteria Energy Efficient Routing Protocol . . . . . . . . . . . . . . . . . . 56
3.3.1 Cluster Head Selection Method . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3.2 Routing Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . 62
3.4 Security Considerations for Energy-efficient Routing . . . . . . . . . . . . . . . . 65
3.4.1 WSNs Security Requirements . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.2 Data Encryption Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.4.3 Encryption Key Establishment and Management . . . . . . . . . . . . . . 68
3.4.3.1 System Initialization Stage . . . . . . . . . . . . . . . . . . . . . 68
3.4.3.2 Nodes Sending Message . . . . . . . . . . . . . . . . . . . . . . . 69
3.4.3.3 New Node joining . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.3.4 Leaving of a Normal Node . . . . . . . . . . . . . . . . . . . . . 70
3.4.3.5 Change of Cluster Head . . . . . . . . . . . . . . . . . . . . . . . 71

4 Performance Evaluation of Energy Efficiency Routing Protocols 72

4.1 Omnet++ Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.1.1 Castalia Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 Simulation Parameters and Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3 Baseline Performance Analysis of LEACH . . . . . . . . . . . . . . . . . . . . . . 76
4.4 Performance Analysis in Different Scales of Networks . . . . . . . . . . . . . . . . 77
4.4.1 Scenario 1: Small Scale Network . . . . . . . . . . . . . . . . . . . . . . . 78
4.4.2 Scenario 2: Medium Scale Network . . . . . . . . . . . . . . . . . . . . . . 79
4.4.3 Scenario 3: Large Scale Network . . . . . . . . . . . . . . . . . . . . . . . 81
4.4.4 Cluster Head Selection Analysis . . . . . . . . . . . . . . . . . . . . . . . . 87

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 CONTENTS

4.4.4.1 Random Cluster Head Selection . . . . . . . . . . . . . . . . . . 87

4.4.4.2 Cluster Head Selection By Remaining Energy . . . . . . . . . . . 87
4.4.4.3 Cluster Head Selection by the Centrality of Nodes . . . . . . . . 88
4.4.4.4 Sending to Base Station by Multi-hop . . . . . . . . . . . . . . . 88
4.4.4.5 Sending to Base Station Directly Without Joining Cluster . . . . 88
4.4.4.6 Sending to Next Cluster Head Through Temporary Routing Node 89
4.5 Performance Comparison of Different Hierarchical Algorithms . . . . . . . . . . . 89
4.5.1 Comparison Analysis with CCS . . . . . . . . . . . . . . . . . . . . . . . . 96
4.5.2 Comparison Analysis with EADAT . . . . . . . . . . . . . . . . . . . . . . 97
4.5.3 Comparison Analysis with ETR . . . . . . . . . . . . . . . . . . . . . . . . 97
4.5.4 Comparison Analysis with PANEL . . . . . . . . . . . . . . . . . . . . . . 97

5 Conclusions 99
5.1 Answering Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2 Challenges and Future Research Trends . . . . . . . . . . . . . . . . . . . . . . . 102

References 125

vii
List of Figures

2.1 Routing protocols classification of WSN. . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 Representation of Hierarchical Routing Protocol Strategies in WSN . . . . . . . . 17
2.3 Chain-based Routing Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Examples of Grid-based Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Area-based Routing Algorithms (LBDD and Ring Routing). . . . . . . . . . . . . 31
2.6 Area-based Routing Algorithms(Railroad and VLDD). . . . . . . . . . . . . . . . 31
2.7 Representation of LEACH routing protocol strategy (S=Sink, L=Leader). . . . . 36

3.1 Most relevant constrains of WSNs. . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.2 Cluster formation in a wireless sensor network . . . . . . . . . . . . . . . . . . . . 50
3.3 Selection of cluster head candidates (CH) . . . . . . . . . . . . . . . . . . . . . . 51
3.4 Procedure for joining the cluster in WSN. . . . . . . . . . . . . . . . . . . . . . . 53
3.5 Multi-Hop transmission with energy efficient distance. . . . . . . . . . . . . . . . 54
3.6 Flow chart of cluster formation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.7 Flow chart of cluster formation (Continued). . . . . . . . . . . . . . . . . . . . . 58
3.8 Flow chart of routing protocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.9 Selection of temporary routing node . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.1 Framework of castalia model implemented in Omnet++. . . . . . . . . . . . . . . 73

4.2 Snapshot of Simulation Environment in Omnet++. . . . . . . . . . . . . . . . . . 74
4.3 Comparisons of the number of alive nodes for 300 x 300 m network scenario. . . 78
4.4 Comparisons of the total remaining energy for 300 x 300 m network. . . . . . . . 79
4.5 Comparisons on successful delivery rate for 300 x 300 m network. . . . . . . . . . 79
4.6 Comparisons on number of alive nodes for 400 x 400 m network. . . . . . . . . . 80
4.7 Comparisons on total remaining energy for 400 x 400 m network. . . . . . . . . . 80
4.8 Comparisons on successful delivery rate for 400 x 400 m network. . . . . . . . . . 81
4.9 Comparisons on number of alive nodes for 500 x 500 m network. . . . . . . . . . 81
4.10 Comparisons on total remaining energy for 500 x 500 m network. . . . . . . . . . 82
4.11 CDF on total remaining energy for 500 x 500 m network. . . . . . . . . . . . . . 82

viii
 LIST OF FIGURES

4.12 Comparisons on successful delivery rate for 500 x 500 m network. . . . . . . . . . 83

4.13 CDF on successful delivery rate for 500 x 500 m network. . . . . . . . . . . . . . 83
4.14 PDF on successful delivery rate of Original LEACH for 500 x 500 m network. . . 84
4.15 PDF on successful delivery rate of Remaining Energy for 500 x 500 m network. . 84
4.16 PDF on successful delivery rate of Density for 500 x 500 m network. . . . . . . . 85
4.17 PDF on successful delivery rate of Remaining Energy and Density for 500 x 500
m network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.18 PDF on successful delivery rate of Multi-hop Shortest Distance for 500 x 500 m
network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.19 PDF on successful delivery rate of Different LEACH parameters for 500 x 500 m
network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.20 Comparison on number of alive nodes of different hierarchical routing algorithms
in 500 x 500 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.21 Comparison on total remaining energy of different hierarchical routing algorithms
in 500 x 500 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.22 CDF on total remaining energy of different hierarchical routing algorithms in 500
x 500 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.23 Comparisons on successful delivery rate of different hierarchical routing algorithms
in 500 x 500 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.24 CDF on successful delivery rate of different hierarchical routing algorithms in 500
x 500 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.25 PDF on successful delivery rate of CCS for 500 x 500 m network. . . . . . . . . . 93
4.26 PDF on successful delivery rate of EADAT for 500 x 500 m network. . . . . . . . 93
4.27 PDF on successful delivery rate of ETR for 500 x 500 m network. . . . . . . . . . 94
4.28 PDF on successful delivery rate of PANEL for 500 x 500 m network. . . . . . . . 94
4.29 PDF on successful delivery rate of Multi-hop within d0 for 500 x 500 m network. 95
4.30 PDF on successful delivery rate of Temp Routing Node for 500 x 500 m network. 95
4.31 PDF on successful delivery rate of different hierarchical algorithms for 500 x 500
m network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

ix
List of Tables

2.1 Review of routing protocols surveys. . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Comparison of hierarchical routing algorithms based on energy consumed by nodes
and leaders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Comparison of energy consumption of hierarchical routing protocols. . . . . . . . 18
2.4 Comparison of hierarchical chain-based routing algorithms. . . . . . . . . . . . . 21
2.5 Comparison of hierarchical tree-based routing algorithms. . . . . . . . . . . . . . 25
2.6 Comparison of hierarchical grid-based routing algorithms. . . . . . . . . . . . . . 28
2.7 Comparison of hierarchical area-based routing algorithms. . . . . . . . . . . . . . 33
2.8 Comparison of hierarchical routing protocols relevant features. . . . . . . . . . . 35
2.9 Comparison of latest improved LEACH algorithms for the cluster formation. . . 40

3.1 Model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.1 Simulation parameters at different levels. . . . . . . . . . . . . . . . . . . . . . . 75

4.2 Parameters used in delay transition matrix (state switching times in milliseconds). 75
4.3 Parameters used in transition matrix (power consumption between different states). 76
4.4 Mean, standard deviation and confidence interval of successful delivery rate com-
parison with different criteria of selecting cluster head with significance level =
0.05 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.5 Mean, standard deviation and confidence interval of successful delivery rate com-
parison with different hierarchical routing algorithms with significance level =
0.05 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

x
Chapter 1

Introduction

The Internet of things (IoT) is the network of connection of physical devices, vehicles, home
appliances, and other items embedded with electronics, software, sensors, actuators. This en-
ables these objects to connect and exchange data. Each thing is uniquely identifiable through its
embedded computing system but is able to inter-operate within the existing Internet infrastruc-
ture through IP addresses [1]. All sensing data are further processed and analyzed in the data
analyzing area. In recent years, IoT has been widely adopted to describe solutions developed
using different devices with computational capacity and connected to the Internet [2]. These
solutions can be applied in domains like health [3], agriculture [4], smart cities [5], industry [6],
logistics [7], among others domains.
Smart Grid is an automated and widely distributed energy generation, transmission and
distribution electrical power network. It is implemented as a full duplex communication network
with bidirectional flow of electricity and information. Smart grid performs as a closed loop
monitoring and control system to collect data and analyze the performance of the electrical
power system [8; 9; 10]. WSNs are used in various applications and stages in electrical power
systems: power generation, power transmission, power substation, power distribution, and power
consumption. In the power generation stage, generators are the main devices in the power
plants. So, flexibly deployable sensors are useful to collect generation data to ensure safety and
security [11]. Different types of sensors such as balance sensors, smoke sensors, and magnetic
field strength sensors are required to collect data and monitor the status of the power generators.
Such information can assist with fault detection, prevent accidents, and optimize the performance
of the power generation schedule. WSNs are especially useful for monitoring the operations of
transmission lines and substations, these areas are difficult and expensive to monitor by using
traditional methods [12]. Currently, the safety of huge number of power transmission towers
relies on regular inspections by patrol officers. This is time consuming, and whenever faults
or disasters occur, it is hard to obtain accurate data for analysis [13]. WSNs can be used on

1
 1.1 Motivations

transmission towers to monitor environmental conditions, such as wind direction and speed,
vibrations, temperature, humidity, pressure, etc.
Billions of physical devices are expected to be connected to the Internet in IoT [14; 15], and
this eventually leads to massive machine-type-communication (mMTC), a use case of the fifth
generation (5G) wireless access technology. In mMTC, wireless sensor networks (WSNs) can be
used to support IoT applications such as surveillance and monitoring. WSNs connect things to
the Internet through a gateway [16]. Therefore, it can be expected that a significant increase in
usage of WSN applications for mMTC.
Wireless sensor network (WSN) refers to a group of dedicated sensors for monitoring and
recording the physical conditions of the environment without a hard wire. And, the data are
mainly processed and centralized at the base station. WSN is just a subset and integral part
of the Internet of Things (IoT) paradigm which includes different technologies such as RFID,
cloud computing, middleware systems and end-user applications. Moreover, the wireless sensors
are not directly connected to the Internet. WSN technology is an essential component of IoT
because it consists of a collection of sensor nodes connected through wireless channels.
A lot of attention has been drawn to research on wireless sensor networks (WSNs) in the past
decades, which has underpinned and driven the more recent evolution of WSNs towards IoT [17].
The low cost wireless sensors are a resilient and effective distributed data collection technology,
and WSN are recognized as the key enablers for the Internet of Things (IoT). WSNs are widely
used for their sensing, wireless communications and computation capabilities. WSNs consist
of large numbers of low cost wireless sensor nodes using low power in places where traditional
networks cannot compete. Power constraint especially dominates the performance of a WSN.
WSNs differ from other traditional data communication networks in that sensors are densely
deployed, and nodes can be easily damaged often because of harsh environmental conditions.
In some deployments, the topology may change from time to time, requiring the links between
nodes to be reconfigured which may cause some instability and require more energy. For these
and other reasons, WSNs may be unstable in the field. Therefore, maintaining stable WSNs is
a challenging task which requires mature monitoring and control strategies appropriate to the
specific deployment.

1.1 Motivations
Wireless sensor nodes are usually powered by battery and nodes are required to run for long
periods of time without physical maintenance. Often, node sensors are difficult to access and it
would be difficult to change or recharge the energy source. Thus, one of the major challenging
objective is to use the energy of sensor nodes efficiently and effectively. WSNs have a wide range
of applications, including large scale networks, wherein up to tens of thousands of sensor nodes

2
 1.2 Research Questions and Methodology

can be deployed in smart grid [18; 19] , WSNs require well-defined, energy-efficient, and adaptable
routing algorithms. The power limitation of the WSN is mainly caused by the small physical size
of sensors, their batteries and the absence of a rechargeable energy supply [20; 21; 22]. In most
applications, a WSN may consist of hundreds or even thousands of sensor nodes. These nodes
have limited energy, low storage size, and narrow bandwidth for communication. Moreover, it
is usually difficult to replace or recharge batteries when they are sparsely deployed in remote
environments. Every node uses power for its sensor transducer, communication among other
sensor nodes and microprocessor computation. The energy required is much more than data
sensing and computation. In fact, most of the energy is consumed in data communications
between nodes. It is expected that the design of routing algorithm and protocols will entail
crucial decisions in managing the complicated WSN environment by balancing key parameters
so as to improve the robustness of networks. As an example a large number of sensor nodes are
required to establish a reliable data communication network between a Utility Company and its
customers. Such systems require efficient algorithms to maintain reliability of the WSNs, which
implies using the limited battery energy of the nodes in the most efficient way.
In most WSN applications, the sensor nodes are often deployed in an adhoc manner where
well-defined placement may not be practical or too costly. Once deployed, the wireless sensor
nodes must have the ability to self-organize and integrate into an efficient and reliable wireless
communication network [23]. WSNs should be able to provide reliable and secure communication
and control capabilities at low cost. A common use of WSNs is for collecting data in different
IoT applications because of their lower cost and deployment flexibility. WSNs are diverse with
many different proprietary and non-proprietary solutions [24; 25]. To apply a WSN effectively,
the characteristics and constraints of WSNs need to be fully understood. The most fundamental
constraint being energy usage [26]. Other common criteria for WSNs are efficiency in memory
storage, processing power and the resulting data throughput [27]. However, energy consumption
and the lifetime of the whole WSN are the most fundamental constraints, they are commonly
used to evaluate the merit of WSN network protocols and algorithms. A number of studies have
been conducted to investigate the issue of energy constraints of WSNs [28].

1.2 Research Questions and Methodology

Due to the relevance of routing in WSNs and their importance in the literature, we attempt to
present a comprehensive study of various routing algorithms and protocols and their effects on
the WSN performance, those protocols are classified in groups under chain, tree, grid or area
based network. As discussed later on Chapter II, surveys have been conducted on WSNs regard-
ing clustering and routing algorithms [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. However, we
identified gaps on the comparison points. As example, authors in [29] have done a performance

3
 1.2 Research Questions and Methodology

comparison of different atypical hierarchical routing protocols, but they have not discussed and
compared the parameters used in formation of clusters in a detailed and clear manner. There-
fore, in this Thesis, we further discuss and analyze the cluster formation method, hierarchical
structure, and leader selection criteria in a more comprehensive way. Furthermore, instead of
just analyzing the energy efficiency of different routing algorithms generally, we consider and
review the energy consumption by (i) sending nodes and (ii) leader nodes in a separate manner.
Then, conclusions of the energy burden and limitations of different routing algorithms are also
presented.
Additionally, in this Thesis, various hierarchical routing algorithms for WSNs are compared
and their performance is discussed for further analysis. Based on our analysis, we provide
justification for ranking the best state-of-the-art routing techniques according to the optimiza-
tion criteria. Additionally, since the well-known algorithm, LEACH [41], is also classified as a
hierarchical-based wireless sensor network, but it does not belong to one of the groups under
chain, tree, grid or area based network. This is because the cluster head in LEACH is selected
randomly from the whole region, and it is not limited and bounded by any grid or area. Besides,
the cluster head will send data to the base station directly with single hop transmission, it is
different with the chain and tree based transmissions. Therefore, this Thesis also discusses and
compares different parameters used in cluster formation for different enhanced LEACH versions
in order to provide the factors to be considered in forming a cluster and routing.
There is considerable research activity in the field of routing protocol of WSN due to the
versatility to sense, measure, monitor and track events. Numerous hardware and software ad-
vancements have resulted in compact and low cost sensor devices with significant communication
capabilities. Transforming sensors from simple measurement and detection devices to devices
able to adapt in response to environmental conditions. This open space to innovation specially
in the field of design and implement energy efficiency multi-criteria routing protocols that can
incorporate security levels. This Thesis focuses on the design and implementation of simple and
multi-criteria energy efficiency routing protocols. We discuss the cluster head selection based on
different criteria and parameters aiming to reduce unnecessary redundancy on data transmis-
sions, while conserving the whole WSN energy resources. We demonstrate that the cluster head
should be carefully selected to be active around coverage holes in order to ensure that the overall
lifetime of the network is maximised. Suitable selection of cluster heads can avoid the expansion
of damaged regions due to the lack of sensor node energy. By defining suitable criteria, the
performance of our proposed model performance is compared with relevant well know routing
algorithms.
Based on the constraints of energy efficiency of wireless sensor networks discussed before, it is
worth to investigate the causes, identify the key parameters that affect the energy consumption
for data communications. Furthermore, the main objective of this Thesis is to put forward novel

4
 1.2 Research Questions and Methodology

easy to implement routing protocol solutions. Therefore after deep literature review presented
in Chapter 2 the following questions are formulated:

1.2.1 Research Question 1

RQ1: What are the factors affecting the energy efficiency of WSN data commu-
nications?

Literature Review Main Points:

• LEACH [41] is the most-basic clustering protocol, it selects cluster head randomly,

• Other researchers proposed some improvements of LEACH based on: Energy Level, Dis-
tance, No. of clusters, Cluster Size and Area of Coverage,

Gaps:

• There is a lack of in-depth discussion of the different LEACH versions [42; 43; 44; 45; 46;
47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58],

• The parameters among different LEACH versions are difficult to compare,

• No sophisticated analysis of which factors are suitable for which circumstances has been
formulated,

Contributions:

• Parameters of different latest LEACH versions are identified and clearly compared,

• Parameters are combined to suit for various cluster formation situation,

• Our published paper [59] and section 2.7 in this Thesis has addressed the analysis of
different LEACH versions.

1.2.2 Research Question 2

RQ2: What are the props and cons of different hierarchical routing algorithms?

Literature Review Main Points:

• There are different hierarchical routing algorithms developed: Chain [60; 61; 62; 63],
Tree [64; 65; 66; 67], Grid [68; 69; 70; 71] and Area [72; 73; 74; 75],

Gaps:

5
 1.2 Research Questions and Methodology

• Lack of discussion and comparison on the parameters used in formation of clusters among
various hierarchical routing algorithms,

• Energy burden and limitations of dierent routing algorithms are not identified,

Contributions:

• Further discuss and analyze the cluster formation method, hierarchical structure, and
leader selection criteria in a more comprehensive way is presented,

• Review on the energy consumption by sending nodes and cluster head nodes is largely
discussed.

• Our published paper [59] and section 2.2 to section 2.6 in this Thesis addressed the analysis
of different hierarchical routing algorithms.

1.2.3 Research Question 3

RQ3: What are the main considerations of the design a routing protocol?

• RQ3.1: How to select a suitable cluster head and form a given cluster?

• RQ3.2: How to shorten the data transmission distance with routing in

order to maximize the energy efficiency?

• RQ3.3: How to balance overall energy efficiency of all sensor nodes for the
whole network?

Literature Review Main Points:

• The cluster formation of different latest LEACH versions [42; 43; 44; 45; 46; 47; 48; 49;
50; 51; 52; 53; 54; 55; 56; 57; 58] are compared,

• The pros and cons of the clustering and routing methods under hierarchical routing algo-
rithms [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75] are identified,

Gaps:

• The organization structures of the chain, tree, grid, and area based networks are not flexible
enough.

• If the destination of transmission is not within the coverage area of the sending node, data
packet may be lost.

Contributions:

6
 1.3 Overview of the Thesis

• Energy consumption for clustering and routing algorithms can be reduced by adequate
combining the following parameters: Remaining energy, Distance to nodes, Distance to
base station, Energy efficient transmission distance, Temporary routing node and Cluster
head rotation.

• Our published papers [76; 77] and chapter 3 in this Thesis discussed in-depth the design
of our clustering and routing algorithms.

We use the following steps of the research methodology to address the Research Questions
and to achieve the objectives of the Thesis. The type of research that will be used in this Thesis
is a combination of qualitative research and quantitative research. The research methodology
phases are explained in the following:

1. Qualitative Research Phase (Literature Review): It aims to gather an in-depth

understanding of Hierarchical Routing Protocols for WSN. It provides a consolidated sum-
mary and in-depth analysis of the Literature in the field. The review identifies relevant gaps
and provides a critical analysis. Thereof, information collected is presented in comparative
tables. Additionally, the qualitative research phase provides the theoretical foundations of
research questions and objectives of this Thesis.

2. Design Phase (Proposed Protocols): This phase aim to use the acknowledge acquired
in the qualitative research in order to design suitable Hierarchical Routing Protocols for
WSN. Thereof, design is presented in flowcharts and pseudo-codes. Those protocols are
implemented and deeply analysed in the next phase.

3. Quantitative Research Phase (Evaluation and Comparison): Quantitative method

is used to gather data, specifically Omnet++ simulator using Castalia framework [78] are
used. For this research Thesis, comparative graphs form is utilized as it is better at
presenting the data for analysis and extracting useful information from the data.

This research methodology is the most suitable approach to accomplish the research objec-
tives of this Thesis.

1.3 Overview of the Thesis

The rest of this Thesis is organised as follows:

• Chapter 2 presents a relevant literature review in energy efficiency routing protocols in

WSNs. The chapter discusses and analyzes the cluster formation method, hierarchical
structure, and cluster head selection criteria. The chapter reviews the energy consump-
tion using different criteria and presents relevant conclusions of the energy burden and

7
 1.4 Publication List

limitations of different routing algorithms. The chapter concludes with the discussion and
comparison of different parameters used in cluster formation for several enhanced LEACH
versions.

• Chapter 3 discusses the main constrains and design considerations of routing protocols
for WSN. The chapter focuses on the design and implementation of simple and multi-
criteria energy efficiency routing protocols. The chapter discusses the cluster head selection
based on different criteria and parameters aims to reduce unnecessary redundancy, while
conserving the whole WSN energy resources. The chapter describes in detail the routing
protocol principle for saving and balance energy in the whole WSN. Finally, the chapter
concentrates on security aspects to be considered in routing protocols.

• Chapter 4 describes in details how the simulations of the proposed routing protocols
where implemented as well as scenarios and simulations parameters. Omnet++ simulator
and Castalia model [78] with well-defined wireless sensors data communication standards
are used. Additionally, in this chapter various hierarchical routing algorithms for WSNs
are compared and their performance is discussed using different criteria.

• Chapter 5 concludes this Thesis by presenting a general conclusion, summarising the

answers of the proposed research questions, and discussing potential future research direc-
tions.

