Cloud Computing Model for Vehicular Ad hoc

Networks
1
Kashif Naseer Qureshi, 1Faisal Bashir, 2Saleem Iqbal
1
Department of Computer Science, Bahria University, Islamabad
2
University Institute of Information Technology, PMAS-AAUR, Rawalpindi, Pakistan
kashifnq@gmail.com, siqbal82@yahoo.com, faisalwn@yahoo.com

Abstract— Cloud computing has become a significant for travelers. Mobile Cloud Computing (MCC) offers various
network access model due to its transparent and ubiquitous services for VANETs in which the data processes anytime
sharing environment with a large number of computing and anywhere in the network [3]. In MCC, the drivers are
resources. Cloud computing services have the potential to using mobile devices for communication, which are
improve vehicular network services through its flexible and connected with the cloud through the internet. The MMC
valuable services. These cloud network services improve the dispenses the requisite environment to incorporate with other
performance of the vehicular application by advance technologies for monitoring the road safety by processing the
communication, traffic management, standardization and data with the help of divergent mobile cloud architectures
safety systems. In vehicular ad hoc networks, vehicle nodes
such as Platform as a Service (PaaS). Although, the mobile
carry more information by adopting cloud computing services
and improve their sensing power, storage capabilities, and
devices have restricted due to limited battery life, limited
onboard computing services. Vehicular cloud computing is one processing capabilities, limited data accessing and other
of the emerging solutions to address the vehicular networks computing resources [4]. These types of services are time-
communication barriers with new hybrid technologies and consuming and costly due to real-time data processing such
remarkable storage features for traffic management and road as traffic jam data processing, accident detection messages
safety by instantly using vehicular resources. In this paper, we transmission [5]. Vehicular Cloud Computing (VCC) utilizes
proposed a vehicular cloud computing model for vehicular ad desired features to serve drivers more precisely. The main
hoc networks. This model provides computational services to aim of VCC is providing low-cost computational services
vehicle nodes and improves the data communication and and improving the performance of VANETs applications.
network performance. In addition, the paper also discusses the VCC also minimizes the traffic congestion issues with better
proposed model different services including data aggregation, Quality of Services (QoS) and manages traffic conditions in
security, privacy, and resource management. At last, the the network [6]. New solutions of VCC are technically
experimental results indicate the better performance of the expedient for ITSs to improve traffic management services in
proposed model in the network. VANETs [7].
Keywords— Cloud computing, traffic management, safety, The main objective of this paper is to introduce a new
vehicular ad hoc networks, component cloud computing based data communication model for
VANETs. The proposed model will address the data routing
I. INTRODUCTION limitations and issues. In the proposed model, the cloud is
available for data communication services and provides fast
Recent advancement in communication technologies has data processing, unlimited storage, and low-cost solutions
introduced new and emerging vehicular communication without any additional resources within the network.
systems with unlimited features to provide safety and
comfort in the transportation sector. Vehicular Ad hoc II. RELATED WORK AND CHALLENGES
Networks (VANETs) are based on a set of communication The authors in [8], proposed a vehicular cloud model by
systems in which the moving vehicle nodes are able to utilizing cloud and vehicular computing resources. In this
communicate with each other for different applications [1, model, the groups of vehicle nodes are using cloud
2]. The main aim of these networks is reducing the traffic computing and communication resources with more sensing
accidents which are caused by deaths and injuries. These capabilities, which can be communicated and allocated for
communication systems are equipped with smart and digital the end users. The proposed model is based on vehicular
traffic navigation, congestion control, and management resources instead of full cloud computing resources.
systems to improve traffic conditions in the network. Due to Although, the vehicular cloud resources are not always being
high the mobility and dynamic nature of traffic in these switched on and often need the authorization of inattentive
networks, the communication systems have been suffered especially when vehicle nodes are in a steady state. users,
from dis-connectivity, delay, packet dropping and limited which can be
storage challenges. Furthermore, the environmental obstacles
attenuate radio signals and disturb data transmission. Some The author in [9] proposed a system called CROWN
costly adopted services also make these networks and which is based on Roadside Units (RSUs) and acts as cloud
communication systems more complex. The pervasive interfaces and directories. The model does not take the
roadside infrastructure offers high-speed internet access by onboard units as a cloud computing object for end users.
advanced wireless communication technologies (3/4/5 G, However, RSUs are not available in some areas and vehicle
WiMAX, LTE) and brings innovative and divergent benefits nodes suffered in hanging and unavailable services issues in
the network. Another effort has taken Vehicular Cloud for
978-1-5386-6831-3/18/$31.00 ©2018 IEEE
Roadside scenario (VCR) in [10], based on RSUs to provide
safety and comfort to travelers in the network. However, this