1.4 Publication List

• Publication 1 (Published): HF. Chan, K. Gomez Chavez, A. Al-Hourani, and H. Rudolph.
“Hierarchical Routing Protocols for Wireless Sensor Network: A Compressive Survey”. In
Springer Wireless Networks, 2019. DOI: https://doi.org/10.1007/s11276-020-02260-z [59]

• Publication 2 (Published): HF. Chan and H. Rudolph. “New energy efficient routing
algorithm for Wireless Sensor Network”. In proceedings of the TENCON 2015 - 2015 IEEE
Region 10 Conference, Macao, 2015, pp. 1-5. DOI: 10.1109/TENCON.2015.7372955. [76]

• Publication 3 (Published): HF. Chan and H. Rudolph. “Innovative Self-Organization

Wireless Sensor Networks for Electrical Power Systems”. In proceeding of the International
Conference on Electrical Engineering (ICEE), 2015. [77]

8
Chapter 2

Literature Review and

Comparative Analyses

In this chapter, we present a survey of the state-of-the-art routing techniques in WSNs. We

first present a survey on the most relevant work in routing protocols. Then, we highlight our
contribution. This is followed by a survey of different WSN routing protocols. Routing techniques
can be generally classified as (i) flat, (ii) hierarchical, and (iii) location-based routing. This
survey focuses on the deep analysis of WSN hierarchical routing protocols. Hierarchical routing
protocol is one of the method to save energy when transmitting data in a long distance. It divides
the whole network into clusters, and data can be transmitted along the cluster heads to share
the energy burden in data communications. We further classify hierarchical protocols based
on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in
order to compare and highlight the advantages, disadvantage and performance issues of each
routing technique. Finally, we conclude this chapter by presenting a comprehensive survey of
the recent improvements of Low-Energy Adaptive Clustering Hierarchy (LEACH) [41] routing
protocols and a comparison of the different versions presented in the literature. This work has
been partially published in our paper [59]
A large amount of sensors are deployed outdoor in both the transmission grid and distri-
bution grid to monitor the status of the electrical power system. With the increasing usage of
solar panels and wind turbines in the renewable energy systems, power generation and usage is
currently more dynamic and scattered on the customers sides [79; 80; 81] In order to maintain
the reliability of power supply, wireless sensor network is used to generate massive amount of
data periodically for automatic monitoring and control [7]. Smart grid should be operated with
self-healing and fault tolerant functions. The objective is to accommodate the power generation,
storage and consumption efficiently [82; 83; 84]. These applications are required to measure the
data from the environment in every few milliseconds and generate a lot of information to be sent

9
to the base station.
Moreover, due to the long distance transmission between the power generation at the station
and consumption at the end user, extensive use of overhead transmission lines in the grid is
required [85]. And, some substations are needed in between to monitor and control the power
transmission. Wired network like optical fiber may not be appropriate and costly for transmission
in this long power grid. A wireless communication method can solve the problem by using
wireless sensors to support the daily monitoring and control in the substation [86; 87]. Wireless
sensor network is used to monitor overhead transmission lines [88; 89]. Sensors are scattered
on different positions on a tower and transmission line which are far away from the substation.
The linear transmission topology in power grid is not adequate in supporting the massive traffic
requirements. To reduce the energy consumption and time delay in gathering information,
With a large scale IoT network, if each node connects to the Internet individually, there
will be a scalability problem. The wireless sensor nodes are sparsely deployed outdoor without
internet connection solely. Although an individual cellular link for each or group of sensors
can be established, it is costly to implement and induce large overhead to the server. Single
hop transmission is that a cluster head sends data to the base station directly without passing
through other cluster heads. This is the simplest transmission method without the need to
consider other information. However, it may not be suitable for a large scale network because
there is a transmission distance limitation with sensors, and they are not allowed to transmit
data outside a certain range. Even if the data can be transmitted, it may lead to a heavy burden
on the cluster head because the energy consumption is directly proportional to the distance, and
is higher for longer distances [32].
This problem can be solved by multi-hop clusters in which low-power short-distance radio
communication, such as Bluetooth and Zigbee, is used. It can be alleviated by clustering and
multi-hop transmission methods for IoT networks to minimize the number of required Internet
connections and transmissions.
The multi-hop transmission mechanism are effectively adopted in WSN for IoT [90; 91; 92;
93; 94]. It is useful for the IoT network deployed in a very large area where a large volume of the
smart nodes are monitored such as monitoring the street lights, traffics and pollution in a city.
Multi-hop transmission is that cluster heads send data to the next cluster head(s) until the base
station is reached. This method can divide a single long distance into multiple shorter distances
for transmissions. This can share the loading among cluster heads, and it is more suitable for
large scale networks. However, a flexible and adaptive routing method is needed because energy
will be wasted for unnecessary transmissions, and data packets are lost when there is no next
cluster head within the sensing range. Moreover, cluster heads which are closer to the sink are
always overloaded with heavy traffic which causes them to be exhausted rapidly. Clustering with
unequal size has been the main solution under investigation to overcome such problems [32].

10
 2.1 Routing Protocols Surveys: A Review

2.1 Routing Protocols Surveys: A Review

Recent surveys have been conducted on WSNs regarding clustering and routing algorithms.
These surveys are mainly focusing on energy saving, scalability, reliability, auto-configuration and
they discuss the different techniques used for improving the performance of WSNs as summarized
in Table 2.1 and explained bellow.

Table 2.1: Review of routing protocols surveys.

Survey Focus Conclusions
Hierarchical routing protocols are a
Routing protocols are classified into four
good approach for scalability and
A survey of general main groups: data centric, hierarchical,
transmission efficiency, though more
routing protocols [35; 38]. location based and multi-path based
research work is needed to improve the
routing protocols.
energy efficiency.
Discusses several hierarchical routing
Shortening the latency
algorithms aiming at selecting a suitable It provides a list of routing protocols for
and minimizing the
cluster head and controlling the shortening communication delays and
energy consumption on
frequency of data transmissions to save saving energy.
WSN [39].
energy
Discusses challenges and limitations of
Clustering energy efficient WSNs, pros and cons of different No single routing algorithm is suitable
approaches [30]. algorithms and metrics for assessing for all situations.
WSN.
Analyses predefined performance
m-BEENISH is more energy efficient
Heterogeneous clustering metrics of WSNs, propose a new routing
than other heterogeneous clustering
methods [31]. protocol (m-BEENISH) and cluster head
environment protocols.
selection based on different energy levels
Discusses the strengths and weaknesses
To know the location of sensors nodes is
Improvements and of different LEACH versions under
an energy costly overhead and delays
categorization of LEACH different domains, such as energy
may occurred in multi-hop
versions [32]. efficiency, security, optimization, data
transmissions.
aggregation, etc.
Describes the formation of clusters and
Different LEACH versions
the selection of cluster heads in different It suggests ways to secure LEACH by
and security
aspects, also mentions different attacks various methods.
methodologies [33].
on LEACH.
Standardization plus flooding techniques Hop count, Euclidean distance, and
Standardization, flooding
for route on-demand, clustering power distance, gradient approaches can
and gradient
techniques for dividing the large be used to measure the distance between
approaches [40]
network into groups. nodes.
Integration of tree based network with
To analyse sleep and wake up schedules
duty cycles can shorten the delay time
Routing algorithms for in WSN and explain the concerns on
and broadcast method but is not a
duty-cycles WSN [36; 37]. broadcast and multicast routing for
suitable solution for duty-cycle
duty-cycled WSN.
networks.

There are different limitations and challenges that a routing protocol should deal with.
In [35; 38], the routing protocols are classified into four main categories: (i) data centric, (ii)
hierarchical, (iii) location-based, and (iv) multipath-based routing protocols. Each protocol is
then further analyzed under each category. The comparison criteria are based on node mobility,
power consumption, data aggregation, scalability, and multi-path ability. It is concluded that
hierarchical routing protocols are still a good approach regarding scalability and successful trans-
mission rate, and further research can be done to improve their energy efficiency, especially for
large scale sensor networks because it can divide the large network area into smaller clusters to
share data transmission burden among all sensor nodes. There are many factors to be balanced
in designing WSN protocols, such as fault tolerance, energy efficiency, scalability, latency, power
consumption and network topology. The authors in [39] mainly concentrate on two main factors:
reducing the latency and minimizing the energy consumption, in designing WSN routing proto-

11
 2.1 Routing Protocols Surveys: A Review

cols. The authors, also discusses hierarchical routing algorithms, TEEN [95] and APTEEN [96],
aiming at selecting a suitable cluster head and controlling the frequency of data transmissions
to save energy. To shorten the latency in data transmissions, some protocols like SPEED [97]
can maintain a desirable transmission rate in data communications. RAP [98] can provide a real
time request and query transmission with scheduling for large area network. Moreover, LAP [99]
is a location based and connectionless communication protocol, and it can shorten the commu-
nication delay and save energy by maintaining a table containing neighbour nodes to select the
best transmission path for forwarding data packets. RPAR [100] is a real time protocol to save
energy consumption and shorten the time delay by meeting a packet transmission deadline with
desired transmission velocity.
Different energy efficient clustering approaches have been categorized and compared in the
survey [30]. The challenges and limitations of WSN are discussed as well as the different pros
and cons of each algorithm. The analysis of hierarchical routing and its use in WSNs is based
on different parameters such as the clustering approach and the selection of cluster heads. The
indicators of performance in terms of network’s lifetime, battery life, data transmission and sens-
ing techniques are summarized. The authors conclude that there is no single routing algorithm
which is suitable for all situations. Similarly, different recent heterogeneous clustering methods
of sensors with different level of initial energy are discussed in [31]. The analysis is based on some
predefined performance metrics such as network lifetime, number of heterogeneity levels, cluster
head selection, energy efficiency and stability. A new routing protocol named m-BEENISH is
proposed. The cluster head selection is based on the initial and residual energy level of nodes.
Five different energy level are defined for nodes, and the nodes with higher energy level will have
a greater chance to become a cluster head. The authors concludes that m-BEENISH is more en-
ergy efficient than other similar protocols under the heterogeneous clustering environment with
higher stability, longer lifetime, and higher successful transmission ratio.
LEACH [41] is one of the hierarchical routing algorithms to save nodes’ energy for data
communications. However, there are some security concerns on LEACH when it comes to IoT,
because many sensors nodes are connected together to communicate sensitive and private data.
It is because LEACH is a single hop transmission protocol, and the size of the group is generally
larger when comparing with multi-hop. Therefore, there is a huge amount of data communi-
cations among the members. For Sybil attack, some unauthorized nodes pretend as a normal
node and send the data. This prevents the normal nodes from transmission and cause collisions
because of the huge number of transmissions in larger cluster size with single hop transmission
as LEACH. Thus, hierarchical routing protocol with data encryption algorithm is proposed in
section 3.4 in this Thesis to address the security and energy efficiency issues of wireless sensor
network communications. In [32], the improvements and categorization of different LEACH
versions in two main groups i) single hop, and multi-hop transmissions. Then, it further analy-

12
 2.1 Routing Protocols Surveys: A Review

ses the algorithms based on different parameters, such as clustering methods, energy efficiency,
overhead, scalability complexity, load balancing, location of nodes, and delay. It discusses the
strengths and weaknesses of the different LEACH versions under different domains, such as
energy efficient, security, optimization, data aggregation, mobility, scalability, cluster size, etc.
It also mentions that knowing the location of sensors nodes is costly because it consumes a
lot of energy in data extraction and communication. More overheads and delays may occur in
multi-hop transmissions due to paths construction. Energy consumption is also a major factor
in considering the cluster head selection, thus conclude the authors. The survey in [33] describes
different LEACH versions and security methodologies. It describes the formation of clusters and
selection of cluster heads in different aspects, and mentions different attacks on LEACH, such as
Sybil Attack, Selective Forwarding, and Flooding attack [33]. It further suggests ways to secure
LEACH by various methods such as hop by hop, end to end data aggregation, and other security
mechanisms.
Standardization, flooding and gradient approaches have been reviewed in [40]. Flooding
technique developed by IETF MANET working group aims at finding a route on-demand to
optimize the number of relaying nodes. Flooding is a protocol that relays a message from
a source node to all other nodes in the network. It is commonly used in the initiation and
operation in most wireless sensor network (WSN) algorithms. Flooding applies in routing tree
creation [101; 102], clock synchronization [103], code and data dissemination [104; 105; 106],
node localization [107], and cluster formation [108]. Network floods are the fundamentals in
managing the operations the whole sensor network. However, it is costly in terms of both latency
and bandwidth because flooding is propagated by all nodes in order to ensure a complete and
reliable coverage of the network. It is not effective if the sensor nodes is not within its flooding
area. There are two characteristics of a low-duty cycle networks that make flooding challenging.
First, a packet cant be received by the target nodes simultaneously if they are not awake.
Therefore, the sender needs to broadcast the same packet multiple times if its receivers do not
wake up at the same time. Thus, flooding in such networks is not applicable and eventually
the packet is communicated by multiple unicasts. Second, wireless communication is unreliable
in connection when comparing with wired one. If a packet can’t be received successfully, a
re-transmission is required which may induce more redundant network traffics due to flooding.
Gradient routing approach can be applied in wireless sensor networks when data are trans-
mitted in the direction to the sink from scattered sensors. Thus, each sensor node can forward
sensing data to the sink by the gradient direction information. The main objective of the gra-
dient routing is to decide the next hop locally with the neighbor node which is closer to the
sink so that sensing data can follow the direction of descending gradient to reach the sink. The
value of gradient needs to be constructed and maintained at each node locally. The gradient
value is managed by initial and periodic flooding in the form of some control packets, such as

13
 2.1 Routing Protocols Surveys: A Review

interest in Directed Diffusion [109] or advertisement in GRAB [110], from a sink. However,
periodic flooding throughout the whole network may cause excessive overhead, waste of en-
ergy and unnecessary occupation of bandwidth in sensor networks. Moreover, when there are
network topology changes due to a sensor node failure or wireless link disconnection, some of
the gradient values will become inaccurate and hence requires more frequent flooding for the
updates. These situations make the gradient routing approach challenging when applying in
wireless sensor network.
Furthermore, clustering techniques are proposed in order to divide large network into clusters
so that data can be transmitted in an acceptable range. Various clustering techniques proposed
by different researchers are also analysed in this chapter. Knowing the location of nodes is useful
for environmental monitoring and mobile applications. Sensor nodes can be equipped with a
GPS function to determine their location. Hop count, Euclidean distance, and power distance
can be used to measure the distance between nodes. Gradient approach, which measures the
distance according to the height of nodes, is also discussed. These are different limitations
and challenges that a routing protocol should be concerned with. In [35; 38], It then further
analyses each protocol under each category. The comparison indicators are based on the nodes’
mobility, power consumption, data aggregation, scalability, and multiple paths. It concludes
that hierarchical routing protocols are still a good approach for scalability and transmission
efficiency, and more research work can be carried out to improve the energy efficiency especially
for large scale sensor networks. With the emergence of IoT, there will be many of applications
with mobile sensor nodes, and there is a need to modify some of the existing routing algorithms
to suit the new applications.
Duty cycling aims to conserve energy by turning a sensor nodes radio on and off, and it
has become a fundamental mechanism in the design of Wireless Sensor Networks. The primary
objective of duty cycling is to reduce the energy consumption of nodes and extend the whole
network lifetime. Duty cycling can be used to shorten idle listening time, and reduce the duration
of a radio transceiver waiting in vain for a packet. Idle listening is not the only cause of energy
waste. Energy will also be consumed when a node listens for a packet that is not intended to be
the receiver. Moreover, control packet overhead and collisions also waste the energy of a node.
On the contrary, there are some drawbacks of duty cycling which induce more control traffics in
the network and thus increase the chance of collisions.
The implementation of routing algorithms for duty-cycled WSNs consists of sleep and wake up
schedules for synchronization of data transmissions. Several techniques are discussed in [36; 37].
Authors state that integration of tree based network with duty cycles can shorten the delay
time and k-neighbourhood algorithm method reduces the number of neighbourhood connections
to a desired value. For this the neighbourhood nodes adjust the sleep schedule and routing
path among themselves to save energy consumption. It also explains the concerns on broadcast

14
 2.2 WSN Routing Protocols Classification

and multicast routing for duty-cycled WSNs. It is because a simple broadcast method is not a
suitable solution because each node may have a different sleep schedule, and also collisions may
be occur when more than one node sends data simultaneously. Besides, redundant data will be
sent if similar or identical data is sent from neighbouring nodes in high density networks. Some
analysts proposed considering the probability that a node rebroadcasts a packet in the current
active time, and the probability that a node remains on after the active time when it normally
would sleep in order to determine the multicast approach. Others may add a flag to the packet
to store the quality of links to all its neighbours and its broadcast packet reception status to
reduce redundant transmissions.
LEACH is a clustering-based protocol in which cluster-heads are randomly rotated to share
the burden of being a cluster head while collecting data from its cluster members and transmit-
ting data to the base station. In terms of duty cycling, LEACH adopts a TDMA scheduling for
intra-cluster communications, and all cluster members synchronize their duty cycles with the
schedule of their cluster head.

2.2 WSN Routing Protocols Classification

As stated earlier, in order to create a robust WSN, a well-developed routing protocol is essential.
WSN routing protocols can be categorized into three different groups [35; 38]:

1. Flat Routing Algorithms: Sensor nodes have similar functionality in data gathering, func-
tionalities, transmission and power consumption.

2. Hierarchical Routing Algorithms: Sensor nodes are divided into several clusters. In each
cluster, the node with the higher energy level is basically commonly chosen as the cluster
head based on different well-know metrics.

3. Location-based Routing Algorithms: Sensor nodes use geographical information to send

data to specified regions. So sensor nodes need to be able to localize themselves, or their
location be known.

Since the demand of connecting huge number of wireless sensor nodes scattered in wide
areas for IoT applications are increasing, single hop transmission is not possible for these long
distance transmissions. Therefore, hierarchical routing protocols are likely the choice of IoT
sensor networks [33], we focus on further classifying and analysing several hierarchical routing
protocols based on different criteria in Figure 2.1.

2.2.1 Hierarchical Routing Algorithms Background

Hierarchical routing techniques are commonly used in IoT applications. Many researchers studied
and analyzed the usage of hierarchical routing in IoT applications. Resource aware hierarchical

15
 2.2 WSN Routing Protocols Classification

Figure 2.1: Routing protocols classification of WSN.

routing algorithm is implemented in heterogeneous wireless IoT networks. [111] A hierarchical

security framework is developed for defending against sophisticated attacks on wireless sensor
networks in smart cities [112]. A hierarchical cloud computing model [113] is proposed for
context-aware Internet of Things services. It enhanced system throughput when comparing
with legacy uniform binding based computing models. The proposed computing model can be
deployed to the information technology consumer devices and network entities as a key infras-
tructure. Hierarchical routing using swarm-intelligence is developed in traffic system. [114] It
splits traffic networks into several smaller and less complex networks by introducing a hierarchy
between the roads. It guides the vehicles to travel between cities using the fastest way by con-
sidering the traffic load on the roads. An intelligent green communication approach in the smart
healthcare system is developed for monitoring the patients. [115] The proposed approach used
a centralized cluster-based hierarchical routing mechanism to partition the health monitoring
devices into clusters. This hierarchical approach can monitor various vital signs of the patients
in an energy-efficient manner.
Hierarchical routing algorithms in WSNs have been studied from a variety of angles [32]. A
common method is clustering by dividing sensor nodes into clusters [30]. This is a commonly
used data communication technique to reduce the energy consumption by sending data from
sensors to cluster head and to the base station. In hierarchical clustering, the whole sensor
network is divided into different clusters or multiple layers. Transmission within a cluster is
coordinated by each cluster head which is also responsible for routing between clusters or base

16
 2.2 WSN Routing Protocols Classification

stations. Data travels from one level to another enabling it to travel longer distances. This
can make the data communication faster and more energy efficient. Thus, clustering provides
data aggregation advantages among cluster heads at different levels in order to improve the
performance of the whole WSN. The following categories are commonly used:
Hierarchical routing can be also classified into the following main sub-categories: i) chain-
based, ii) tree-based, iii) grid-based, and iv) area-based routing. The graphical representation
of hierarchical routing algorithms are depicted in Figure. 2.2. In this survey, the most relevant
state-of-the-art algorithms have been selected for each category as show in Figure 2.1. These
techniques and algorithms are explained in the following sections.

Figure 2.2: Representation of Hierarchical Routing Protocol Strategies in WSN

Table 2.2 shows a comparison among hierarchical routing protocols based on energy, load
balancing, delay, scalability and data loss probability and how each of those protocols perform
in those fields.

Table 2.2: Comparison of hierarchical routing algorithms based on energy consumed by nodes
and leaders.

Energy
Data Loss
Consump- Energy
Load Time Probability
Algorithms tion by Consumption by Scalability
Balancing Delay (by node
Sending Leader
failure)
Node
Chain-
Low High (long distance) Medium Long Poor High
Based
Low (shorter
Tree-Based Low Medium Medium Medium High
distance)
Grid-Based Medium Low Good Short Good Medium
Area-Based Low High Good Long Poor Low

For each category of the hierarchical routing techniques we will discussed in detail the main
features and characteristics in the following section. Table 2.3 provides a comparison of the

17
 2.3 Hierarchical Chain-Based Routing Algorithms

performance of hierarchical routing algorithms regarding their energy consumption. We will

later further elaborate all this metrics.

Table 2.3: Comparison of energy consumption of hierarchical routing protocols.

Remaining Energy
Algorithm Structure Energy Consumption
for Electing Leader
PEGASIS [60] Chain 7 High
CCS [61] Chain 7 High (Better than LEACH).
High (Longer distance between two nodes in
EBCRP [62] Chain X rectangle, but better than CCS because of
ladder.)
CHIRON [63] Chain X Low (because of multihop).
Low (Use more energy for path selection due to
EADAT [64] Tree X
not shortest path).
BATR [65] Tree 7 Medium (Even no for tree formation).
Low (Minimum spanning tree for routing. Data
PEDAP [66] Tree X volume and transmission distance to calculate
the link cost).
ETR [67] Tree 7 Low (because low computation cost)
PANEL [68] Grid 7 Low (high computation cost).
High (Grid is constructed at the center of the
TTDD [69] Grid 7 source. If control message increases, more
energy consumption).
Medium (Good because of hierarchical
HGMR [70] Grid 7
structure).
GMCAR [71] Grid 7 Low (Because of traffic sharing).
LBDD [72] Area 7 Low
Ring Routing [73] Area 7 Low (high overhead in building ring structure).
Railroad [74] Area 7 Low
VLDD [75] Area 7 Low (Because flooding is not needed).

2.3 Hierarchical Chain-Based Routing Algorithms

In chain-based hierarchical routing, the whole WSN is divided into multiple chains; a leader node
is selected in each chain. Each sensor node will deliver the packets to the next nearest node until
reaching the leader node in Figure. 2.3. Aggregation of data is carried out through transmission.
The leader will then send the aggregated data to the base station. When comparing with other
types of hierarchical routing algorithms, chain based routing is easy to set up and maintain, It
is because the chain starting from the farthest node always find the next closest neighbor to
connect to in the network until all nodes are connected. And, there is no frequent change of the
formation of the chain, and the nodes always send the data to the nearest node. Therefore the
energy consumed for chain formation is low because it doesn’t involve complicated computation.
However, the problem of chain based routing is that there may be many nodes in a chain, and
if the source is far away from the leader node, then the data needs to travel a long distance to
the leader. The time used for the delivery is long, and this may cause time delays, and may
not be suitable for time critical applications. Besides, nodes which are very closer to the leader
node will always be involved in data transmissions. This creates a heavy burden on these nodes,
and the energy consumption of these nodes higher. Another consideration is that if there is a
node malfunction in a chain, all the data travelling through the failed node and chain will be

18
 2.3 Hierarchical Chain-Based Routing Algorithms

lost. This affects the reliability of the applications and the performance of the chain. The most
relevant chain-based routing algorithms are listed below:

Figure 2.3: Chain-based Routing Algorithms.

1. PEGASIS (Power-efficient Gathering in Sensor Information Systems) [60]: A

chain can be formed by the sink and the nodes themselves. All nodes know global network
information and the location of every node. The node which is farthest away from the
sink starts the formation. It will find the closest node as the next connection until it
arrives at the leaders. The leaders will send the aggregated data to the sink and leaders
can be rotated. This helps with load balancing to some extent because every node in the
network may become involved in the data transmission, and the leaders can be rotated to
share the burden. However, very long time delays may occur because of the long distances
travelled from the source to the sink as the data passes through many intermediate nodes.
Therefore, it is not suitable for time critical application and not appropriate for a large
scale network. Several improvements of PEGASIS has been proposed in the literature.
In [116] PEGASIS with Improved Network Lifetime (PEGASIS-INL) is proposed. The
PEGASIS-INL selects a node as a leader only if it is within a strong communication range
of base station. Similarly, in Modified PEGASIS [117], each node communicates only with
a close neighbor node with minimum distance and transmits data to the base station,
thus reducing the amount of energy spent per round. Thus PEGASIS-INL and Modified
PEGASIS algorithms outperform PEGASIS in terms of the energy consumption and the
network lifetime.

2. CCS (Concentric Clustering Scheme) [61]: It is built with multiple chains in contrast
with PEGASIS which has one chain only. The multiple chains of CCS are divided into

19
 2.3 Hierarchical Chain-Based Routing Algorithms

different layers. One cluster head will be selected in every chain. Within each chain, all
nodes will send data to the nearest node until data reaches the cluster head. Next, the
cluster head on the farthest chain will send data to another cluster head on the next higher
layer until it reaches the base station. Compared with PEGASIS, the distance travelled
can be shortened greatly because the data is transmitted among cluster heads up to the
base station. Therefore, the time delay is shorter and it is more suitable for large scale
networks. However, the cluster head on the chain which is closest to the base station will
have a heavier traffic loading and will likely be exhausted earlier than others. Note that
residual energy is not considered in selecting the cluster head. Thus a low residual energy
node may be selected and also become quickly exhausted.

3. EBCRP (Energy-Balanced Chain-cluster Routing Protocol) [62]: The whole net-

work is divided into many rectangular sections, and one chain is established in each rect-
angular area by using a ladder algorithm. Cluster heads are selected based on the residual
energy and they are rotated to share the loading. The cluster head of each chain collects
data from all nodes in its chain and send data directly to the base station. Compared with
the greedy algorithm, the ladder algorithm that EBCRP is using, is more energy efficient
because the total transmission distance may be shorter. The traffic loading and burden is
shared. This avoids exhausting any one cluster head node. However, the whole network
is divided into rectangles. The neighbouring nodes within a rectangle may not be in the
shortest distance path, and therefore may consume more energy. Furthermore, because of
single hop communication, EBCRP is not suitable for large scale networks.

4. CHIRON (Chain-based Hierarchical Routing Protocol) [63]: Different from the

EBCRP which is formed with fixed rectangular shapes, CHIRON divides the whole network
into fan-shaped areas, which is more flexible. The node farthest away from the base station
starts the chain formation. The node then connects the closest node to form a chain by
using a greedy algorithm. In each chain, a leader is selected based on the residual energy,
the node with the highest residual energy becomes the chain leader. The chain leader will
then collect the data from its chain members, transmit the data to the next chain leader,
and then finally to the base station. Due to its multiple hop transmissions, this routing
protocol is suitable for large scale networks. It can also shorten the transmission distance
and reduce the time delay for data transmission. The fan-shaped division may be more
flexible than rectangles, and the chain can be formed according to the requirements of the
situation at that moment.

20
 2.3 Hierarchical Chain-Based Routing Algorithms

2.3.1 Chain-Based Routing Algorithms Comparison

As explained above these algorithms have advantages and disadvantages based in the main
objective and scope of the network as shown in Table 2.4. Below the most relevant aspects of
these algorithms are compared.

Table 2.4: Comparison of hierarchical chain-based routing algorithms.

Location
Global
Informa-
Formation Knowledge of Hierarchical Leader
Algorithm Structure tion for
Method Nodes Structure Selection
Electing
Position
Leader
Chain formed by
sink (furthest One chain for
Non-
PEGASIS [60] Chain node). Centralized Required whole Rotate
required
with greedy network.
approach.
Multiple
One leader
chains with
CCS [61] Chain Centralized Required in every Required
different
chain.
levels.
Divided into One chain in
rectangular each Rotate in Non-
EBCRP [62] Chain Required
sections. Ladder rectangular every chain. required
algorithm. section.
Divided into fan
Leader is Required
shape areas. Chain
selected (Farthest
formed by the node One chain in
CHIRON [63] Chain Required based on away to BS
farthest away from each area.
remaining will be the
the BS. Greedy
energy. leader first).
algorithm.

2.3.1.1 Energy Efficiency

One of the most important issues in WSNs is optimal use of the resources in the network in
order to optimize the energy consumption of each sensor node. For PEGASIS, there is one
chain only, the data must be transmitted to all the nodes in the chain, and therefore the energy
consumptions of nodes is very high. Also the sending of data packets to the base station will be
highly concentrated on the one leader, and therefore that leader will become exhausted sooner
because of the huge data volume that it needs to handle. Therefore, PEGASIS is the worst in
terms of energy efficiency [60]. For CCS, the energy consumption is less than PEGASIS, because
the network is divided into multiple chains in concentric circular tracks. Therefore, the length
of each chain can be shortened, and data transmission does not involve as many nodes. On the
other hand, the data is transmitted between chain leaders to the base station. Therefore, the
burden of data transmissions can be shared among chain leaders [61]. For EBCRP, the energy
efficiency is similar to CCS. The network is divided into rectangles, and therefore the length
of the chain in each rectangle may be shorter than that in CCS. Therefore, fewer unnecessary
nodes may be involved in data transmissions. However, due to its single hop transmission to
the base station, the distance between the chain leader and the base station may be long, and
therefore the chain leader will consume more energy. A drawback is that the nodes next to

21
 2.3 Hierarchical Chain-Based Routing Algorithms

each other inside a rectangle may not form the shortest path. CHIRON, is the best in terms of
energy efficiency among the chain-based routing algorithms. When compared to EBCRP, the
network is divided into fan-shaped areas. It is more flexible than a rectangle divided in EBCRP,
and therefore the probability of sending data to the next node in the shortest distance will
increase and thus reduce the energy consumption. Moreover, the transmission from the chain
leader to the base station is multiple hop, therefore, the individual transmission distance of each
chain leader is shortened, thus sharing the burden and saving energy. Therefore, among all the
chain-based routing algorithms, CHIRON seems the most energy efficient.

2.3.1.2 Stability and Node Failure

For PEGASIS, there is only one chain. If there is an exhausted or malfunctioning node, the
data transmitted through that chain will be affected. The leader is selected randomly, and the
residual energy of nodes is not considered. If a node with not enough energy is selected, it
does not have the required amount of energy to send to the base station. Therefore, the data
packets will be lost. Moreover, there is one leader for the whole network, and that leader will
be exhausted quite soon [60]. For CCS, EBCRP, and CHIRON, the network is divided into
multiple chains instead of one single chain. Therefore, the length of the chains is shortened,
and the data transmissions can be shared by many leaders. Therefore, the data transmission
is not reliant on a single leader, and the data packets lost in case of leader node failure will be
greatly reduced. Among the three algorithms, CCS is the worst because the leader is selected
based on the location of the chain, and residual energy is not considered. Thus if a node with
little energy is selected as leader, this will affect the stability of data transmissions [61; 62; 63].
When comparing EBCRP and CHIRON, both algorithms consider residual energy in selecting
the chain leaders. However, the single hop feature of EBCRP creates a heavy burden on the
chain leader due to long transmissions distance. This quickly exhausts the chain leader and
affects the stability of the entire network [62; 63]. Among all the algorithms in chain-based
routing, CHIRON comes out the best.

2.3.1.3 Suitability for Large Area Network

PEGASIS, is not suitable for large area networks because there is only one chain for the whole
network and if one node breaks down, then all data packets will be lost. PEGASIS also has
innate time delays because a data packet is required travel through all nodes in the chain to
the leader. Therefore it is not suitable for large networks [60]. EBCRP is also not appropriate
for large area networks because it is a single hop transmission, and the chain leader may not be
located within transmission range of the base station. Also a lot of energy would be required for
long distance transmissions within the transmission range in a large area network [62]. Both CCS
and CHIRON are more suitable for large area networks because of multiple hop transmissions,

22
 2.4 Hierarchical Tree-Based Routing Algorithms

thus transmissions to the base station from a chain leader can be shortened though multiple hop
transmissions. However, CHIRON is still better because the transmission between the leaders
must be in the next consecutive level in CCS. In CCS the flexibility of transmission is less than
that of CHIRON, which does not have the limitations of level organization [61; 63]. Again,
among all the chain-based routing algorithms, CHIRON protocol is the most suitable for large
area networks.

2.4 Hierarchical Tree-Based Routing Algorithms

There are WSN applications used tree-based routing algorithms. For instance, Dashtestani et
al. [118] use the tree shaped multi-hop hierarchical network for under water sensors. Xiao, Fu,
et al. [119] suggest hierarchical tree-based image compression and transmission mechanism for
wireless multimedia sensor networks. Furthermore, Capella, J. V., et al. [120] developed a new
robust, energy-efficient and scalable tree-based wireless sensor networks architecture applied to
a wireless fire detection system. For tree-based routing, the nodes are divided into multiple
branches, leaf nodes and parent nodes. The data is transmitted from the leaf node to its parent
node, and further to the next parent node until it comes to the base station (Fig. 2.2.b). The
data is aggregated by the cluster head before sending. In general, there are three methods of
performing data aggregation: (i) the data from different sensors are combined in one single data
packet with the same header. (ii) the data is compressed in a smaller size before transmission.
(iii) the data is aggregated in which data from different nodes measuring the same kind of data
is combined into one smaller data packet by averaging the data. Among all the data aggregation
mechanisms, they aim to remove the data replication, and reduce the number of transmission of
all sensor nodes in order to save the energy consumption, and thereby increase whole network
lifetime.
Some researchers proposed greedy routing, when a node sends data packets to the next
routing hop, it tries to find the next hop with the shortest distance to the base station in terms
of Euclidean distance, and it is supposed to be the closest node to the base station. However, the
algorithm only considers the neighbor that is closest to the destination. If it cant find a suitable
neighbor with such criteria, it will retain the packet in this node. This becomes a routing void,
which will cause a failure of the greedy routing in this case [121; 122]The tree topology is easy to
form, every node just needs to send data to the next higher level node which is closer to the base
station, and cluster formation is not required. This may reduce energy consumption for tree
routing. The drawback of tree formations is that if a parent node of a tree is not functioning,
then all the data transmission under its branch will be lost. Also, the parent nodes which are
very close to the base station and with many branches connected will consume a lot of energy
because of the greater data volume. Additionally, if the branch consists of many nodes, this

23
 2.4 Hierarchical Tree-Based Routing Algorithms

may cause a time delay in data transmission and it may increase energy consumption. The most
relevant tree-based routing algorithms are listed as follows:

1. EADAT (Energy-aware Data Aggregation Tree) [64]: The sink starts the tree
formation. Every node will set a timer for itself to start the transmission, and the waiting
time is associated with its residual energy. The higher the residual energy is, the shorter
the waiting time will be. Then, the node will select the higher residual energy and the
closest node as its parent. When the residual energy of a parent node is lower than a
specific value, it will broadcast a message to let its child know. The child may then select
another parent for transmission. In EADAT, the residual energy is considered in selecting
the connection node and path. Therefore, the chance of selecting an exhausted node will
be reduced and failure transmission can be prevented. Moreover, this can achieve some
load balancing by using the node with higher energy first, and making the whole network
live longer. However, although residual energy is one of the factors to select the path, the
final path may not be the shortest distance, and overall more energy may be consumed,
and many more nodes may be involved in data transmissions. The result is a possible
increase in the total energy consumption and longer time delays.

2. BATR (Balanced Aggregation Tree Routing) [65]: The base station collects the
global location information of all nodes and forms the routing paths. BATR will construct
the minimum spanning tree based on the energy consumption, and it will calculate the
number of child nodes under the tree to balance the loading. BATR can extend the
lifetime of the network because it considers the energy consumption to build the routing
path. Besides, the loading can be balanced by evenly distributing the child nodes among
different trees. However, the residual energy of every node is not considered when creating a
tree. So, some low residual energy nodes will be exhausted sooner, and induce transmission
failure.

3. PEDAP (Power-efficient Data Gathering and Aggregation Protocol) [66]: This

protocol also uses the minimum spanning tree to calculate energy consumption. It uses
data volume and transmission distance to build the tree. Besides that, the residual en-
ergy of nodes will be considered in data transmissions. PEDAP can balance the energy
consumption by building a minimum spanning tree with the energy and distance cost for
transmission. It can also shorten the delay time after considering the distance for trans-
missions. However, the formation of the tree may be complex, and the energy used for
calculating the path may be huge. Therefore, the setup energy may be very high, especially
for large scale networks.

4. ETR (Enhanced Tree Routing) [67]: Each node maintains a routing table containing
the next hop information. The node will select the path with the lowest hop count. Since

24
 2.4 Hierarchical Tree-Based Routing Algorithms

the routing table just stores the next hop information, the storage and computation cost is
low, and this can save some energy. However, the path is selected based on the minimum
hop count, and the residual energy is not considered. Therefore, a low residual energy
path may be selected, and will be exhausted soon, in which nodes may fail and data will
be lost.

Notice that additional techniques can be combined with tree-based routing protocol. In [123]
sleep scheduled and tree-based clustering approach routing algorithm (SSTBC) for energy-
efficient in WSN is been proposed. SSTBC preserves energy by turning off radio or switching
to sleep mode of unnecessary nodes, which observe almost the same information, base on their
location information to remove redundant data in the network.

2.4.1 Tree-Based Routing Algorithms Comparison

As explained above these algorithms have advantages and disadvantages based on the main
objective and scope of the network as shown in Table 2.5. In the following the most relevant
aspects of the proposed algorithms are discussed.

Table 2.5: Comparison of hierarchical tree-based routing algorithms.

Location
Global
Informa-
Formation Knowledge of Hierarchical Leader
Algorithm Structure tion for
Method Nodes Structure Selection
Electing
Position
Leader
Start from the sink
as the root node.
Parent and
Use remaining Non-
EADAT [64] Tree Required leaf No leader
energy as timer to required
relationship.
set the priority to
send data.
Start from the sink.
According to the BS needs the Parent and
Non-
BATR [65] Tree number of child location of leaf No leader
required
nodes (same) and nodes. relationship.
density.
Sink is the root, Parent and
PEDAP [66] Tree and form the tree in Required leaf No leader Required
a centralized way. relationship.
Start from sink or
root and Updated Parent and N for
ETR [67] Tree neighbour table to Non-required leaf No leader selecting
check the minimum relationship. next hop
no. of hops.

2.4.1.1 Centralized or Decentralized Organization

For EADAT, BATR, and PEDAP, the tree structure and routing path is constructed in a cen-
tralized manner by the sink. The sink will coordinate the required information of nodes to build
and maintain a tree structure. Therefore, a large amount of data is required for communication
between nodes and sink. Thus the setup cost may be high and it takes time to set up a tree.
However, for ETR, the nodes will communicate with each other only, setting up their routing

25
 2.5 Hierarchical Grid-Based Routing Algorithms

table to create a routing path. So, the routing path is formed in a decentralized manner which
is more flexible, faster and adaptive to a changing environment [64; 65; 66].

2.4.1.2 Residual Energy

For BATR and ETR, residual energy of the nodes is not considered in constructing a tree
structure and routing path. Therefore, a node with less or not enough energy may be selected as
parent, and data packets may be lost. For EADAT and PEDAP, residual energy will be a factor
in forming a tree and routing path. This can prevent energy holes and balances the loading of
the nodes to extend the lifetime of the whole network [65; 67].

2.4.1.3 Shortest Path

EADAT, PEDAP and ETR will choose the shorter path for data communications, EADAT will
find the parent with the shorter distance; PEDAP calculates the link cost based on the trans-
mission distance; and ETR will decide the routing path with minimum hop counts. However,
BATR will not consider the distance in routing. It just balances the tree structure and loading
of nodes based on location and density of nodes [64; 66; 67].

2.5 Hierarchical Grid-Based Routing Algorithms

For grid-based structure, the entire network area is divided into many regular rectangular cells,
and the size of the cell is fixed. It differs from Voronoi Tessellation method [124; 125] in
which the whole network is partitioned by many irregular areas by calculating the distance
and neighbourhoods of a sensor. Grid-based network in regular size is easy to form with low
computation cost. A cluster head is selected for each cell, and all nodes within a cell will send
data to their cluster head. Then, the cluster head will then send the data to the next cells
leader until it reaches the base station in Figure 2.4. The organization of grid-based routing
is simple, the formation is based on the geographical location of the nodes. It is argued that
the data can be transmitted in a more efficient way because the cell size is fixed, and only the
location information of the cluster head of the cell is required. However if there is a relatively
high number of nodes in a particular cell, this may create heavy traffic and excessively drain the
cluster head’s energy. The most relevant grid-based routing algorithms are listed below:

1. PANEL (Position-based Aggregator Node Election Protocol) [68]: This uses the
location information to select the aggregator. The whole network is divided geographically,
and the node closest to the reference point will be the aggregator. The aggregator will
collect data from the members in its cluster, and finally send them to the base station. Since
every node has the chance to become an aggregator, this helps to achieve load balancing

26
 2.5 Hierarchical Grid-Based Routing Algorithms

by sharing the burden of communicating with the base station. Note that some energy
will be used for collecting the location of sensor nodes, and will increase the setup cost.

2. TTDD (Two-tier Data Dissemination) [69]: The grid is divided into multiple cells,
and some nodes are used to relay the data requested from the mobile sinks to the source.
The mobile sink will send a data request to the intermediate nodes in a flooding way. The
source chooses the next relay nodes by using a greedy algorithm until the data reaches
the boundary of the network. The mobile sinks will move around the grids and extract
the information from the closest node of the source. TTDD is suitable for on demand
applications. However, the flooding method may consume a high amount of energy, and
therefore it is not suitable for large scale and high traffic networks. Moreover, the movement
of mobile sinks may not be in the same pace as the route formed. There may be a time
delay, and re-transmission may be required, which will also consume more energy.

3. HGMR (Hierarchical Geographic Multicast Routing) [70]: Here the whole network
is divided into multiple cells depending on their geographical location. Within each cell,
there is an access point to manage the location information. The network is built with
a hierarchical structure. The source will deliver data from the highest level to the lowest
level access points. For HGMR, different nodes will take corresponding roles in data
transmissions, since the accessing points can be rotated. This helps to balance the energy
consumption. As the network is divided into multiple cells and layers it is suitable for
large scale networks. However, the transmission from higher level access points to lower
levels does not consider the location issue. So, the path may not be the shortest which
may cause some time delay, and may also consume additional energy.

4. GMCAR (Grid-based Multi-path with Congestion Avoidance Routing) [71]:

The network is divided into cells. There is one leader node in each cell. The cluster head
will collect data from the members in its cell, and send data to the cluster heads in other
cells. Each cluster head will store a routing table, which consists of the cell density and hop
count information. GMCAR will also separate high and low traffic. For low traffic there
is just one path to the sink in the boundary grid. On the contrary, for high traffic there
are multiple paths to the sink with non-boundary cells. Moreover, a secondary cluster
head will be selected to share the heavy traffic if necessary. The separation of high and
low traffic, and a secondary leader if required can help to share and balance the loading of
the whole network to enhance the energy efficiency, and shorten the time delay for heavy
traffic with multiple paths. The cluster head will do the coordination work until its energy
descends to a certain level.

The above list concentrates in the most popular grid-based WSNs, however several additional
solutions can be found in the literature. In [126] grid-based routing protocols are enhanced via

27
 2.5 Hierarchical Grid-Based Routing Algorithms

balancing a load of data traffic among sensor nodes as evenly as possible. Instead in [127], the
sensor network is divided into logical grids of k-cells and a Cell Header is elected among each
cell that acts as leader to cluster. For density grid-based clustering, Authors in [128] propose
two new approaches i) artificial bee colony is an swarm based optimization technique, and ii)
the compressive sensing. All those techniques shown an improve in energy efficient of the overall
WSN.

2.5.1 Grid-Based Routing Algorithms Comparison

As explained above, these algorithms have advantages and disadvantages based on the main
objective and scope of the network as shown in Table 2.6.

Table 2.6: Comparison of hierarchical grid-based routing algorithms.

Location
Global Informa-
Cluster
Formation Knowledge of Hierarchical tion for
Algorithm Structure Head
Method Nodes Structure Electing
Selection
Position Cluster
Head
Required
Closest to
Divided into several Parent and rotation
the
PANEL [68] Grid geographical Required leaf with equal
reference
clusters. relationship. chance
point or
sink is CH.
Dissemination
Greedy approach nodes are
Parent and
and multiple mobile responsible Non-
TTDD [69] Grid Required leaf
sinks across for relaying required
relationship.
different cells. query
message.
Required
BS which BS work in
Tree is constructed (Location of
manages the Higher and rotation, but
HGMR [70] Grid from the source in lower BS is
location lower level BS. may be
each cell. not
information. overloaded.
considered).
Master node
act as CH.
Master node
Traffic
contain the routing
Cell densities Parent and sharing
table. Boundary Non-
GMCAR [71] Grid and hop count leaf mechanism
grids for low traffic required.
to select path. relationship. in which a
and non-boundary
secondary
grid for high traffic.
master node
is selected.

Below the most relevant aspects of the proposed algorithms are discussed. With vertexes
to the geometric center equidistant, only the shape of regular (i) triangle, (ii) square and (iii)
hexagon can complete and cover entire areas in a non-overlapping way as shown in Figure 2.4.
Area coverage is an important consideration of WSNs when applying in large scale applica-
tions of IoT. Many researchers studied coverage problem in WSNs [129; 130; 131]. The coverage
area refers to the area covered by sensing ranges of sensors. It is defined as every point within the
sensing area must be accessed by at least one sensor [132]. In WSNs, there is a trade-off between
covering large area and reliable connectivity. Connectivity becomes stronger with the decrease in
the distance between nodes and base station, and vice versa. Moreover, if the distance between

28
 2.5 Hierarchical Grid-Based Routing Algorithms

Figure 2.4: Examples of Grid-based Network.

the nodes is over the sensing range of a node, the nodes will get disconnected, and therefore no
packets can be transmitted. Finally, it will decrease the successful packet delivery rate.
End-to-end Delay is the total time required for (i) processing of a packet within a sensor
node; (ii) waiting a packet within the queue in a sensor node before starting transmitted to
the next node. (iii) sending out a packet from a node. (iv) travelling from one sensor node to
another. The end-to-end delay increases when the distance between the sensors and the base
station is longer. As the experiment done by [133], the square grid deployment method performs
better than other strategies in terms of area coverage. While Tri-Hexagon Tiling is the best
strategy in terms of end-to-end delay.
Resilience refers to the ability of a network to provide and maintain a suitable quality of
service when attacks occurred [134; 135]. Two types of attacks, degree-based and betweenness-
based node attacks, are considered in the experiment done in [133]. Node degree is the number
of incident edges with a node [136]. Node betweenness denotes the number of shortest paths
traveling through a given node [137]. The regular deployment methods have high resilience
to degree-based and betweenness-based node attacks as compared with a random deployment
method. It is because the nodes are densely deployed in some parts of the network area with
a random deployment method, and the node degree and betweenness is higher when comparing
with regular deployment. Therefore, random deployment is more vulnerable to attack. On
the contrary, the regular deployment methods have high resilience attacks as compared with a
random deployment method.

2.5.1.1 Time Delay

GMCAR can deliver data with the shortest time delay because it can divide the network into
heavy and low traffic networks with non-boundary grids and boundary grids respectively. There-
fore, data can be communicated to the leader through different channels in non-boundary grids
with heavy traffic. Besides, a secondary master node may be assigned to the cell for heavy traf-
fic. This may reduce traffic jams and reduce time delay problems [71]. However, TTDD is not

29
 2.6 Hierarchical Area-Based Routing Algorithms

suitable for applications where periodic data is needed [69] because it may introduce long time
delays. This is because the sink first sends a request for data to the dissemination nodes, and
the dissemination nodes will then send it to the nodes in a flooding manner. After the source
node receives the request, it will then send the data to the agent nodes. Finally, the mobile sink
will move to different cells and collect data from the agent node. The whole process takes a lot
of time and may cause greater time delays.

2.5.1.2 Energy Efficiency

GMCAR is the highest energy efficient routing algorithm in its class. This is because the
network is divided up based on density and there is a secondary master node to share the
loading. Moreover, the master node is selected based on its residual energy, and can be rotated
out when its energy drops. Therefore, this protocol can balance the loading amongst the nodes
in the network [71]. Again, TTDD will consume the highest energy in data communications,
because the data requests and replies involves many nodes. Also a flooding technique is applied
in the data requests, and some energy will be wasted in some redundant data communications
with nodes which are not the source. The cost of constructing the grid centered at the source is
also very high [69].

2.5.1.3 Shortest transmission distance

PANEL and GMCAR can transmit data in a shortest distance. The nodes under PANEL can
transmit data to the cluster head in a shortest distance. It is because in PANEL, a node which
is closest to the reference point in a cell will be selected as the cluster head of the grid. The
probability that a given node becomes cluster head is determined by the size of the Voronoi cell
of the node, and the size of the area within which the reference point is selected. PANEL can
adjust the size of the Voronoi cells of the nodes on the edge of the cluster by re-sizing the area
within which the reference point is selected Therefore, the sensor nodes can send data to the
cluster head in the minimum distance. GMCAR can select the routing path with the minimum
hop count. It can also help to reduce the transmission distance. However, TTDD and HGMR
will not consider location when selecting a cluster head and deciding a routing path [69].

2.6 Hierarchical Area-Based Routing Algorithms

The whole sensor network is divided into multiple areas, and the size of each area can be varied.
The base station or sink will send a data request to the closest nodes in the area to collect the
data in Figure 2.5 and Figure 2.6.
Flooding of the data request will be executed until the source of the data is located. The
source node will then send the data to the sink. However, the concentration of data traffic to-

30
 2.6 Hierarchical Area-Based Routing Algorithms

Figure 2.5: Area-based Routing Algorithms (LBDD and Ring Routing).

Figure 2.6: Area-based Routing Algorithms(Railroad and VLDD).

wards the sink in a wireless sensor network causes the nodes nearest the static sink to consume
more energy and create hotspots problem. The usage of mobile sinks can be utilized to solve
this problem, and they are widely used in IoT applications in health monitoring and vehicles
tracking [138]. Mobile sinks can provide load-balancing by moving around the network to receive
data from the nodes. However, the mechanisms for the movement of the sinks, such as adver-
tising the location of the mobile sink to the network, introduce overheads in terms of energy
consumption and packet delays. Therefore, a predefined path for the mobile sink is necessary.
The hierarchical area-based routing algorithms can provide a path for the mobile sink because
there are leader nodes on the line, rail, or ring collecting data from all nodes in the network.

31
 2.6 Hierarchical Area-Based Routing Algorithms

Besides, the mobile sink just needs to follow the route to collect the data from the leader nodes.
This can help to minimize the overheads to setup and maintain the route for mobile sinks, and
also reduce the cost of communications among nodes. The most relevant area-based routing
algorithms are listed as follow:

1. LBDD (Line-based Data Dissemination) [72]: This is a typical area-based routing

protocol. The whole network is divided into two equal areas by a vertical line of nodes.
The nodes on the vertical line will store the data for serving the requests from the sinks.
All nodes will know each others location information. The source node will send data to
the closest node on the line. A sink will send a data request to the line in a perpendicular
way. The node receiving the request will process it and continue to relay the request to
other nodes on the lines in both directions. Finally, the node storing the data receives the
request and then sends the data directly to the sink. The setup and the communication of
the structure are simple. However, if the number of nodes on the line is small, there may
be a high burden on them. These nodes will be exhausted quickly. Besides, if more nodes
are assigned to the line, the energy consumption of all nodes involved on the line will be
greater because a flooding technique is used for data requests. Therefore, this protocol is
not suitable for large scale networks.

2. Ring Routing [73]: Proposes a ring topology. The operation is similar to LBDD. But,
a ring is formed instead of a line. The relay nodes of the ring can be swapped with the
normal nodes. The ring structure is simple to form. It improves load balancing among
nodes because the relay nodes on the ring are rotated in order to protect any one node
from overload. Using the ring structure, the source node can find the closest relay node
in a shorter distance, and it reduces the time delay and consumes less energy in data
transmissions. However, if the network is large, the ring structure setup costs may be
huge because data requests are sent to all the involved nodes on the ring, and present an
overhead.

3. Railroad [74]: A data dissemination architecture named Railroad was presented for large-
scale WSNs. The network is divided by one rail which coordinates the data request. The
rail is located in the central part of the network for easy access of all nodes. The data
request will be sent to the rail until to the source node, and the source node will send data
directly to the sink. Sending the data request from the sink is by unicasts rather than
flooding. A rail structure is more flexible than a line or ring structure, and the relay nodes
on the rail can be easily accessed by normal nodes which can shorten the distance and time
for the source node to send data to the relay nodes on the railway. However, the rail is
usually quite long, and data requests transmitted along the rail may require a longer time
and cause delays. These delays increase in large scale networks.

32
 2.6 Hierarchical Area-Based Routing Algorithms

4. VLDD (Virtual Line-based Data Dissemination) [75]: It is proposed to achieve

energy-efficient and reliable data transmission. VLDD designs a Virtual Line Structure
(VLS) for data storage. The virtual line is used for collecting data from a source. A source
node knows the location information of the mobile sinks, and will calculate a suitable entry
point onto the rail. The relay node on the rail will then send data to the neighbour nodes on
the rail, after which mobile sink will send a data request to the VLS to obtain the data. The
virtual line will be reconstructed based on the location of the mobile sinks. Different from
flooding based transmissions, the nodes will calculate the suitable entry point to send, thus
avoiding unnecessary delivery, reducing energy consumption and shortening the delivery
time. However, similar to the problems that other area-based routing algorithm face, VLS
also suffers from increased overhead and transmission delays for large scale networks.

2.6.1 Area-Based Routing Algorithms Comparison

As covered above, area based routing algorithms have advantages and disadvantages based on
the main objectives and scope of the network as shown in Table 2.7. Below the most relevant
aspects of the proposed algorithms are discussed.

Table 2.7: Comparison of hierarchical area-based routing algorithms.

Location
Global
Informa-
Formation Knowledge of Hierarchical Leader
Algorithm Structure tion for
Method Nodes Structure Selection
Electing
Position
Leader
Two equal parts by
a line of nodes for
data storage and
lookup. Sink sends
Leader for
the query to the
LBDD [72] Area Required One line storage of Required
inline nodes for
data.
data, until to the
storage, the storage
will then send to
sink.
Rotation
Ring One closed
Area Similar to LBDD Required among ring Required
Routing [73] circle line.
nodes.
Only one rail Leader for
Non-
Railroad [74] Area Similar to LBDD. Required located in the storage of
required
middle area. data
Virtual Line
Structure for data
storage (ring Leader for
VLDD [75] Area structure). Required Line or ring. storage of Required
Calculate the group data
region based on the
location of sink.

2.6.1.1 Scalability

VLDD is most suitable for large area networks because the virtual line structure can be reorga-
nized according to the situation of a particular environment. It will also take into account the

33
 2.6 Hierarchical Area-Based Routing Algorithms

location of nodes [75]. Therefore, the formation of the virtual line is more flexible for adapta-
tion to different network sizes. As for the Railroad method, this is also suitable for large area
networks because the rail can be constructed in a flexible way and can be formed closer to the
source nodes [74]. Concerning Ring Routing, this is less suitable for large area networks if the
ring is too small. However, if the ring is too large, the distance of the inner source nodes to
the ring is also large. Therefore, it is less suitable for large area networks when compared with
VLDD and Railroad. LBDD is the worst case in terms of scalability because there is only one
vertical straight line created in the middle of the network to store the data from the source
nodes. Without considering the location of nodes, the distance between the sources nodes and
the vertical line may be very long, and it is not suitable for large scale networks [72].

2.6.1.2 Energy Efficiency

VLDD is the most energy efficient because the virtual line created is based on the location of
the nodes, and can thus shorten the distance between the source nodes and the virtual line.
The nodes on the virtual line can be swapped. This helps to balance the energy consumption
of nodes [75]. The Railway method is also good for energy efficient transmission because the
Railway can be formed closer to the source nodes and the data can be transmitted for a shorter
distance. Besides, a unicast method is used instead of flooding, which can reduce unnecessary
transmissions and save energy [74]. As for Ring Routing, the source nodes inside the ring may
need to send data to the nodes on the ring for a longer distance, and therefore it will consume
more energy [73]. LBDD is the worst in energy efficiency because the vertical line is formed in
the middle of the network, and the sources nodes need to send data to the line from a longer
distance [72]. Besides, the leader nodes on the line send requests to each other on the line in a
flooding manner and this would waste a lot of energy.
Finally, Table 2.8 shows a comparison among hierarchical routing specify protocols based on
load balancing, data aggregation, delay, suitability for large scale, mobility and implementation
cost while highlighting the relevant key features of each protocol.

34
 Table 2.8: Comparison of hierarchical routing protocols relevant features.

Suitable
Data Time for Large Implem.
Algorithm Structure Load Balancing Mobility Remarks
Aggr. Delay Scale Cost
Network
PEGASIS [60] Chain Medium X Very Long Low 7 Low Long distance between the leaders and sink.
Poor (Energy consumed CHs send to next level long distance
CCS [61] Chain 7 Long Low 7 Low
more for nodes near BS) between the leaders and sink.
Equal data and short distance only CHs
Medium (Because of
EBCRP [62] Chain 7 Long Low 7 Low directly send to BS. Long distance between
switching CH)
the leaders and sink.
Medium (The areas
CHIRON [63] Chain 7 Short Low 7 Low Multihop
divided may be uneven)
EADAT [64] Tree Medium 7 Long Low 7 Low NA
Poor (Remaining energy
Not affected by node failure use remaining
of nodes is not
BATR [65] Tree 7 Long Low 7 Low energy to select the optimal path (minimum
considered for data
spanning tree).
transmissions)

2.6 Hierarchical Area-Based Routing Algorithms

Remaining energy of nodes is considered for
PEDAP [66] Tree Good X Medium Low X High
load balance.
Poor (Remaining energy
ETR [67] Tree of nodes is not 7 Medium Medium 7 Low Node failure.
35

considered)
Good (Each node can
Supports asynchronous applications. Huge
PANEL [68] Grid act as aggregator with Y Medium Medium 7 High
computation cost.
equal chance)
Event-driven applications, queries on
TTDD [69] Grid Good 7 Very Long Medium X High demand, crossing point and flooding. If the
mobile sink moves fast, there is problem.
HGMR [70] Grid Medium 7 Medium High 7 Low Hierarchical structure with different roles.
Master node keeps the routing table.
GMCAR [71] Grid Poor 7 Medium Medium 7 High Multiple paths for high traffic, and single for
low.
Poor (Rely on the line Broadcast is used. Flooding is needed, this
LBDD [72] Area only. The wideness of 7 Medium Medium X High may cause high traffic. Not good for high
the line is limited) traffic.
If one node is died, the whole ring broke and
Ring Good rotation of ring
Area 7 Medium Medium X High rotation is needed. High computation
Routing [73] nodes
overhead.
Unicast is used. Bottleneck is avoid because
Railroad [74] Area Good 7 Long Medium X High
the rail is large.
VLDD [75] Area Poor 7 Long Medium X High Not good when line structure
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

2.7 Low-energy Adaptive Clustering Hierarchy (LEACH)

Routing Algorithm
LEACH is the best-known protocol in clustering in WSNs [41]. In this protocol, the cluster
heads are selected based on their energy threshold value, they send advertisement messages with
CSMA protocol to the whole WSN as shown in Figure 2.7. For each sensor node, it will join
the cluster from which it receives the strongest invitation message. Next, cluster heads prepare
a TDMA scheduling program to manage data transfer from cluster members. This will prevent
data collision and reduce energy consumption. Finally, the TDMA schedule in cluster nodes
will be received. After that it goes to the steady state phase. Sensors send their specific data
to respective cluster heads and cluster heads receive, aggregate and finally send them to the
base station. LEACH is the commonly applicable hierarchical clustering algorithm for designing
energy efficient WSNs which aim to reduce the power consumption over the whole WSN. For
LEACH, the selection of cluster heads are rotated among the nodes in a cluster based on a specific
period of time. Each cluster head will gather the data and transmit it to the base station. The
clustering method can extend the lifetime of the WSN [13, 14]. Moreover, LEACH also uses
aggregation techniques to combine the original data into a smaller packet size for transmission
so that only the required information will be aggregated and forwarded to the base stations in
order to save energy and bandwidth.

Figure 2.7: Representation of LEACH routing protocol strategy (S=Sink, L=Leader).

LEACH is a self-organizing clustering protocol, it applies randomization methods for selecting

cluster heads, and then tries to share the energy burden evenly among the sensors in the network.
In LEACH, the nodes are divided into clusters, where one node in each cluster acts as the
cluster-head. All nodes in the network have a equal chance to be a cluster head where LEACH
includes randomized function of the rotation of cluster heads in order to avoid the quick energy-
depletion of nodes. Cluster heads in LEACH broadcast their roles to other neighbouring sensors
nodes in the vicinity. Sensor node join different cluster head based on the minimum required
communication energy. After all sensor nodes join the clusters, each cluster head creates a

36
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

transmission schedule for the nodes in its cluster (the nodes are called cluster members). Unless
a node needs to transmit data, the radio module of a sensor node is turned off to save energy.
After a cluster head collects data from its member nodes, the cluster head aggregates the data
and then transmits it to the base station. The operation of LEACH is divided into rounds,
where each round begins with a set-up phase for cluster formation, then data is transmitted
from member nodes to cluster head and then finally to base station. To form a cluster, a node
is elected as the cluster head for the current round based on the desired percentage of cluster
heads in the network and the round number. The selection is made based on a Bernoulli process
with a success rate given by [139; 140; 141; 142; 143; 144],

p
Tn = (2.1)
1 − p(rmod( p1 ))

where n is the node index, p is the desired percentage of cluster heads, r is the round number.
Using this selection probability, each node will be a cluster-head at some point within 1/p
rounds. In the first round, i.e. r = 0, each node has an equal probability p of becoming a
cluster-head. The nodes that are cluster-heads in round 0 cannot be selected as cluster-heads
for the upcoming next 1/p rounds. Thus the probability that the remaining nodes are cluster
heads must be increased, since there are fewer nodes that are eligible to become cluster-heads.
After 1/p − 1 rounds, Tn = 1 for any nodes that have not yet been cluster-heads, and after
1/p rounds, all nodes are once again eligible to become cluster-heads. The cluster heads for the
current round broadcast an advertisement message to the rest of all nodes. For this broadcast,
the cluster heads use a carrier-sense multiple access (CSMA) protocol. All non cluster head
nodes must keep their receivers on during this phase to receive the advertisements of cluster
head nodes. Each sensor node decides to join which cluster for this round based on the received
signal strength of the advertisement, this will allow the nodes to join cluster heads with minimum
amount of required transmitted energy for communication. During the cluster Set-Up Phase,
after each node decided to join a particular cluster head, it then informs the cluster head node
that it will be a member of this cluster by transmitting a join request back to the cluster-head
also using CSMA MAC protocol. During this phase, all cluster head nodes must keep their
receivers on for receiving the join requests. The cluster-head node receives all the messages for
nodes that would like to be included in the cluster. Based on the number of nodes in the cluster,
the cluster-head creates a TDMA schedule telling each node when it can transmit the payload
data. This schedule is broadcast back to the nodes within the cluster.
After the cluster formation is completed and the TDMA schedule is determined, data trans-
mission can begin. Each sensor node sends data during their allocated transmission slot to the
cluster head. The radio of each sensor node can be turned off until its allocated transmission
time slot begins in order to minimize the energy consumption of sensor nodes. The cluster head

37
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

must keep its receiver on to receive all the data from the nodes in the cluster. When all the data
has been received, the cluster head sends the data packets to the base station.
Radio is simply a broadcast medium. Transmission in one cluster will affect communication
in a nearby cluster due to interference. To reduce the effects of interference, each cluster com-
municates using different CDMA codes. Thus, when a node decides to become a cluster-head,
it chooses randomly from a list of spreading codes. It informs all the nodes in the cluster to
transmit using the same spreading code. The cluster head then filters all received data packets
by using the given spreading code. Thus neighboring clusters radio signals will be filtered out
and do not corrupt the transmission of nodes in the cluster.
The role of cluster head is rotated. Thus every node will have an equal chance to act as cluster
head in order to avoid depleting individual nodes and losing sensors. No global information of the
network is required. This protocol can greatly reduce the energy used for data communications
between sensors within its clusters and other cluster heads. It can also switch the non-active
sensor nodes into sleep mode to save energy. LEACH uses single-hop routing methods for data
transmission such that each node can send data to the cluster head which then gathers the data
and sends it directly to the base station. The cluster heads will consume a lot of energy when
they are located far away from the base station and it is therefore not feasible to implement
this routing algorithm into large scale WSN applications. Furthermore, the idea of rotation of
cluster heads constitutes an extra power overhead for the whole sensor network, e.g. cluster heads
rotation, advertisements broadcasts etc, which may also lower the power available to sensors.
In addition, LEACH may not guarantee a fair and uniform cluster head distribution based on
remaining energy because cluster heads are selected randomly.

2.7.1 Latest Improvements on LEACH

LEACH is one of the basic protocol used for clustering in WSN, and there are researchers pro-
posed using low power techniques with modified LEACH protocol for Zigbee network [145]. Al
Essa, Ali, et al. performed Enhancement and performance analysis of LEACH algorithm in
IOT [146]. Behera, et al. proposed Energy-efficient modified LEACH protocol for IoT applica-
tion [147]
ZigBee is a wireless network protocol for low-speed and short-distance transmission. It adopts
IEEE 802.15.4 as a technical standard which defines the operation of low-rate wireless personal
area networks. It specifies the physical layer and media access control for the networks. Zigbee is
one of the communication protocol which further extends the standard by developing the upper
layers. ZigBee network can adopt a cluster tree topology including a coordinator (root), routers
(master) and end devices (leaf). End Devices in the network can send and receive messages.
A tree network needs at least one router to extend the network coverage. The main tasks of a
router are to allow leaf nodes to connect to it, relay messages from one leaf node to another,

38
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

and aggregate the information of the network and send it to the coordinator.
The network scale in IoT applications is getting larger and larger, and the number of net-
work nodes is also increasing. Thus, clustering mechanism can be utilized to save the energy
consumption in data transmissions. The clustering idea was introduced into ZigBee network
to reduce the overall data traffic of the network, The clustering mechanism can extend the the
whole network lifetime by data fusion and efficient energy utilization.
Many researchers have proposed some improvements on LEACH by also considering energy
requirements. The energy levels may refer to the initial, current, average or total energy. Other
variations may use the distances, number of clusters, area of coverage, cluster size, and moving
window size. Below the latest improved LEACH versions are summarised in Table 2.9:

39
 Table 2.9: Comparison of latest improved LEACH algorithms for the cluster formation.

2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

Optimum Distance No of Cons.
Number of Moving Uniform
Number of Initial Current Average Total between Area of Rounds have
Algorithm Nodes and Window Cluster
cluster Energy Energy Energy Energy Nodes and Coverage not been a
Clusters Size Size
heads Sink Cluster Head
LEACH [41] 7 7 7 7 7 7 7 7 7 7 7
A-LEACH [42] X X X 7 7 X 7 7 7 7 7
Leach-B [43] 7 7 X 7 7 X 7 7 7 7 7
C-LEACH [44] X X X 7 7 7 7 7 7 X 7
LEACH-CE [45] X 7 X X 7 7 7 7 7 7 7
DCHS-L [46] 7 X X 7 7 7 7 7 7 7 X
E-LEACH [47] X X X 7 7 X X X 7 7 7
H-LEACH [48] 7 X X 7 7 X 7 7 7 7 7
I-LEACH [49] X 7 X X 7 X X X 7 7 7
K-LEACH [50] X X X 7 7 X X X 7 7 7
MAP-L [51] X 7 X X 7 7 7 7 X 7 7
N-LEACH [52] 7 X X 7 7 7 7 7 7 7 7
P-LEACH [53] X 7 X X 7 7 7 7 7 7 7
Quadrature
7 7 7 7 7 X X X 7 7 7
LEACH [54]
LEACH-SWDN
7 X X 7 7 7 7 7 7 7 7
40

[55]
T-LEACH [56] X 7 X 7 X 7 7 7 7 7 7
U-LEACH [57] 7 7 X 7 7 X X X 7 7 7
W-LEACH [58] 7 X 7 X 7 X X X X 7 7
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

• Ali et al. proposed A-LEACH, which uses the initial energy and current energy of nodes
for calculating the energy factor. It also considers the most suitable number of clusters
among the total number of clusters in selecting a cluster head [42].

• Mu Tong et al. proposed Leach-B. It maintains a desired percentage of cluster heads.

For the first round, cluster heads will be selected randomly. After that, if the number of
clusters is less than the desired percentage, the node with shorter time interval will be in
higher priority to be a cluster head, and the time interval is inversely proportional to the
nodes residual energy. The value of time interval is set as t = k/E, where k is a selected
factor and E is the residual energy of each node. That means the nodes with highest
residual energy will be the cluster heads. On the contrary, if the total number of clusters
is higher than the desired number, then the cluster heads with lowest residual energy will
become normal nodes [43].

• Mehta et al. introduced C-LEACH, the author considered the balancing of the clusters’ size
which is uniformly distributed across the whole network, with considering the minimum
and maximum number of members in each cluster and sets a threshold value for them.
Moreover, C-LEACH also considers the current and initial energy of nodes [44].

• M. Tripathi et al. proposed LEACH-CE. According to the LEACH-C algorithm nodes with
higher than the average nodes energy are selected as cluster heads. However, since nodes
with residual energy higher than average do not imply the highest energy, cluster heads
may also die quickly if their energy was only a little above average. Therefore, LEACH-CE
suggests to select the node with maximum residual energy in the cluster as the final cluster
head in order to balance the energy usage of nodes [45].

• Handy et al. proposed Deterministic Cluster-Head Selection (DCHS), it selects the cluster
head not only based on the current and maximum energy, but also considering the number
of rounds that a node has not been a cluster head [46].

• Xu et al. proposed E-LEACH, which also considers the current and initial energy of nodes.
However, when calculating the probability of a cluster head it consider the distance to the
base station and area covered [47].

• Azim and Mohammad proposed Hybrid LEACH (H-LEACH), it calculates the differences
between the current and initial energy and also uses the number of clusters and total
number of nodes in determining the selection of cluster heads [48].

• Beiranvand et al. proposed I-LEACH, which considers the residual energy, distance to
the base station, and number of neighbor nodes in selecting a cluster head. I-LEACH
will compare each item above with the average of all nodes in the network. Therefore,

41
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

the nodes with higher residual energy, the shorter distance to the base station and more
number of neighbor nodes will have a higher opportunity to become a cluster head [49].

• Udompongsuk et al. proposed a hybrid approach to enhance the cluster head selection
probability and with moving window instead of using either initial or maximum energy
and the protocl is therefore called Moving window Average and selection Probability or
MAP [51].

• Li et al. proposed N-LEACH which uses the current and initial energy to do the calculation.
However, it uses its own determined probability in selecting the cluster heads rather than
considering the number of cluster heads and the total number of nodes [52].

• Ke-yin et al. proposed PLEACH which considers the average energy of all nodes. It will
compare the current energy of a node with the average energy of all nodes. If a node has
a higher positive remaining energy compared to the average energy, then it will be have a
higher priority for selection as a cluster head [53].

• Manzoor et al. proposed Quadrature LEACH. Each node will send its location information
to the base station. Quadrature LEACH divides the whole network area into four equal
regions according to the nodes location. Within each quadrant, some cluster heads are
selected to coordinate the data transmissions of its members. This can balance the loading
of cluster heads and with better coverage [54].

• A. Wang. et al. proposed LEACH-SWDN which uses nodes residual energy to select
cluster heads. To keep a stable number of cluster heads, LEACH-SWDN uses a sliding
window control for the cluster heads selection criteria. The algorithm will dynamically
selecting cluster head according to the number of alive nodes. It will consider the initial
energy of the nodes, and the average energy of alive nodes which have not been a cluster
head for that particular cycle [55].

• Hou et al. proposed T-LEACH. To calculate the probability of being a cluster head, the
author uses the total energy of all nodes, rather than the initial energy of each node.
Moreover, it also takes the distance between the member nodes within its cluster into
consideration, in addition to the number of nodes and cluster heads [56].

• Ren et al. proposed U-LEACH because generally cluster heads further away from the base
station will consume more energy in data transmission because of the longer transmission
distance. Therefore, U-LEACH proposes dividing the network into concentric circles, and
the clusters which are most far away from the base station should have a smaller cluster
size. Conversely, as clusters come closer to the base station their size increases. Residual
energy, distance and weight factors are also considered in selecting a cluster head. This is

42
 2.7 Low-energy Adaptive Clustering Hierarchy (LEACH) Routing Algorithm

done to reduce hotspots, i.e. higher use for cluster heads which are far away from the base
station [57].

• In et al. proposed W-LEACH [58] based on K-LEACH [50]. Instead of using the current
energy, W-LEACH uses the moving average energy consumption based on the window size
of the energy. It can consider that a node may consume different amount of energy due to
various conditions of sensors.

As explained above these algorithms provide several improvements over LEACH by taking
into account additional metrics for implementing the routing techniques as summarized in Ta-
ble 2.9, which compares the latest improved LEACH algorithms for cluster formation. Notice
that several works have analyzed more in deep the performance between LEACH and specific
routing protocols [148; 149; 150]. The comparisons are done on the basis of different factors that
have to be considered while choosing methodology for particular project of WSN.

43
Chapter 3

Energy Efficiency Routing

Protocols: Design and
Considerations

In this chapter, we discuss the relevant constrains and design considerations of routing protocols
for WSN. Then, we propose our multi-criteria energy efficiency routing protocols that take
into account the constrains of WSN. We develop a clustering algorithm based on LEACH and
introduce (i) remaining energy, (ii) distance to nodes, and (iii) distance to base station as the
criteria in forming a cluster. After that, a flexible and adaptive multi-hop routing algorithm
is proposed by adopting an energy efficient distance for selecting the next hop of cluster head.
Furthermore, a temporary routing node is selected to act as the intermediate node for routing
in case if there is no next cluster head available within the energy efficient distance. This can
help to prevent data packet loss, and maintain a higher successful delivery rate.
Since the constraints of energy, memory and computational power of the WSN nodes, en-
ergy efficiency protocols and algorithms are the most challenging topics in WSN. Because of
the increasing demands of various WSN applications in IoT, many studies have focused on
these areas in recent years. Behera, Trupti Mayee, et al. proposed an energy-efficient routing for
greenhouse monitoring using heterogeneous sensor networks [151], and IoT-based Environmental
Monitoring [152]. Ahmed, Awais, et al. [153] suggested an energy-efficient sensor network rout-
ing protocol for large distributed environmental monitoring applications. AWAIS, Muhammad,
et al. [154] discussed energy efficient routing protocols for void hole alleviation in IoT-enabled
underwater WSN. Javaid, Nadeem, et al. [155] also proposed an balanced energy consumption
adaptive routing for IoT-enabled underwater WSNs. Moreover, Lazarevskal, Maja, et al. [156]
suggested an energy-efficient routing protocol for IoT-based healthcare mobile applications. Fi-

44
 3.1 Design Considerations on Routing Protocols for WSN

nally, analyze and propose a security technique that can easily be incorporated in the proposed
routing protocols.

3.1 Design Considerations on Routing Protocols for WSN

The recent rapid development of WSNs has increased the range of WSN applications and their
scale [24]. Popular examples are smart grids and renewable energy systems in which large
quantities of data are required to be collected. Gungor et al. [157] discussed the opportunities
and challenges of using battery-powered wireless sensor networks in Smart Grid. WSN is also a
cost-efficient tool for residential energy management in the smart grid [158]. It helps to manage
peak load and optimize electricity generating resources. With the growing size of smart grids,
there are increasing challenges in maintaining a networks’ performance, reliability, stability,
economy of scale [24; 159]. WSNs can give many benefits over traditional communications used
in existing electrical power systems. WSNs have been increasingly adopted as a useful technology
to improve different areas of electric power systems, finding applications in the various stages
of electricity generation, delivery, and utilization [160; 161]. Another application for WSNs in
the same context is their integration into everyday consumer electronics and appliances with the
aim of smoothing peak demand spikes in at the consumer level. The challenges are in designing
reliable WSN systems in terms of security, energy efficiency and adaptability.

3.1.1 Criteria of a Robust Routing Algorithm for Wireless Sensor Net-

work

The communication infrastructure of smart grid applications between energy generation, trans-
mission, and distribution and consumption requires two-way, end-to-end reliable and secure
communications [162]. A robust wireless sensor network is often defined as invariance degree
of state, behavior, and function under interference or disturbance. There are several criteria to
determine a robust WSNs, and the degree of importance of each criteria will vary with different
applications [163]. Here, we focus on the routing algorithm, and determine a robust routing
algorithm in WSNs should be energy efficient, scalable, reliable, self-organized, and secured.

• Energy Efficient: For the smart grid environments, the connectivity and mobility of the
nodes are low, but they may not have sufficient energy and memory for processing. There-
fore, an energy efficient routing algorithm is important to select suitable cluster heads in
clustering, and reduce the number of transmissions in order to balance the energy consump-
tion of the whole network. Energy source of sensors are mainly from a battery, it is hard to
change or recharge their batteries because of the great amount of sensor nodes in often a
hostile and hazardous environment such as IoT-based underwater applications [154; 155].

45
 3.1 Design Considerations on Routing Protocols for WSN

Besides, they are often difficult to be accessed. Therefore, it is crucial to apply energy-
efficient routing algorithms in order to reduce the energy used by sensor nodes and extend
the lifetime of the whole WSN [20; 21; 22].

• Scalability: A smart grid should be scalable to facilitate the flexibility of adding and
removing of sensors for the power grid [164]. Many smart meters, smart sensor nodes, smart
data collectors, and renewable energy resources are joining the communications network.
Hence, smart grid should handle the scalability with the integration of advanced web
services. The number of sensor nodes deployed in a WSN may range from tens, hundreds,
or even tens of thousands of nodes. Thus, when designing the routing algorithm, it should
be scalable for different network sizes [165; 166], such as utility metering applications with
million of devices connected for collecting data.

• Reliability: Reliability is defined as the probability of a packet being successfully delivered.

WSNs reliability is influenced by many factors such as component failure, environmental
influences, task changes, and network update. Reliability is also affected by congestion
therefore, depending on the application, congestion control mechanisms in the routing al-
gorithm are almost mandatory [167]. There are always conflicts between reliability and
efficient energy consumption because a reliable routing algorithm should consume more
energy. Therefore, some balancing approaches between the two criteria needs to be consid-
ered. Due to the harsh conditions of smart grid environments, such as background noise,
high attenuation, and wireless propagation phenomena, WSNs suffer from easily broken
links and intermittent connectivity [168; 169]. When designing a reliable routing protocol,
Reliability is also a critical factor for evaluating WSNs performance to maintain end-to-end
reliable transmissions.

• Self-organization: Self-organization with route maintenance is critical in smart grid appli-

cations in order to enable prevention and corrections of faults. Multiple routes can be used
alternatively when a route fails. This method is suitable in tree-based routing since mul-
tiple routes are already existed in the routing table. This can have a significant impact to
guarantee the normal operation when there is network failure. After sensor nodes are de-
ployed in the network environment, sensors should be able to re-organize themselves if and
when nodes fail or the network topology changes. Adaptive routing protocols able to follow
the real time topology change need to be deployed in such dynamic scenarios [170; 171]. In
sensor networks, sensor nodes can join or leave a cluster in different iterations, which will
change the node density and network topology of the newly formed cluster. Thus, routing
algorithms used for sensor networks should be flexible enough to cater for the frequent
changes of members of a cluster [172].

46
 3.1 Design Considerations on Routing Protocols for WSN

• Security: Secure information exchange are extremely vital for smart grid applications,
especially for billing purposes and grid control [173] A sensor network can be used to
deliver personal and private data. So, a secured data communication network is essential
for data transfer in order to protect the data from being copied, destroyed or altered in
the path. Routing protocols should not exclude security in their operations [174; 175].

3.1.2 Constraints of Wireless Sensor Networks

It is always hard to balance the ideal criteria for an ideal robust WSN, and especially when the
requirements of specific WSN applications add additional constraints [176]. The more relevant
constrain are depicted in Figure 3.1 and discussed below:

Figure 3.1: Most relevant constrains of WSNs.

• Limited and unstable energy supply: The number of dead nodes is an indication of the
energy management across the entire WSN. This is because the energy source of sensor
nodes are usually batteries which have a limited life. Networks may be used in environ-
ments, where it is difficult to change node batteries. Thus the main challenge consists in
designing energy efficient routing algorithms for WSNs which balance the available energy
of the entire network in the most efficient way [20; 21; 22].

• Massive, random, and varying node deployment: Deployment of sensor nodes can be static
or random which calls for different performance requirements and routing algorithms. In
many applications, sensor nodes can be scattered randomly or sparsely distributed over
an area. If the distribution of the sensor nodes are changing from time to time, optimal
routing algorithms need to be able to adapt to this changing network topology to manage
the whole sensor network in an energy efficient way [177].

• Unreliable network environment: Some sensor nodes may be unreliable because of physical
damage, malfunction, or lack of energy. This affects the performance of the WSN. Ideally
the routing algorithms should be able to reconfigure themselves around dead or unreliable
nodes [178].

• Scalability: Routing algorithms should adjust to different scales of the network. Sensor
nodes may also be equipped with residual energy sensing ability, or special processing, and

47
 3.2 Simple Energy Efficient Routing Protocol

communication functions. Also, physical communication paths between different sensor

nodes may vary. The ideal routing algorithm should be flexible enough to consider these
different parameters in a changing environment [165; 166].

3.2 Simple Energy Efficient Routing Protocol

Centralized hierarchical routing algorithms are widely adopted and used in IoT applications,
and it is further discussed as follows.
Yang, Guojie, et al. [179] proposed a centralized cluster-based hierarchical approach for
communication in a smart healthcare system for monitoring patients. The base station makes
the decision about cluster head election. The health monitoring devices are partitioned into
clusters. Each cluster is administered by a cluster head, which is selected by a centralized base
station. This transfer of control from nodes to the base station ensures that the overall energy
of the network is utilized efficiently.
Awan, Kamran Ahmad, et al. [180] suggested a stable and centralized trust-based clustering
mechanism for IoT enabled vehicular ad-hoc networks.The trust-based clustering is a centralized
approach that allows the sinks to choose a trustworthy CH for secure and authentic clustering.
The sinks maintain and calculate the degree of trust to select a CH.
Al-Janabi, Thair A., et al. [181] developed a centralized routing protocol With a scheduled
mobile sink-based artificial intelligence for large scale IoT. It uses a centralized architecture
based on the software-defined network and cloud technologies to reduce network complexity and
the overheads generated by the nodes in the route identification procedure. It can mitigate the
overall energy consumed by the nodes and thus, prolonging the overall network lifespan.
In this section, we propose a simple and effective energy-efficient routing protocol that op-
timizes the energy efficiency of the whole WSN. The goal of the protocol is to minimizes the
energy consumption of long-distance transmission for large scale WSN applications.

3.2.1 Assumptions of Our WSN Model

For the WSN applications in the smart grid, it is assumed that all sensors are static. Our
proposed centralized clustering and routing algorithm is managed at the base station, and the
base station maintains all the information of sensor nodes, such as location and remaining energy
for cluster head election. If there is any adding or removal of sensor nodes, the base station will
update its information. We defined the WSN model with homogeneous sensor nodes and multi-
hop transmissions. Sensor network N = (S, E) consists of a set of sensors S = s0 , s1 , s2 , ..., sn .
Each sensor si has a unique identification number IDi , whereas s0 is the only base station in
the sensor network. All sensor nodes and base station are located in the same two-dimensional
plane. We assume that each sensor si has a circular communication area, where radius is rc

48
 3.2 Simple Energy Efficient Routing Protocol

with, and can communicate with other sensors or the base station within the communication
area. If two sensors, si and sj , can communicate each other, there is a communication link lij
established. Two sensors are called connected if there is a path established between two sensors
in N. All computation of cluster formation and routing algorithms are centralized and managed
by the base station. Our WSN model considers the following assumptions:

• There is one Base Station (BS), known as the sink in the WSN, and its power, storage,
communication and computation resources are unlimited. BS is located at the center of
the network area.

• All sensor nodes, n, are homogeneous. They all have the same propagation range, storage
capacity, battery power, communication range and processing capabilities.

• The sensors nodes are randomly distributed and static for the whole WSN.

• All sensors know their physical location with GPS equipped or pre-configured.

• The sensor nodes are grouped into clusters, and there is one cluster head in each cluster
(the process will be explained in the following section).

• Cluster members can transmit messages to their cluster head or directly to the BS. Cluster
head can then sends data directly to the BS, or though multi-hops by sending data to next
cluster heads, and finally to the BS.

• A round is defined as when every node in a cluster completes one-time data transfer to
the cluster head for existing TDMA schedule which is broadcast to its members by the
cluster heads. The time slot for each transmission by a node is the same, and a node
needs to wait until the round is ended. The round ends after all the sensors send data to
the cluster head, and it goes to the next round. Then, the nodes will be informed a new
TDMA schedule in the next round by its cluster head.

3.2.2 Cluster Formation Algorithm

The sensor nodes are distributed randomly in the network area. One cluster head is selected in
each cluster as shown in Figure 3.2. To further improve the drawback of LEACH which selects
the cluster heads randomly, the proposed cluster formation is based on the remaining energy, the
node density, the distances between nodes, and the distances to the BS. The remaining energy
of each sensor node is used in the selection of the cluster head. In this sense, the selection of a
node without sufficient remaining energy and far away from the centrality of all node members
being a cluster head can be avoided. Finally, the cluster head’s role will rotate if its energy falls
below a threshold. The flowchart for our proposed cluster formation algorithm is depicted in
Figure 3.3 and explained in detail in the following:

49
 3.2 Simple Energy Efficient Routing Protocol

Figure 3.2: Cluster formation in a wireless sensor network

1. First, initialize the startup energy of every node, the total number of sensor nodes, and
the location of the base station.

2. To reduce the energy consumption and overheads in cluster head rotation and cluster
formation. A cluster head is only rotated when its energy is below the average energy
of all sensor nodes in the whole network. WHILE it is the first round of cluster head
selection or the cluster heads remaining energy less than the average residual energy of
the whole sensor network. The BS re-calculates the average energy (E), of the current
network. Where the E is given by equation 3.1:

n
1X
E= Ei (3.1)
n i=1

Where, n is the total number of nodes.

3. If the energy of the node is greater than E, then that particular sensor node has the
chance to be selected as a cluster head and will be put into the cluster head candidate list.
Otherwise, it will be considered as a normal sensor node and act as a cluster member.

4. Next, the system extracts the sensor nodes from the cluster head candidate list, and
calculates the number of nodes within the intuitive cluster area. To ensure the cluster
head can receive data from all members within the energy efficient distance, the cluster
head should be located at the centre of the cluster area like a circle. Therefore, the intuitive
cluster area of a cluster head can be calculated with the energy efficient distance as the
radius, The area is calculated by equation 3.2:

Ad0 = d20 π (3.2)

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 3.2 Simple Energy Efficient Routing Protocol

Figure 3.3: Selection of cluster head candidates (CH)

based on the energy-efficient distance equation 3.3:

s
Ef s
d0 = (3.3)
damp

defined in the first order radio model [182; 183; 184; 185; 186]. where, Efs is the energy
required for amplification of transmitted signals to transmit a one bit in open space, and
Eamp is the energy required for amplification of transmitted signals to transmit a one bit
in long-distance path models. It will then update the cluster heads candidate list.

5. Furthermore, it measures the centrality of a cluster head by calculating the average dis-
tances from the elected cluster head to all other nodes based on equation 3.4. Centrality

51
 3.2 Simple Energy Efficient Routing Protocol

of a cluster head denotes how central to all other nodes that the elected CH is among all
other elected CHs within the entire network. The lower the average distance, the higher
the centrality of a cluster head, the higher probability that the elected one can be a cluster
head. Centrality of a cluster head is calculated as the average of the distances to all its
neighbour nodes.
n
1 X
DtoALLi = dij (3.4)
n i=1,j=1
q
2 2
where, dij = (xi − xj ) + (yi − yj ) . It also measure the proximity to the Base Station
by comparing its distance to the base station with the average distance of all cluster heads
to the BS as calculated by equation 3.6:

n
1X
DtoBS = DtoBSi (3.5)
n i=1

q
2 2
where, DtoBSi = (xi − x0 ) + (yi − y0 ) . The candidate with shortest distance to the
base station have the highest proximity value, and will be the cluster head. The eligibility
value, ELi , of a elected cluster head is based on the centrality of all nodes and the proximity
to the base station.

 DtoALL  DtoBS
ELi = (3.6)
DtoALLi DtoBSi

After base station completes the selection of cluster heads, the base station broadcasts this
information in the sensor network. The base station sends the cluster head ID to every sensor
node, and if the received cluster head ID matches its own sensor node ID. Then, the node becomes
a cluster head. Otherwise, it acts as a normal node. This centralized cluster heads selection
mechanism can reduce the energy consumption and overheads of all nodes in the network when
it compares to relying the normal nodes to do the computation themselves.
To join a cluster, every sensor node calculates its distance between all cluster heads and the
BS, this is depicted in Figure 3.4. If the distance to the BS is the shortest, then the sensor
node does not join any cluster. Otherwise, the sensor node will join the cluster with the shortest
distance, and the system will update the membership of the clusters. The joining cluster process
will continue until all sensor nodes are considered. If the number of sensors within a cluster
exceeds the maximum member size, then assign one more cluster head for the group based on
the candidate list.

3.2.3 Multi-Hop Routing Algorithm

Instead of single hop transmission as LEACH, which induces packets loss due to a transmission
of data to the base station is outside the sensing range of a node. Thus, multi-hop transmission

52
 3.2 Simple Energy Efficient Routing Protocol

Figure 3.4: Procedure for joining the cluster in WSN.

within an energy efficient distance is proposed in our routing algorithm. And, at the same time,
we preserve the beauty of single hop transmission to reduce the number of times of transmissions
under the condition that the distance of a cluster head to the base station is within the energy
efficient distance . Besides, it is not compulsory for a node to join a cluster if it is very close
to the base station. It can present a data packet being sent back and forth to reduce the data
packets travelling distance, save energy consumption and reduce time delay.
In order to save energy the network is divided into multiple clusters. The cluster head
node collects and aggregates information from its neighbors. Then, cluster heads deliver the
information collected from its cluster members to the next cluster head, and finally to the base
station through minimum number of hops with reference to the energy efficient distance. It
extracts the eligible cluster heads information from its database, and selects the next targeted
cluster heads within its energy efficient transmission range and also the closest to the base
station. It does not select the next cluster head with the shortest distance because it involves
greater number of cluster heads for the whole transmission. Therefore, our proposed routing
algorithm described in Figure 3.5 can avoid redundant transmissions and save communication
costs.
It can also relieve the stress of the hotspot or bottleneck issues because our proposed cluster-
ing formation algorithm selects the candidates with higher proximity to the base station as the

53
 3.2 Simple Energy Efficient Routing Protocol

cluster heads, and more available cluster heads are located closer to the base station to share
the burden. Moreover, the cluster heads are rotated based on the remaining energy in order to
balance the loading of all sensor nodes in the network. However, if a sensor node is close to the
BS i.e. within the distance of d0 , DtoBSi < d0 , it can send data to the BS directly in order
to save energy. The diagram for the proposed routing algorithm is depicted in Figure 3.5 and
explained in the following:

Figure 3.5: Multi-Hop transmission with energy efficient distance.

Cluster heads will do aggregation of the data collected from its members. The data is
aggregated in which data from different nodes measuring the same kind of data is combined into
one single smaller data packet by averaging the data. Instead of collecting and sending data
from all sensor nodes, the data aggregation mechanisms aim to remove the data replication, and
reduce the number of transmission of all sensor nodes in order to save the energy consumption,
and thereby increase whole network lifetime. Cluster heads are allocated a TDMA schedule for
transmitting data to the base station. After collecting data from its cluster members, cluster
will wait for a fixed period of time according to the schedule to avoid transmissions collisions
among other cluster heads. If the distance to the BS is shorter than other cluster heads, data
will be sent to BS directly. Otherwise, it will extract the cluster heads information from the
database. It selects the next targeted cluster heads within its energy efficient transmission range
and also closest to the BS.
For each round, the base station broadcasts the ID and the remaining energy of eligible cluster

54
 3.2 Simple Energy Efficient Routing Protocol

heads to all cluster heads. Each cluster head stores the information in its database. It then
estimate whether the receiving cluster head has sufficient energy or not before the transmission
to avoid packet loss [185; 187; 188; 189; 190; 191]. If not, another receiving cluster head will be
selected. After transmission, both cluster heads update their own remaining energy level. The
energy consumed for a sensor to transmit k − bits data over d meters is based on the first order
radio model [182; 183; 184; 185; 186] given by equation 3.7:

E(k, d) = Eelec k + Ef s kdr (3.7)

Where,

• Efs : energy required for amplification of transmitted signals to transmit a one bit in open
space,

• Eamp : energy required for amplification of transmitted signals to transmit a one bit in
long-distance path models,

• Eelec : the energy spent in transmitting and receiving data for a sensors electronics,

• r: path-loss exponent depending upon the network environment and the propagation model
used.

If a transmission distance is shorter than d0 [182; 183; 186; 192], the energy, E(k,d), used for
data transmission is defined as

E(k, d) = Eelec k + Efs kd2 if d < d0 (3.8)

Otherwise,
E(k, d) = Eelec k + Efs kd4 if d > d0 (3.9)

The equation is used in [182; 183; 186; 192]. The energy is consumed for a sensor to receive
k-bits data is given by equation 3.10:

Ereceive (k) = Eelec k (3.10)

Reference values are given in Table 3.1 as suggested in [185; 186; 193; 194]. The values are
widely adopted in various IoT applications. Rghioui, Amine, et al. use the data for measuring
human activities in ambient assisted living and e-health [195]. Sangwan, Aarti, et al. Delay
Tolerant Energy Efficient protocol for studied inter-BAN communication in mobile body area
networks [196]. Awan, Sabir Hussain, et al. developed smart energy control internet of things
based agriculture clustered scheme for smart farming. Behera, Trupti M., et al. intorduced an
hybrid heterogeneous routing scheme for improving network performance in WSNs for animal

55
 3.3 Multi-criteria Energy Efficient Routing Protocol

tracking [197]. Liu, Yuxin, et al. design and analyze the probing route to defense sink-hole
attacks for Internet of Things security [198]. Haseeb, Khalid, et al. developed an secure and
authentication-based sensor cloud architecture for intelligent Internet of Things [199].

Table 3.1: Model parameters

Radio Energy Model Value

Eamp 0.0013 pJ/bit/m4
Efs 10 pJ/bit/m2
d0 87 m
Eelec 50000 pJ/bit

3.3 Multi-criteria Energy Efficient Routing Protocol

After the description of our simple energy efficient in Section 3.2, we further construct the
threshold values of remaining energy and density combined with LEACH function. These new
threshold values are compared with the original LEACH and analysis are made in chapter 4.
Besides, we further improve the routing algorithm by assigning a temporary node in between
when there is no available routing node within the energy efficient distance. There is a detailed
description of the enhanced multi-criteria energy efficient routing protocol this section.
Optimization of WSNs depends on their physical deployment, and it will bring to the fore a
very focused number of parameters to be optimized. Self-organization of WSNs is an important
issue to be considered, and it requires the nodes to form a network by collaboration with each
other without using manual intervention. In this section, we propose an Multi-criteria Energy-
efficient Routing Protocol able to optimize the energy efficiency of the whole WSN.

3.3.1 Cluster Head Selection Method

It is assumed that all mathematical computations of cluster formation and routing are imple-
mented and centralized at the base station. For cluster head selection, we use LEACH as the
baseline. However, the cluster heads in LEACH is selected randomly, thus the nodes with lower
remaining energy or farther away from other cluster members may be selected as a cluster head.
It induces higher energy consumption for transmissions and higher failure transmission rates.
The equation of selecting a cluster head mentioned in the original LEACH is just based on the
factors: random number, round number, and probability of a cluster head to total number of
nodes. But, there is no parameter regarding energy of a node in the equation. Therefore, it
can’t ensure that a node presumably has more energy available than nodes become a cluster
head. The original LEACH can only try to share the role as a cluster head randomly by rotation
among all nodes, and avoid a node repeating as a cluster head frequently. However, it can’t
guarantee a node with higher remaining energy can be a cluster head. Therefore, we add the

56
 3.3 Multi-criteria Energy Efficient Routing Protocol

several metrics one by one on top of the basic LEACH to investigate the significance of each
metric. The flowchart of the proposed cluster formations is depicted in Figure 3.6 and Figure 3.7
explained in the following:

Figure 3.6: Flow chart of cluster formation.

1. Based on remaining energy and average energy of all nodes: The remaining energy
of each sensor node is record in the BS, it will calculate the average remaining energy of all
nodes. The average energy of all nodes is used because it considers the remaining energy of
all nodes in the network instead of just some focused nodes in order to balance the loading
of all nodes in the network. Besides, we do not select the highest remaining energy solely
as it may ignore the concern of the location of nodes.If a node has the remaining energy
higher than the average remaining energy of all nodes, it will have a higher chance to be
selected as a candidate of the cluster heads.

2. Based on distance to BS: The distance to the BS is also a factor to be considered as

a candidate of a cluster head. If the distance to the BS lower than the average distance
to BS of all nodes, it will have a higher chance to be selected as a candidate of the cluster
heads.

3. Based on distance to individual cluster members: Besides the distance to the BS,
the distances to all other nodes will also be considered. It is because if a cluster heads

57
 3.3 Multi-criteria Energy Efficient Routing Protocol

Figure 3.7: Flow chart of cluster formation (Continued).

location is closer to all other members within its cluster, the distances to the cluster head
from all other cluster members will be shortened, and therefore it can reduce the energy
consumption for data transmissions.

4. Based on the combined threshold value with LEACH: The factors with remaining
energy, distance to BS, and distance to cluster members mentioned above will be consid-
ered. These factors will be combined with LEACH function as given in equation 3.11:

p
T (L) = (3.11)
1 − p(rmod( p1 ))

in the way of individually to form a threshold value for consideration as a cluster head. If
a node with random value lower than the threshold value, it will be selected as a cluster
head.

Threshold value of remaining energy: (equation 11) is expressed in equation 3.12:

 p E
i
T (E) = 1 (3.12)
1 − p(rmod( p )) E

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 3.3 Multi-criteria Energy Efficient Routing Protocol

Where:

• p : Percentage of number of cluster heads in the network,

• r : Round number,

• Ei : Remaining energy of a node,

• E: Average Energy of all nodes,

Threshold value of distance to all nodes: (equation 12) is expressed in equation 3.13:

 p  DtoALL
T (Dn ) = (3.13)
1 − p(rmod( p1 )) DtoALLi

Where:

• p : Percentage of number of cluster heads in the network,

• r : Round number,

• DtoALLi : Distances to all other nodes of a node,

• DtoALL: Average distances to all other nodes,

Threshold value of distance to base station: (equation 13) is expressed in equation 3.14:

 p  DtoBS
lT (DBS ) = (3.14)
1 − p(rmod( p1 )) DtoBSi

Where:

• p : Percentage of number of cluster heads in the network,

• r : Round number,

• DtoBSi : Distances to the base station of a node,

• DtoBS: Average distances to the base station of all nodes,

5. Based on the cluster size: If the number of cluster members exceeds the optimal cluster
size, more cluster head(s) in the cluster will be selected. The loading of a cluster head will
also be considered. If there are many cluster members within a cluster, there will be many
data packet sent to a cluster head. The cluster head will be exhausted quickly. In this
case, more cluster heads will be selected for that cluster to balance the loading of a cluster
head. The intuitive number of clusters and the cluster size, i.e. the number of nodes in
a cluster, are defined in equation 3.15 and 3.16 respectively. In case the number of nodes
in a cluster exceeds the optimal cluster size, an extra cluster head will be assigned in that

59
 3.3 Multi-criteria Energy Efficient Routing Protocol

cluster. However, this situation rarely happens, because the cluster heads are elected based
on the centrality of all nodes. Thus, the extra cluster head is just in case if needed to share
the loading and the burden of the cluster head, and the election is also computed by the
base station. This occasional adding of cluster heads does not incur too much overheads
caused by cluster formation and update.

The optimal number of clusters in a network is defined in equation 3.15:

Axy
T (N C) = (3.15)
d2o π

Where:

• N C : Number of clusters,

• Axy : Area of network,

• d0 : Energy efficient transmission distance

The cluster size, CS, which is the number of sensor nodes, is defined in equation 3.16:

n
T (CS) = (3.16)
T (N C)

The pseudo code for the proposed cluster formation algorithm is given by:

INITIALIZE:
1 Initialize total number of sensor nodes, n
2 Initialize number of cluster heads, CH
3 Initialize the size of the network area
4 Indicate the location of the BS(x0,y0)
5 BS calculates the average energy of the current network with equation 3.17:

n
1X
E= Ei (3.17)
n i=1

.
6 IF a node with remaining energy higher than average THEN
7 the node has higher chance to be a cluster head and will put into the CH candidate list
8 ELSE
9 Not eligible for election
10 END IF

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 3.3 Multi-criteria Energy Efficient Routing Protocol

11 Measure the centrality by calculating the cumulative distances from the candidate members
and the cluster head with using the equation 3.18:

n
1 X
DtoALLi = dij (3.18)
n i=1,j=1

12 Measure the proximity to the data sink by calculating the average distance from the cluster
head to the data sink with equation 3.19:

n
1X
DtoBS = DtoBSi (3.19)
n i=1

13 The candidate with highest eligibility value have higher chance to be a cluster head.
14 Generate a random number between 0 and 1 as shown in equation 3.20:

r ∈ (0, 1) (3.20)

15 Calculate the Threshold value of Remaining Energy with equation 3.21:

 p E
i
T (E) = 1 (3.21)
1 − p(rmod( p )) E

16 Calculate the Threshold value of distance to all nodes with equation 3.22:

 p  DtoALL
T (Dn ) = 1 (3.22)
1 − p(rmod( p )) DtoALLi

the Threshold value of distance the base station with equation 3.23:

 p  DtoBS
T (DBS ) = 1 (3.23)
1 − p(rmod( p )) DtoBSi

17 IF (r < T(E) and r < T (DBS) and r < T (Dn )) THEN

18 Select as a cluster head
19 ELSE
20 Act as a cluster member
21 IF a node’s distance to the BS is within the energy efficient distance, d0 THEN
22 the node does NOT join any cluster
23 ELSE
24 The sensor node will join the cluster head with the shortest distance
25 END IF
26 Update the membership of the clusters
27 IF (the number of sensors within a cluster exceeds the maximum member size) THEN
28 Assign more cluster head(s) for the group based on the candidate list
29 END IF

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 3.3 Multi-criteria Energy Efficient Routing Protocol

3.3.2 Routing Algorithm Description

After the formation of the clusters, we then introduce our proposed routing algorithm. Since,
LEACH is using single hop transmission method, this is not suitable for large scale network when
the transmission distance is over the propagation range, and it causes data packet loss. Therefore,
we use the distance to BS of a node, the distance between cluster heads and the energy efficient
distance for transmission to decide the routing strategies. In our multi-hop transmissions, cluster
head mainly sends data to another cluster head as the next hop. But, if there is no available
cluster head within its energy efficient distance, then a normal node within the sending cluster
head’s energy efficient distance will be selected as a temporary routing node. This can reduce
the packet loss in case if there is no next receiving cluster head available. The flowchart of the
proposed routing strategies is depicted in Figure 3.8 and explained in the following:

1. Normal node sends data directly to BS: If a nodes location is close to the BS and
within the energy efficient distance, d0, the data can be sent directly to the BS with-
out joining any cluster. This can save the energy consumption by avoiding unnecessary
transmissions to cluster head, and then to the base station.

2. Cluster head sends data directly to the BS: Cluster head will send data to the BS if
the cluster head’s distance to the BS is within energy efficient distance. After cluster heads
collect data from its members, it will check with its distance to the BS. If the distance
is within the energy efficient distance. It will send data directly to the BS, and without
involving any other cluster head to save energy consumption for the whole sensor network.

3. Cluster heads multi-hop transmissions: If the distance of the cluster head to the
BS exceeds the energy efficient transmission distance. Then, it will transfer to the next
cluster head as an intermediate node. There may be many choices for the next available
cluster heads e.g. cluster head j and k, and it will select the receiving cluster head which
is within its energy efficient distance, d0, and is also the closest to the BS by calculating
its Euclidean distance to the base station.

For example, if DtoBSj <= DtoBSk , then the cluster head will send data to next cluster
head j.
q
2 2
DtoBSj = (xj − x0 ) + (yj − y0 ) (3.24)

q
2 2
DtoBSk = (xk − x0 ) + (yk − y0 ) (3.25)

This can reduce the number of transmissions and number of cluster heads involved for the
whole transmission process. It will then forward to the next cluster head with the same
logic until to the BS. After the cluster head received the data packet, it will further process

62
 3.3 Multi-criteria Energy Efficient Routing Protocol

Figure 3.8: Flow chart of routing protocol.

the packet and transmit to the next cluster head based on the approach mentioned before,
and finally to the BS.

4. Next cluster head is not within energy efficient distance: If there is no cluster
head within the energy efficient distance as shown in Figure 3.9, it will find out the nearest

63
 3.3 Multi-criteria Energy Efficient Routing Protocol

Figure 3.9: Selection of temporary routing node

receiving cluster head. Then, it will select a temporary routing node which contains the
highest remaining energy and within the same energy efficient coverage area with distance,
d0 given by: s
Efs
d0 = (3.26)
Eamp

between the two cluster heads. This approach is to avoid data packet loss by selecting
a temporary routing node in between two cluster heads, and lower the burden of the
temporary routing node by selecting the nearest receiving cluster head. If the distance
between two cluster heads are longer than 2d0, it will follow the same approach and divide
the distance with according to the energy efficient transmission distance and select the
suitable temporary routing nodes in between, until reaching the next closest cluster head.
The details of the operations of the algorithms are illustrated in the pseudo code below.

The pseudo code for the proposed routing algorithm is given by:

1 IF the distance to the BS is shorter than energy efficient distance given by:
s
Efs
d0 = (3.27)
Eamp

2 Data will be sent to BS directly

3 ELSE
4 Node sends data to its cluster head
5 WHILE data NOT reaching BS

64
 3.4 Security Considerations for Energy-efficient Routing

6 Extract the cluster heads information from the list

7 Select the next cluster head which is closest to the base station by calculating its Euclidean
distance to the base station given by:
q
DtoBSj = (xj − x0 )2 + (yj − y0 )2 (3.28)

and within the energy efficient transmission range, d0

8 IF there is no next cluster head within the energy efficient distance d0,
9 Find out the next nearest receiving cluster head
10 A temporary routing node with the highest remaining energy and within the same energy-
efficient coverage area of the two cluster heads will be selected a temporary routing node is in the
same energy-efficient coverage area if equation 3.29 is < d0,
q
DSij = (xj − xi )2 + (yj − yi )2 (3.29)

and equation 3.30 is < d0,

q
DSik = (xk − xi )2 + (yk − yi )2 (3.30)

where j, k is the cluster heads, and i is the temporary routing node.

11 END IF
12 END IF
13 END WHILE // while loop

3.4 Security Considerations for Energy-efficient Routing

Security of WSNs is a major concern in the applications of smart grid. The communication in-
frastructure between energy generation, transmission, and distribution and consumption requires
end-to-end reliable and secure communications. Moreover, the system security should be robust
enough to prevent attacks and provide system stability and reliability with advanced controls.
Secure information storage and transportation systems are extremely vital for power utilities,
especially for billing purposes and grid control [200] because personal, financial and infrastruc-
ture information are involved. The limited processing capabilities of wireless sensor networks
make them more vulnerable to attacks. The precaution measures against attacks should be tailor
made to address the security problems and ensure confidentiality, data readiness and integrity
for the data communication in the smart grid.

There are several security problems and challenges in smart grid :

• Identity Spoofing: An attacker pretends a legitimate thing to communicate in the net-

work with a false identity in an unauthorized way. An attacker can spoof the identity of

65
 3.4 Security Considerations for Energy-efficient Routing

someone’s smart Meter to make payment for its energy consumption.

• Eavesdropping: An attacker secretly or stealthily listening to the private communications

of others devices in the smart grid network without their consent in order to gather infor-
mation. An attacker may access the secret data exchanged by other devices, and easily
know the energy consumption of households.

• Data tampering: An attacker can modify exchanged data, such as lowering the price of
electricity during peak periods to affect the demand of electricity. As a consequence, this
could make households increasing their consumption during the peak period, and thus
overload the electrical power network.

• Privacy: There are some personal data which is private to an entity, and is inherently
special or sensitive to them. An attacker may extract customers’ secret information through
smart meters and smart appliances in residential houses. They may disclose the daily living
habits of a customer to others without permission.

• Availability and Denial of Service: In the traditional power grid, it is difficult to target
and control the availability of electricity meters and substations, especially in a large scale
network. In smart grid, Information Communication Technology is be integrated in the
vital assets of the power grid, thus making it easier for attackers to target and attack them
in order to hinder its normal operation.

Shah, Manali D., et al. [201] proposed a lightweight authentication protocol to authenticate
packets in a broadcast network based on symmetric cryptography primitives. The key for au-
thenticating a packet is only disclosed to the receiver in the subsequent packet. This method
requires the entire network to be synchronized which is complicated to process, and incur time
delay because the receiver has to wait for the next packet in order to obtain the key. Besides,
flooding is required, it may suffer from denial of service attacks. K. Grover and A. Lim discussed
broadcast authentication schemes for wireless networks, and a one-time signature schemes [202]
can be used. However, key sizes are large and the number of supported users per key is lim-
ited. Recently, hash chain and hash tree are introduced for broadcast authentication [203; 204].
However, there are some problems associated with these approaches. All the packets have to be
available before the broadcast process for the construction of the hash tree because it requires
the information of all related packets for processing, and it limits the scope of applications.
Besides, additional transmission overhead is incurred as part of the hash tree is included in each
packet. The encryption-based approach preserves the privacy of consumer records during trans-
mission between different entities, such as the power station and electrical devices of end users.
Khurana et al. [16] discussed some security issues in smart grid related to encryption and key
management in terms of complexity and scalability. Security technologies and issues for smart

66
 3.4 Security Considerations for Energy-efficient Routing

grid networks were investigated by Metke and Ekl [205; 206], with a focus on public key infras-
tructure (PKI) and trusted computing. Homomorphic encryption in smart grid was introduced
in [207] as a secure data aggregation protocol. However, a flexible Homomorphic encryption is
required to improve smart grid computations capabilities. He et al. [208] proposed an efficient
cryptosystem for smart grid that combines PKI and homomorphic encryption techniques. To
conclude, a secure and convenient key management scheme is vital for the success of using data
encryption in smart grid.
Further to the cluster formation and routing algorithm described in section 3.3, we then
introduce our proposed data encryption and decryption mechanism in data transmission for
security concern. It can be implemented together with our energy efficient routing algorithm to
minimize the number of times of data encryption in order to reduce the overhead and energy
consumption.

3.4.1 WSNs Security Requirements

Wang [209] advocates that availability, integrity, and confidentiality are the three main principles
in all security programs. Besides, authentication and authorization mechanisms are also used to
ensure that data packets are sent between the authorized parties.
Security is a major concern in the data transmissions through WSNs. A secured WSN needs
to fulfill the following requirements:

• Data Confidentiality: Preventing sensitive information accessed by unauthorized par-

ties.

• Data Authentication: Receiving node needs to make sure that the data is originated
from the correct source.

• Data Anonymity: The identity of the sending node should not be identified from the
data.

• Data Integrity: Data must not be altered accidentally or deliberately during transmis-
sions between sensor nodes.

• Data Freshness: Data must be recent, and no old messages replayed by an attacker.

3.4.2 Data Encryption Methods

There are two major encryption methods: Symmetric and Asymmetric key encryption.

• Symmetric Key cryptography uses one private key for encryption and decryption.

67
 3.4 Security Considerations for Energy-efficient Routing

• On the contrary, Asymmetric Key cryptography uses a pair of public and private keys
to encrypt and decrypt messages. Each node will keep a secret private key itself, and its
public key will be known by the authorized receiver.

Both Symmetric and Asymmetric cryptographic techniques offer advantages and disadvan-
tages. Symmetric encryption provides cost-effective and efficient methods of data encryption,
but its encryption method is less complicated. Also, its security level is lower because of using
identical symmetric keys for data encryptions between the communication parties. Asymmetric
algorithms can ensure higher security based on the more complicated encryption logic and the
secrecy of the private key, but it will consume more power.

3.4.3 Encryption Key Establishment and Management

As discussed, the WSNs should be implemented with a certain security level, and at the same
time maintain low energy consumption and high efficiency. If only one encryption key is shared
among all nodes in the network, it requires no establishment of additional keys, and the storage
costs and energy consumption can also be minimized. However, the security standard will
be extremely poor because if any node in the network is captured by an attacker, the whole
network will be compromised. The objective in this Thesis is to discuss the difference between
the asymmetric and symmetric key data encryption methods. And, we proposed an efficient
asymmetric key management and implementation mechanisms for multi-hop transmissions in
wireless sensor network. We discussed how the encryption works in different stages and when
there is a change of the cluster formation. Its aim is to reduce the overheads in the communication
of the encryption key and maintain the high security level at the same time. It can work with
any encryption algorithm, and the encryption algorithm itself is not the focus in our Thesis.
Therefore we propose public and private key pairs with minimum interactions between sensors
as explained in the following.

3.4.3.1 System Initialization Stage

Every sensor node is equipped with a GPS module to collect its own location information,
and it can also calculate its own remaining energy. The BS broadcasts a connection invitation
message to all sensor nodes in its receiving range. It is assumed in our simulation that the base
station can broadcast messages to all sensor nodes in the area. In reality, if the area is large,
multiple base stations and relays may be deployed to extend the network. If the sensor node
wants to join the BS, it will generate a one-time session key by a secret random value, and send
a connection request message to the BS together with its session key, location and remaining
energy information. After the BS receives the connection request message, it will generate a
new ID, a pair of private/public keys based on a secret random value and the nodes location

68
 3.4 Security Considerations for Energy-efficient Routing

information. It will then create a new member record in its database and send the information
listed below for encryption by the session key to the connection requested node.

• ID of the connection requested node,

• Private key of the connection request node,

• The ID and the public key of the proposed joining CH,

• Public key of the BS,

The private key is only known by the individual sender node, and is used for individual
authentication and secure communication assurance. On the contrary, public keys will be se-
lectively sent to authorized receivers. The sensor node needs to reply with a connection finish
message encrypted together with the session key and the BSs public key to the BS. After the BS
received the connection finish message, it will decrypted it with its private key, and check the
message, the node is then eligible to send data in the WSN. The BS will then delete the session
key and update the status of the node in the database. If no acknowledgement is received within
a specific period of time, the connection is declined, and a new connection procedure is required.
If the sensor node is selected as a cluster head, the BS will inform the cluster head with the
following information encrypted with the session key:

• ID and private key of the cluster head,

• IDs and public keys of the nodes joining its cluster,

• IDs and public keys of other cluster heads,

• Number of next hops count to the BS,

3.4.3.2 Nodes Sending Message

After the cluster formation, cluster heads broadcast TDMA schedule to their own cluster mem-
bers. Every node can send message to its cluster head according to the timeslot allocated in
the TDMA schedule. For the rest of the time, the node will sleep to save energy. When a node
wants to send a message in its assigned timeslot, it will encrypt the message with its private key
together with its ID, and then encrypt it with the public key of its cluster head.

P ublicCH {ID, P ublicBS {P rivatesender (msg)}}

After the cluster head received the message, it will decrypt the message with its own private
key, and check the ID of the sender. If the ID matches with the member list provided by the
BS, it will then accumulate the messages from other members in its cluster during the round.
Otherwise, it will just drop the message. After the time is over, it will compress the whole

69
 3.4 Security Considerations for Energy-efficient Routing

message to save the transmission cost, encrypt the whole message with its private key, and
transmit it to the next cluster head based on our proposed routing algorithm.

P ublicnext CH{ID, P rivateCH (accumulated msg)}

After the targeted next cluster head receives the message, it will then decrypt the message
with its own private key, and check the ID of the previous cluster headsender. If it matches the
cluster heads list provided by the BS, it will then repeat the process and send the message to
the next cluster head(s). If the ID of the sender does not match the list, it will just decline the
transmission, delete the message and inform the BS. Otherwise the last cluster head will encrypt
the message with the public key of the cluster head, and finally send the message to the BS.
Lastly, the BS will receive the message and decrypt the message with its private key and then
decrypt the message with the public key of the last sender, and check if the ID matches with
its database. If matched, then it will decrypt the message with the public key of the original
sender one by one to get the data. Otherwise, the whole packet will be dropped. So, with our
proposed security mechanism, it can prevent sending data from unrecognised nodes (attackers)
because the message will be rejected and deleted if the sender is not in the list and without
the signature (encryption with senders private key), and no further processing is required in
order to save the energy consumption. Moreover, it can ensure confidentiality because only the
authorized receiver can extract the original message with its private key and nobody else can
modify the original without the decryption key. Thus, the message is sent securely. Therefore,
our proposed algorithm can achieve data confidentiality, authentication, and integrity of WSNs.

3.4.3.3 New Node joining

When a new node joins, it needs to wait for the next connection invitation message broadcast
from the BS, and repeat the procedure outlined before.

3.4.3.4 Leaving of a Normal Node

If a node wants to leave the sensor network, it needs to send a connection terminate message
encrypted with its private key and then with the BS public key to the BS. After the message
is received, the BS will verify it, delete the node in the database and inform the corresponding
cluster head. A BS will regularly broadcast a check status message to all nodes. The sensor
nodes need to reply to the message with the status of current remaining energy to the BS, if a
node cannot response within the time period for a certain number of times, then that node will
be treated as dead. In that case, the BS will delete the node and its keys in the database and
inform the corresponding cluster head as well.

70
 3.4 Security Considerations for Energy-efficient Routing

3.4.3.5 Change of Cluster Head

The BS will monitor the remaining energy status of each cluster head. If the energy is less than
the required level, the BS will re-select the cluster head based on our proposed cluster head
selection algorithm, update its database, and inform the new cluster head and cluster members
for the change. It will also announce the change to other cluster heads. Hence the keys involved
among the parties will be re-distributed.
To conclude, our proposed efficient asymmetric key management and implementation mech-
anisms in wireless sensor network can work with our multi-hop hierarchical routing algorithm
introduced in section 3.3. It can reduce the number of data transmissions, and thus reduce the
overheads incurred by the encryption mechanism. Moreover, the proposed key distribution and
implementation methods with node’s ID element. The information is just delivered to the tar-
geted node and related cluster head(s). It prevents the keys or messages being sent to all nodes
in the whole network. Therefore, it can reduce the network traffic, prevent collision, and avoid
denial of services attacks. Our asymmetric key encryption mechanism is proposed to achieve
data confidentiality, authentication, and integrity of communications in WSNs.

71
Chapter 4

Performance Evaluation of
Energy Efficiency Routing
Protocols

Further to the literature review of the hierarchical routing algorithms, and the analysis of the
various criteria and parameters affecting the energy efficiency of the clustering and routing
algorithms. In this chapter, we deeply analyze the significance of each criteria, and evaluate the
effects of combination of them on the performance of the WSN.

4.1 Omnet ++ Simulator

In our simulations, we use Omnet++ [210; 211] and Castalia [78] model as the wireless network
platform to simulate the wireless network environment and implement various cluster head selec-
tion methods and routing algorithms for comparisons and analyze their performance regarding
energy efficiency. Omnet++ is distributed under the Academic Public License for research pur-
poses. It is a well-known and popular object-oriented modular discrete event system simulation
framework. It is an extensible, modular, component-based C++ simulation library and frame-
work, primarily for building network simulators. The modular structure of Omnet++ can enhance
the reusability for project developments. It consists of modules communicating with message
passing. It allows modules communicate with messages that may contain arbitrary data, in
addition to usual attributes such as a time stamp. Modules can send messages either directly
to the destination or along a predefined path, through gates and connections [210; 211]. Simple
modules can be grouped into compound modules and formed a hierarchy structure. The funda-
mental of this infrastructure is a component architecture for simulation models. Various models

72
 4.1 Omnet++ Simulator

and frameworks can be adopted for supporting sensor networks, wireless ad-hoc networks [212],
Internet protocols [213], performance modeling [214], photonic networks [215], etc.

4.1.1 Castalia Framework

Castalia is a model simulating WSN, Body Area Networks (BAN) and generally networks of
low-power embedded devices, developed by Thanassis Boulis in the Networked Systems theme
at NICTA from 2007 [216]. It can be used to test the distributed algorithms and/or protocols
in realistic wireless channel and radio models, with a realistic node behaviour. Castalia also
includes the features such as model for temporal variation of path loss, fine-grain interference
and RSSI calculation, physical process modeling, node clock drift, and several popular MAC
protocols implemented. Castalia provides tools to help run large parametric simulation studies,
process and visualize the results. Castalia supports ZigbeeMAC or IEEE 802.15.4 MAC.
The nodes connect to each other through wireless channel module(s). Messages pass from one
module to another. Wireless channel decides which nodes should receive the packet. Wireless
channel sends messages to the radio that carry information about the signal, such as: modulation,
bandwidth, carrier frequency, and most importantly, the strength of the signal in dBm. Castalia
defines wireless channel with various models such as average path loss modeling, nodes mobility,
temporal variation modeling. The nodes are also linked through the physical processes that
they monitor. Figure 4.1 show the modular scheme of the WSN implemented by Castalia in
Omnet++. The sensor node is a combination the modules that simulate different attributes of
the real sensor node using different parameters. The simulation parameters are described in
details in the following sections.

Figure 4.1: Framework of castalia model implemented in Omnet++.

73
 4.2 Simulation Parameters and Scenarios

4.2 Simulation Parameters and Scenarios

Figure 4.2: Snapshot of Simulation Environment in Omnet++.

The objective of this section is to provide in-deep overview of the performance of different
routing protocols under several general scenarios. Rather than applying the proposed clustering
and routing algorithms to a specified application, we designed a flexible, smart and intelligent
algorithm which can suit for different scenarios, environments and applications. Thus, In our
simulations, we concentrate on the analysis of the performance of the following criteria:

• the remaining energy in each sensor node,

• the average energy of all nodes ,

• the distance of each sensor node to base station,

• the distance of each sensor node to all other nodes to measure the density of nodes, and

• If the next receiving cluster head is outside the sensing range, a temporary routing node
is selected on demand from the normal nodes in between the two cluster heads in order to
facilitate the data transmission in this occasion. The selection criteria is defined in section
3.3.2.

Table 4.1 describes the parameters used in the simulation scenarios. It includes the network
environment, application layer, routing layer, wireless channel, radio energy model, transmission
and receiving modes. NetBufferSize specifies the size of the buffer found in the routing mo-
dule. If the buffer is full, it will automatically create and send control message to the application.
A time slot is defined as the time duration of a Time Division Multiple Access (TDMA) slot
assigned to each sensor node for sending data to its cluster head.

74
 4.2 Simulation Parameters and Scenarios

Table 4.1: Simulation parameters at different levels.

Environment Value
Number of nodes 50
Location of base station Center
Application Layer Value
Packet rate 1 packet/s
Constant data payload 100 bytes
Routing Layer Value
NetBufferSize 1000 packets
Sink Node 0
SlotLength 0.2 s
RoundLength 20 s
Percentage of cluster heads 0.05
Transmission power used 1 dBm
Wireless Channel Value
Communication standard 802.15.4
Propagation distance 100 meters (maximum)
Nodes mobility static
Signal sigma 0
Bi-directional signal sigma 0
Path loss exponent 2
Path loss at a reference distance P L(d0 ) 55dB
Radio Energy Model Value
Initial Energy of Nodes 50 J
Eelec 50 nJ/bit
Eamp 0.0013 pJ/bit/m4
Ef s 10 pJ/bit/m2
d0 (energy efficient distance) 87 m
Receiving Modes Value
Data rate 250 kbps
Modulation type PSK
Bits per symbol 4
Bandwidth 20 MHz
Noise bandwidth 194 MHz
Noise floor -100 dBm
Sensitivity -95 dBm
Power consumed 62 mW
Transmission Levels Value
Tx 0 dBm

Table 4.2: Parameters used in delay transition matrix (state switching times in milliseconds).

From Receive From Transmission From Sleep

To Receive - 0.01 ms 0.194 ms
To Transmission 0.01 ms - 0.194 ms
To Sleep 0.05 ms 0.05 ms -

75
 4.3 Baseline Performance Analysis of LEACH

Table 4.3: Parameters used in transition matrix (power consumption between different states).

From Receive From Transmission From Sleep

To Receive - 62 mW 62 mW
To Transmission 62 mW - 62 mW
To Sleep 1.4 mW 1.4 mW -

Castalia is used in our simulation, where it uses the lognormal-shadowing model [217] to calcu-
late the path loss between two nodes. The path loss at distance d between two nodes, PL(d) is
defined as below.  
d
PL(d) = PL(d0 ) + 10η log + Xσ (4.1)
do

where PL(do ) is the reference path loss at distance d0 , η is the path loss exponent and Xσ
is the lognormal-shadowing channel gain modeled as a zero-mean Gaussian random variable
with standard deviation. We outline the default values for these parameters in Castalia as per
specification of CC2420. And this CC2420 model is commonly used in IoT applications such as
health care [218] , Agriculture and Farming [219], smart grid [220; 221]. Some of the applications
of WSN under 5G like electronic healthcare, vehicle-to-vehicle communication system require
data rates higher than 10 Mbps within a limited bandwidth of 20 MHz over a coverage range
larger than 100 m [222], Tavares, Rodrigo C., et al. [223] proposed that an increase in available
bandwidth from 2 to 20 MHz reduces the schedule length in binary trees. In our simulations,
we use the CC2420 radio module with 20 MHz bandwidth for analysis. Besides, it is assumed
that the setting of the sigma for both signal to zero. That means, all nodes at a certain distance
from a transmitter get the exact same signal strength and all links are perfectly bidirectional
(i.e the quality of node A to node B is the same as the quality of node B to node A). Table 4.2
illustrates the time delay transition switching from receive, transmission and sleep mode. While
Table 4.3 describes the power consumption switching from receive, transmission, and sleep mode
respectively.

4.3 Baseline Performance Analysis of LEACH

Firstly, we use LEACH as the baseline routing protocol, and one factor is varied at a time in
order to investigate the significance of the effects. The three main factors that are used to
evaluate the energy efficiency and transmission effectiveness:

• Number of alive nodes versus time: to realize the number of nodes which are still function-
ing after certain period of time. A node is defined as alive or functioning if its remaining
energy is greater than 0J (if Ei > 0).

• Total remaining energy versus time: Notice that the energy model defined in the resource
manager of Castalia keeps track of the energy spent by the nodes. The residual energy of

76
 4.4 Performance Analysis in Different Scales of Networks

all nodes are summed up in equation 4.2:

n
X
E= Ei (4.2)
i=1

At different intervals of time to investigate the energy consumption of the whole network.

• Successful delivery rate versus time: When both sending and receiving nodes are within the
distance of the coverage area and have enough energy to complete the transmission, it is
defined as a successful transaction. On the contrary, there is a packet loss. The successful
delivery rate is calculated using equation 4.3:
P
SD
SR = P (4.3)
TD

Where:

– SR : Successful Rate,

– SD : Successful Delivery,

– T D : Total Delivery,

As the multiple criteria routing algorithm introduced in Chapter 3.3, we would like to inves-
tigate and analyze the significance of each of the following criteria affecting the performance of
the wireless sensor network. The selection methods of cluster head that proposed are based on:

• remaining energy of a node,

• density of nodes,

• both remaining energy and density of nodes,

• multi-hop transmissions,

• temporary routing rode.

4.4 Performance Analysis in Different Scales of Networks

Next, we further investigate the methods of selecting next cluster head. It can be selected based
on the shortest distance, or with our proposed method by selecting the farthest one within the
energy efficient distance and also towards the direction of the sink as the next cluster head for
routing. Here, we would like to analyze these effects under different scales of networks. In
our simulations, IEEE 802.15.4 standard is used, it defines the operation of low-rate wireless

77
 4.4 Performance Analysis in Different Scales of Networks

area networks with maximum coverage area 100 meters. Based on the energy-efficient distance
equation 4.4: s
Efs
d0 = (4.4)
damp

defined in the first order radio model [182; 183; 184; 185; 186]. The base station is located in
the center of the network region. Therefore, we categorize the whole network into three different
scales 300 m x 300 m (small), 400 m x 400 m (medium), and 500 m x 500 m (large). We analyze
three routing algorithms with i) single hop, ii) multiple hops with shortest distance to next hop,
and iii)multiple hops with energy efficient distance in three different scales of grid sizes

4.4.1 Scenario 1: Small Scale Network

As observed in Figure 4.3, the number of alive nodes remained similarly for the small scale of
network, the first node exhausted starting from 720s for all different algorithms, and there are
around 44 nodes still functioning at 980s. They perform more or less the same. In Figure 4.4,
the energy consumption of the identified routing criteria are also similar. Most of them dropped
to around total 346J at 980s, except the one with density criteria which performs slightly better
with total 352J at 980s. The results of different algorithms for small scale network performs
similar because the transmission distance is short, and multi-hop transmissions may not be
necessary. Therefore, most of the required transmissions are just single hop. Therefore, the
overall remaining energy used and number of alive nodes still functioning are similar. LEACH
with single hop transmission is also feasible in a small scale network. This is because the distance
between the base station and nodes is shorter in a small scale network, and direct transmission
to the base station is already good enough to save energy.

Figure 4.3: Comparisons of the number of alive nodes for 300 x 300 m network scenario.

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 4.4 Performance Analysis in Different Scales of Networks

Figure 4.4: Comparisons of the total remaining energy for 300 x 300 m network.

Figure 4.5: Comparisons on successful delivery rate for 300 x 300 m network.

However, the successful delivery rate of multi-hop transmission with shortest distance is
higher than others as shown in Figure 4.5. Most of them can maintain the successful delivery
rate at 0.65, and multi-hop transmission can maintain at a higher rate with 0.85. The mutli-hop
transmission is better than others because there may be some cases where the distance between
the cluster head and the base station is over the sensing range so that the packets are lost. On
the contrary, multi-hop transmission can minimize the distance gap to improve the successful
delivery rate.

4.4.2 Scenario 2: Medium Scale Network

For the performance of different algorithms in medium sized network as observed in Figure 4.6,
the first node exhausted at around 720s for all algorithms, and there are still around 30 nodes
working for most of them. However, just 28 nodes alive for original LEACH. Regarding the total

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 4.4 Performance Analysis in Different Scales of Networks

remaining energy, there are around total 153J at 980s for most of the algorithms as shown in
Figure 4.7. However, the one with just remaining energy criteria performs slightly poor with just
total 146J, and original LEACH is the worst with total 132J at 980s. In this case, it is obvious
that LEACH performs the worst, and it is because the distance between the base station and
nodes are longer, and more energy is consumed for data transmissions. Therefore, the nodes are
depleted more quickly early in the simulation compared to multiple hop transmissions. LEACH
performs worse than all other parameters in general because the cluster head of LEACH is
selected randomly, and the nodes may die quicker when lower remaining energy nodes are selected
as cluster heads.

Figure 4.6: Comparisons on number of alive nodes for 400 x 400 m network.

Figure 4.7: Comparisons on total remaining energy for 400 x 400 m network.

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 4.4 Performance Analysis in Different Scales of Networks

Figure 4.8: Comparisons on successful delivery rate for 400 x 400 m network.

The successful delivery rate is higher by using multi-hop transmission with around 0.75 in
average when comparing with single hop algorithms with just around 0.62 in average as shown
in Figure 4.8. It is because the transmission distance is longer in medium scale network, and
more failure transmissions are resulted for single hop transmission.

4.4.3 Scenario 3: Large Scale Network

When it comes to large scale networks, the first node exhausted at 720s for all algorithms, and
there are just 17 nodes remaining at 980s. And, the total remaining energy dropped to around
64J for all algorithms. This situation is very similar to the medium scale network.

Figure 4.9: Comparisons on number of alive nodes for 500 x 500 m network.

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 4.4 Performance Analysis in Different Scales of Networks

Figure 4.10: Comparisons on total remaining energy for 500 x 500 m network.

1
0.9
0.8
0.7
0.6
CDF (x)

0.5
0.4 Original LEACH
Remaining Energy
0.3 Density
Remaining Energy & Density
0.2
Multi-hop Shortest Distance
0.1
0
0 250 500 750 1000 1250 1500 1750 2000 2250 2500
x = Energy [J]

Figure 4.11: CDF on total remaining energy for 500 x 500 m network.

For the successful delivery rate, LEACH performs the worst with just 0.56 among all single
hop transmission algorithms in the large scale network as the select cluster heads randomly.
The situation can be further improved by using remaining energy and density for selection.
It can be observed in Figure 4.12 when comparing the successful transmission rate that using
remaining energy and density for consideration as a cluster head can perform better in single
hop transmission. Moreover, using multi-hop transmission algorithm in large scale network is
necessary with the highest successful delivery rate 0.67.

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 4.4 Performance Analysis in Different Scales of Networks

Figure 4.12: Comparisons on successful delivery rate for 500 x 500 m network.

In Figure 4.13, the results show that multi-hop transmission algorithm is dominant with
higher successful delivery rate among all different algorithms. Single hop transmission is not
suitable especially for large scale network because of long distance transmission by individual
node. It is because the transmission distance is much more longer this time, and it should rely
on the multi-hop transmission method in order to shorten the transmission distance done by
individual node. Otherwise, there will be more failure transmissions.

0.9 Original LEACH

Remaining Energy
Density
0.8 Remaining Energy & Density
Multi-hop Shortest Distance
0.7

0.6
CDF (x)

0.5

0.4

0.3

0.2

0.1

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x = Successful Delivery Rate

Figure 4.13: CDF on successful delivery rate for 500 x 500 m network.

From Figure 4.14 to Figure 4.18, the PDF on successful delivery rate of individual algorithm
are presented on different charts, and there is a single chart to combine all the PDF results for
comparison in Figure 4.19. From the figure of Probability density function (PDF) on successful

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 4.4 Performance Analysis in Different Scales of Networks

delivery rate of different LEACH parameter in Figure 4.19 and the statistical data are summa-
rized in Table 4.4 , we can discover that the multi-hop shortest distance algorithm outperforms
others with higher successful delivery rate 0.64. While other single hop transmission algorithms
work similarly with successful delivery rate 0.55. And, Original LEACH version which selects the
cluster head randomly is the worst. This can conclude that multi-hop transmission is necessary
for large scale network as it can reduce the probability of failure transmissions due to the long
transmission distance.

Figure 4.14: PDF on successful delivery rate of Original LEACH for 500 x 500 m network.

Figure 4.15: PDF on successful delivery rate of Remaining Energy for 500 x 500 m network.

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 4.4 Performance Analysis in Different Scales of Networks

Figure 4.16: PDF on successful delivery rate of Density for 500 x 500 m network.

Figure 4.17: PDF on successful delivery rate of Remaining Energy and Density for 500 x 500 m
network.

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 4.4 Performance Analysis in Different Scales of Networks

Figure 4.18: PDF on successful delivery rate of Multi-hop Shortest Distance for 500 x 500 m
network.

5
Original LEACH
Remaining Energy
Density
4 Remaining Energy & Density
Multi-hop Shortest Distance

3
PDF(x)

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x = Successful Delivery Rate

Figure 4.19: PDF on successful delivery rate of Different LEACH parameters for 500 x 500 m
network.

Table 4.4: Mean, standard deviation and confidence interval of successful delivery rate compar-
ison with different criteria of selecting cluster head with significance level = 0.05

Mean Standard deviation Confidence interval

Original LEACH 0.5522 0.1063 0.5375 - 0.5670
Remaining Energy 0.5568 0.1108 0.5414 - 0.5721
Density 0.5547 0.1083 0.5397 - 0.5697
Remaining Energy & Density 0.5561 0.1136 0.5403 - 0.5718
Multi-hop Shortest Distance 0.6399 0.1207 0.6232 - 0.6567

To further analyze the behaviour among various parameters of the single hop transmission. It
is found that original LEACH performs the worst with the lowest successful transmission rate, it
is because the cluster head is selected randomly. Nodes with lower remaining energy or far away

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 4.4 Performance Analysis in Different Scales of Networks

the centrality of nodes may be selected, and it therefore induces failure transmissions. It is also
discovered that just considering either remaining energy or density of nodes in selecting a cluster
head may not be enough. They also perform even worse than considering both parameters.
It can be concluded that multi-hop transmissions is necessary for large scale network with
long transmission distance. It is because the successful delivery rate will be greatly reduced when
using single hop transmissions due to the failure transmissions will be occurred frequently under
the long distance transmission. Therefore, in the next section we compare different hierarchical
routing algorithm and propose an energy efficient multi-hop routing algorithm to save energy
and also maintain high successful transmission rate at the same time.

4.4.4 Cluster Head Selection Analysis

According to the different criteria of selecting cluster head we discussed before, it is interesting
to know how different criteria such as randomness, remaining energy, density, distance to next
hop may affect the performance of the whole wireless sensor network.

4.4.4.1 Random Cluster Head Selection

Based on the results, we can observe that when a cluster head is selected randomly such as
LEACH, a node with lower remaining energy may be selected as cluster head. In this case, the
cluster head does not have enough energy to process data from its members and hence induces
data packets loss. Besides, it may exhaust quickly, and cannot function any more. Therefore,
the overall performance of energy efficiency and successful delivery rate is the poorest among
all.

4.4.4.2 Cluster Head Selection By Remaining Energy

Instead of randomly selecting a cluster head, we further use the remaining energy of nodes to
select a cluster head. A node’s remaining energy is compared with the average energy of all
nodes. If a node with higher remaining energy expressed in equation 4.5:

 p E
i
T (E) = (4.5)
1 − p(rmod( p1 )) E

It will have a higher chance to become a cluster head. In this case, we find that the performance
is better than the cluster head selected randomly. It is because it can avoid a node with lower
remaining energy level being selected as a cluster head, and prevent data packet loss. It can also
balance the energy consumption of the whole sensor network. The node with higher remaining
energy has higher chance to become a cluster head, and prevent the nodes with lower remaining
energy from exhausting faster.

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 4.4 Performance Analysis in Different Scales of Networks

4.4.4.3 Cluster Head Selection by the Centrality of Nodes

However, just using the remaining energy for selecting a cluster head may not be sufficient
because higher remaining energy nodes may not be evenly located in the whole network area.
For some situations, nodes cannot find the higher remaining energy cluster head nearby or within
the propagation range. Then, data packets will be lost. Therefore, we further use the centrality
of nodes as a criteria to select a cluster head. We calculate the total distances to other nodes of
a node with equation 4.6:
n
1 X
DtoALLi = dij (4.6)
n i=1,j=1

If a node with shorter total distances to other nodes comparing with the average, it will have
a higher chance to be a cluster head. In this case, we can improve the uneven distribution of
the cluster heads which are selected by remaining energy solely. We combine the criteria of
remaining energy and density of nodes for selecting a cluster head, and we can find that the
successful delivery rate is higher than either just by one criteria with remaining energy or density
alone.

4.4.4.4 Sending to Base Station by Multi-hop

For data communications in large area network, it can be observed in our simulation results in
Table 4.4 with network size 500 x 500 m, the successful delivery rate of single hop transmission
is just around 0.55, which is much lower than that of multi-hop transmissions with around 0.64.
Besides, in Figure 4.13, the CDF shows that multi-hop transmissions ensure a higher successful
delivery rate with every same CDF value when comparing with single hop transmissions. Single
hop transmission is not adequate because the distance between the cluster head and the base
station may be long such that the distance is not within the propagation area, and the data
packets are lost. Moreover, the energy consumption is huge for long distance transmission. The
cluster heads exhaust rapidly, and the whole network malfunctions. The successful transmission
rate is relatively low. Therefore, single hop transmission is not suitable for large scale network.
Furthermore, we improve the situation by multi-hops transmissions. The cluster head transmit
the data packet to the next cluster head which is within the sending cluster head’s energy-
efficient distance, d0 , and the receiving cluster head is also closest to the base station. It can
greatly enhance the success delivery rate by shortening the transmission distance and balancing
the loading among different cluster heads.

4.4.4.5 Sending to Base Station Directly Without Joining Cluster

If a node is very close to the base station, and the distance to base station is shorter than the
distance to the cluster head. There is no point for the node to form a cluster with any cluster
head. It is because it places a heavy burden to its cluster head, and duplicates the overall energy

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 4.5 Performance Comparison of Different Hierarchical Algorithms

consumption in transmissions. Besides, it is not necessary to transmit the data packet to a

farther away cluster head than the base station which also causes delays. It is observed that
the energy efficiency can be enhanced by direct sending to base station under the circumstances
when a node’s distance to base station is shorter than to other cluster heads.

4.4.4.6 Sending to Next Cluster Head Through Temporary Routing Node

However, there are some situations that the multi-hop transmission is not possible when the
distance between cluster heads is over the propagation range. In this case, there will also be
failure transmissions happened. To further investigate the problem, we propose to assign a
temporary node in between. The routing node is selected from the normal nodes, and it should
be in the same coverage area of the two cluster heads. The same coverage area is defined as the
overlap propagation range of the two cluster heads. In case if there are more than one candidates,
the node in the same coverage area which is closer to the receiving cluster head, and with highest
remaining energy will be the temporary routing node. The temporary node just serves for this
particular transmissions and resumes to act as a normal node. For next transmission, another
temporary routing node may be chosen according to that particular situation. Based on the
results, it is found that it helps to overcome the shortage of basic multi-hop communications
when the transmissions distance between two cluster heads is over the transmission range.
From Figure 4.19, we can observe that the successful delivery rate is higher when considering
energy and density in selecting cluster heads. However, single hop transmission is not suitable
for longer distance transmissions. Therefore, multi-hop transmissions are required to shorten
the transmission distance and sharing the burden of all nodes in the whole sensor network and
improve the energy efficiency as well. The successful delivery rate is also further improved from
our design by adding temporary routing nodes between cluster heads for multi-hop transmissions.
In the next section, we focus on the large scale network and discuss how it can further improve
the network performance with our proposed multi-hop routing algorithm by selecting the most
suitable cluster head and next hop for data transmissions.

4.5 Performance Comparison of Different Hierarchical Al-

gorithms
In this section, we have compared our proposed routing technique with other similar hierarchical
clustering and routing algorithms: CCS, EADAT, ETR, and PANEL. The following are the main
observations: For the number of alive nodes as shown in Figure 4.20, the first node exhausted
at 720s for all hierarchical algorithms. The performance of EADAT, ETR, and our proposed
algorithm perform similarly, where there are still around 18 nodes remaining at 980s. For Panel,

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 4.5 Performance Comparison of Different Hierarchical Algorithms

there are just around 5 nodes working after 980s. CCS performs the worst in this case with all
nodes exhausted at 900s.

Figure 4.20: Comparison on number of alive nodes of different hierarchical routing algorithms
in 500 x 500 m.

Regarding the total remaining energy as shown in Figure 4.21, CCS peforms the worst as all
nodes exhausted at 900s, and there are still total 68J remaining at 980s for EADAT, ETR, and
our proposed routing algorithm.

Figure 4.21: Comparison on total remaining energy of different hierarchical routing algorithms
in 500 x 500 m.

The respective CDF on total remaining energy of all hierarchical algorithms are also presented
in Figure 4.22. It shows that CCS and PANEL have a higher probability of less total remaining
energy among all other hierarchical routing algorithms.

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 4.5 Performance Comparison of Different Hierarchical Algorithms

Figure 4.22: CDF on total remaining energy of different hierarchical routing algorithms in 500
x 500 m.

For the comparison on the successful delivery rate among all different hierarchical routing
algorithms in Figure 4.23, EADAT, ETR, Multi-hop within d0 performs similarly with around
0.66 successful delivery rate in average. For PANEL, it has the highest successful delivery rate
in some situations with around 0.85. However, the results are more fluctuated that PANEL with
successful delivery rate in the range between 0.6 and 0.82. Moreover, it then drops significantly
starting from around 750s because many nodes start to be exhausted, and most of the sensor
nodes can’t function anymore and induce high data packet loss. As shown in the results, our pro-
posed routing algorithm with temporary routing node works steadily and keep higher probability
with successful delivery rate with around 0.7 in average. Although the successful delivery rate
of our algorithm is sometimes slightly lower than PANEL in the beginning, it performs better
after 750s. Moreover, the successful delivery rate of our proposed can be maintained steadily
across the whole network lifetime. Therefore, our proposed algorithm performs the best of all
hierarchical algorithms.

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 4.5 Performance Comparison of Different Hierarchical Algorithms

0.9

0.8

0.7
Successful Delivery Rate

0.6

0.5

CCS
0.4
EADAT
ETR
0.3 PANEL
Multi-hop within d0
Temp Routing Node
0.2

0.1

0
0 100 200 300 400 500 600 700 800 900 1000
Time [s]

Figure 4.23: Comparisons on successful delivery rate of different hierarchical routing algorithms
in 500 x 500 m.

For the CDF on successful delivery rate of all hierarchical algorithms in Figure 4.24, it shows
that the CCS performs the worst because it gets higher probability with around 0.2 for the lower
successful delivery rate between 0.1 to 0.6. It is relatively higher than other hierarchical routing
algorithms. On the contrary, our proposed algorithm can maintain at a higher probability with
successful delivery rate between 0.7 to 0.8.

1
CCS
0.9 EADAT
ETR
0.8
PANEL
Multi-hop within d0
0.7
Temporary Routing Node (Proposed)
0.6
CDF (x)

0.5

0.4

0.3

0.2

0.1

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x = Successful Delivery Rate

Figure 4.24: CDF on successful delivery rate of different hierarchical routing algorithms in 500
x 500 m.

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 4.5 Performance Comparison of Different Hierarchical Algorithms

For Figure 4.25 to Figure 4.30, it shows the PDF on successful delivery rate of different
hierarchical routing algorithms on different charts individually. The results in Figure 4.25 show
a bimodal distribution on successful delivery rate of CSS routing algorithm because all sensor
nodes are exhausted and can’t function starting from 900s, and there is no successful transmission
from 900s to 1000s, and thus the successful delivery rate dropped to zero after 900s. Therefore,
this forms a bimodal distribution of the PDF diagram as shown in Figure 4.25.

Figure 4.25: PDF on successful delivery rate of CCS for 500 x 500 m network.

Figure 4.26: PDF on successful delivery rate of EADAT for 500 x 500 m network.

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 4.5 Performance Comparison of Different Hierarchical Algorithms

Figure 4.27: PDF on successful delivery rate of ETR for 500 x 500 m network.

Figure 4.28: PDF on successful delivery rate of PANEL for 500 x 500 m network.

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 4.5 Performance Comparison of Different Hierarchical Algorithms

Figure 4.29: PDF on successful delivery rate of Multi-hop within d0 for 500 x 500 m network.

Figure 4.30: PDF on successful delivery rate of Temp Routing Node for 500 x 500 m network.

Then, the PDF on successful delivery rate of different hierarchical routing algorithms are
combined in a single chart for comparison as shown in Figure 4.31, and the statistical data on
successful delivery rate of all hierarchical routing algorithms are also presented in Table 4.5. It
shows that the mean of successful delivery rate of EADAT, ETR are around 0.68. Although,
PANEL can reach the highest delivery rate in some occasions, but it is more fluctuated, and
with occurrences spreading different levels of successful delivery rate. Our proposed routing
algorithm with temporary routing node can maintain steadily with a higher successful delivery
rate between 0.7 and 0.75 among all other hierarchical routing algorithms.
From Figure 4.31 and the statistical data presented in Table 4.5, we can observe that the suc-
cessful delivery rate of our proposed algorithm performs the best among all different hierarchical
routing algorithms. It can maintain with a higher successful delivery rate for 0.71 in average,
and is also relatively stable with low standard deviation at 0.09. While CCS and PANEL re-

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 4.5 Performance Comparison of Different Hierarchical Algorithms

sults with higher standard deviation among all hierarchical routing algorithms. On the other
hand, EADAT and ETR have low standard deviation, but their successful delivery rate is also
relatively lower than others.

6
CCS
EADAT
5 ETR
PANEL
Multi-hop within d0
4 Temp Routing Node (Proposed)
PDF(x)

0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x = Successful Delivery Rate

Figure 4.31: PDF on successful delivery rate of different hierarchical algorithms for 500 x 500 m
network.

Table 4.5: Mean, standard deviation and confidence interval of successful delivery rate compar-
ison with different hierarchical routing algorithms with significance level = 0.05

Mean Standard deviation Confidence interval

CSS 0.5647 0.2864 0.5250 - 0.6044
EADAT 0.6750 0.0944 0.6619 - 0.6881
ETR 0.6517 0.0859 0.6397 - 0.6636
PANEL 0.6807 0.1797 0.6558 - 0.7056
Multi-hop within d0 0.6808 0.0920 0.6681 - 0.6936
Temp Routing Node (Proposed) 0.7179 0.0950 0.7047 - 0.7311

We can find that our proposed clustering and routing algorithm maintains both higher suc-
cessful delivery rate and functioning nodes for the whole network lifetime. We have analyzed the
performance of different hierarchical clustering and routing algorithms, and discussed the root
cause under each of them below.

4.5.1 Comparison Analysis with CCS

The reason is that CCS is a chain-based routing algorithm. The whole network area are divided
into multiple chains. Each node sends data to the closest node within a chain until reaching the
cluster head of that chain. The cluster head will send to the next cluster head of another inner
chain until to the base station. CCS gets the problem with long transmission delay because the
transmissions may involve huge number of nodes, and the involved nodes consume energy which
are not necessary under such a short distance within the propagation area. Moreover, the next

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 4.5 Performance Comparison of Different Hierarchical Algorithms

inner cluster head may not within the propagation area of the sending node, and it causes data
packet loss. Besides, residual energy is not considered for selecting the cluster head. Node with
lower residual energy can also be a cluster head, and it exhausts quickly for transmitting the
huge amount of data. It also leads to lower successful delivery rate.

4.5.2 Comparison Analysis with EADAT

Next, we compare our algorithm with other tree-based hierarchical routing algorithms. EADAT
is a tree based hierarchical routing algorithm. The node chooses its parent with higher residual
power and shorter path to the sink. However, the next parent node may not be the shortest
path to the sink, and it may transmit with a longer distance, and deviate from the shortest path
to the sink. Therefore, it causes longer time delay for transmissions. More parent nodes are
involved than usual so that the overall energy consumption of all nodes is higher. It is observed
that more nodes exhausted and with lower successful transmission rate when comparing with
our algorithm.

4.5.3 Comparison Analysis with ETR

ETR is another tree-based routing algorithm. ETR selects the path with the minimal hop count
to the sink. Although path with minimal hop count seems to be a shorter path, it does not
guarantee the transmission must be a shortest path because the distance between two nodes
may be long. It may also longer than the propagation area, and this induces data packet
loss. Moreover, it does not consider remaining energy in forming a tree. Nodes with lower
residual energy may be selected as a parent node. The node with lower residual energy exhausts
quickly with the heavy communication loads. Therefore, the transmission may be failure, and
data packets are lost. It is obviously found that ETR has lower successful delivery rate when
comparing with our algorithm.

4.5.4 Comparison Analysis with PANEL

PANEL is a kind of grid-based hierarchical routing algorithm. The whole network area is divided
into smaller grids with clusters. Within each grid, one cluster head is selected with equal chance.
After collect data from other members in its grid, it sends data to other cluster heads and finally
to the base station. Panel select cluster head within its grid based on location of nodes. However,
it does not consider the distance of two cluster heads between different grids, and there may be
failure transmissions occurred due to long transmission distance. On the other hand, remaining
energy of nodes is not considered, and nodes with lower energy level may be selected as cluster
head. Therefore, the cluster head can be exhausted soon. Moreover, the distribution of nodes
may be uneven in each grid for the whole sensor network. Some grids may contain more sensor

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 4.5 Performance Comparison of Different Hierarchical Algorithms

nodes, and others may less. PANEL does not have the feasibility to cope with this variations.
Therefore, this may induce unbalanced energy consumption among all nodes and fluctuated
successful transmission rate.
To sum up, our proposed clustering and routing algorithm performs better than CCS, EA-
DAT, ETR, and PANEL with higher energy efficiency and successful delivery rate. Although
the successful delivery rate is slightly lower than PANEL, the number of alive nodes can function
for a longer period of time. This is very important because the main purpose is to extend the
network lifetime of all sensor nodes by balancing the loading of the whole sensor network, and at
the same time to ensure higher successful transmission rate. This can be fulfilled by multi-hop
transmissions to shorten the transmission distance, reducing unnecessary data transmissions to
save the energy consumption for switching states of all sensor nodes in the whole network.

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Chapter 5

Conclusions

In this Thesis, we have proposed an energy efficient routing algorithm for wireless sensor net-
work, it can improve the cluster head selection criteria by construction of threshold values with
remaining energy and centrality of nodes. Furthermore, it can minimize the number of transmis-
sions by considering the energy efficient distance for routing. Finally, the function of a temporary
routing node is introduced to improve the successful delivery rate. These measures can balance
the loading of all nodes in the whole sensor network and to maintain high successful delivery
rate at the same time.
We have classified and discussed hierarchical routing techniques as i) chain-based, ii) tree-
based, iii) grid-based, and iv) area-based routing. For each category of the hierarchical routing
techniques we have discussed in detail the main features and characteristics. Since, routing
techniques in WSNs is a well known area of research, with a wide set of research results. In
this Thesis, we presented a comprehensive survey of hierarchical routing techniques in WSNs
which have been covered in the literature. Those techniques have the common objective of
extending the lifetime of the WSM, while not compromising the performance of data delivery.
Furthermore, we classified hierarchical routing techniques based on the routing techniques. We
also highlighted some of the most relevant metrics of the routing paradigm, as well as the
advantages and disadvantages of each routing technique. Additionally, we presented a deeper
analysis of the latest improvements provided for variations of the LEACH protocol.
Since the lack of node power, physical damage, and environmental interference are the main
causes of node failures and blockages in network. We designed energy efficiency routing protocols
as the one of solutions for having a fault-tolerant behaviour and forming new links and paths
to sink nodes. Our design consists of transmission power adjustment and/or re-routing traffic
along alternative paths of higher energy nodes by accepting proper level of redundancy in the
network. We remarked that the remaining energy and energy efficient transmission distance are
significant factors for designing an energy efficient clustering and routing algorithms to improve
network lifetime. The routing algorithm must be smart and flexible to meet the changes of
the conditions of the network environments. Therefore multi-criteria routing protocols are been

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 5.1 Answering Research Questions

designed and deep investigated in our results. Routing protocols correctly designed based on
the scenario of the WSNs are able preserve nodes energy and preserve overall network lifetime
as presented in our results.

5.1 Answering Research Questions

RQ1: What are the factors affecting the energy efficiency of WSN data commu-
nications?

The more relevant observations and conclusions in this question are:

• The remaining energy of each sensor node, as expected, plays a relevant role on the overall
WSN lifetime.

• The locations of the normal nodes, cluster heads and base station are very affluent param-
eters that affect the energy consumption of the whole WSN.

• Similar to previous factor, the distances between different nodes, cluster heads and base
station are very relevant factor for WSN lifetime.

• The cluster heads selection criteria is another relevant factor, it may be based on localiza-
tion, remaining energy, proximity to Base Station and others.

• Similar to previous factor, the clustering and grouping method used for the sensor nodes
are also very relevant.

Answering this question the factors where identified, and the significance of the effects of each
factor on energy efficiency on WSN was discussed on Chapter 4.

RQ2: What are the props and cons of different hierarchical routing algorithms?

The more relevant observations and conclusions in this question are:

• For EADAT, BATR, and PEDAP, the setup cost may be high and it takes time to collect
the required information of sensor nodes to build and maintain a tree structure.

• For BATR and ETR, the residual energy of the sensor nodes is not considered in con-
structing a tree structure and routing path, sensor nodes with less energy may be selected
as parent, and it induces data loss in WSN.

• EADAT find the parent with the shorter distance; PEDAP calculates the link cost based
on the transmission distance; and ETR will decide the routing path with minimum hop

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 5.1 Answering Research Questions

counts. This may reduce the data transmission distance and reduce the energy consump-
tion. However, BATR will not consider the distance in routing.

Chapter 2 discussed in details the pros and cons of several hierarchical routing algorithms using
different criteria.

RQ3: What are the main considerations of the design a routing protocol?

The more relevant observations and conclusions in this question are:

• The selection of the cluster heads should be based on the remaining energy, the average
energy of all nodes, the distance to the base station, and the distance to all other nodes.

• The data transmission distance should be shorted using multi-hop routing, and the distance
should be within the energy efficient distance to minimize the energy consumption.

• For transmission between cluster heads which are over the sensors coverage, temporary
routing nodes may be assigned in between to minimize the data packet loss.

• The energy consumption of the whole WSN can be balance by finding the most suitable
cluster head for that particular situation and to perform cluster head rotation to share the
loading.

Chapter 3 and Chapter 4 discussed in details the consideration for the design of hierarchical
routing algorithms.

• RQ3.1: How to select the suitable cluster head and form cluster?

After analysing different factors that affect cluster head and cluster formation, we can con-
clude that to select a cluster head, the remaining energy and density of the sensor nodes should
be used as a key factor. Instead to form a cluster, if a nodes distance to BS is within the en-
ergy efficient distance, then joining cluster should not be required for a sensor node. Otherwise,
sensor nodes should join the nearest cluster head to form a cluster. Chapter 3 and Chapter 4
discussed in details the factors that affect the cluster head and formation of cluster and present
relevant results.

• RQ3.2: How to shorten the data transmission distance with routing in

order to maximize the energy efficiency?

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 5.2 Challenges and Future Research Trends

After analysing the transmission distance, we can conclude that if a sensor nodes distance
to BS is within the energy efficient distance, it should send data to the BS directly. Otherwise,
a sensor node should send data to the nearest cluster head, and then cluster head should send
the data to the next cluster head until reaching the base station. We observed that sometimes
to shorten the transmission distance and also maximize the energy efficiency, sending data with
the shortest distance may not be the good solution because it induces frequent changes of nodes
states, and increases energy consumption. Therefore, it is proposed to send data to the farthest
next cluster head within the energy efficient distance. If there is no next cluster head within the
energy efficient distance, a temporary routing node can be selected for communication. Chapter
3 and Chapter 4 discussed techniques to maximize the energy efficiency and present relevant
results.

• RQ3.3: How to balance overall energy efficiency of all sensor nodes for the
whole network?

After analysing different scenarios and techniques, we can conclude that cluster heads should
be rotated to balance the loading of all nodes. If a nodes remaining energy is lower than
the average remaining energy of all nodes, it cant be selected as a cluster head anymore, and
functioned as a normal sensor node only. The purpose of this technique is to extend the time
of the first nodes energy being exhausted without affecting the normal operation of the whole
WSN. Chapter 3 and Chapter 4 discussed techniques to balance the energy in the whole WSN
and present relevant results.

5.2 Challenges and Future Research Trends

WSN has become one of the technologies for the future by driving the consolidation of IoT as
one of the most innovative technology. Nowadays, smart sensor nodes are involved in almost
every field of life, supporting various monitoring and tracking applications. Resource constraints
pertaining to WSNs impose several challenges and therefore new frontiers of research [224; 225]
in the field. Some of the most relevant challenges that are driving the research trends in WSN
are:

• Security: The future of WSN is evolving in a subset of IoT technology. Predicting mil-
lions of nodes being added to WSNs and the internet will provide malicious actors with
innumerable number of attacks, especially because the sensor nodes suffer from security
holes [226]. There are many reasons behind the state of insecurity in WSN, limited compu-
tational resources, limited energy budget and limited memory are the most relevant. In the

102
 5.2 Challenges and Future Research Trends

future, security concerns will no longer be limited to the protection of sensitive informa-
tion and assets, it will extend to the protection of sensitive sensor as example healthcare.
Therefore WSN cyber-security architectures, protocols and algorithms will be the focus of
research [227].

• Privacy: The IoT creates unique challenges to privacy, many that go beyond the data
privacy issues that currently exist in WSN, because WSNs were originally designed to
operate in private networks. Integrating sensors devices into private environments without
properly protection and connecting to Internet is becoming a common use. However, the
collection of private information using WSN exposes legal and regulatory challenges facing
data protection and privacy law [228].

• Regulatory Standards: Regulatory standards for managing data from collection, storing
and usage are missing. There is a need for clear guidelines on the retention, use, and
security of the WSN data. This will drive the new generation of sensor on hardware and
software [229].

• Compatibility and Longevity: IoT is growing in many different directions. This will
cause difficulties and require the deployment of extra hardware and software when connect-
ing sensor devices. Other compatibility issues stem from non-unified cloud services, lack
of standardized M2M protocols and diversities in firmware and operating systems among
sensor devices [226; 230].

• Real-time Data Processing and Analysis: Analysis of data on a real-time or a near

real-time basis, driving timely decision making and action are becoming mandatory spe-
cially on industrial WSN [231; 232].

However, the most challenging issue in WSN remain the limited amount of energy in the
sensors. While renewable energy technologies similar to solar and wind panels are not new, the
systems are far too large or invasive (in several applications) for WSNs. Thus, the design of
networking protocols for such WSNs powered by ambient energy harvesting remain the most
relevant research trend in WSN [233].

103
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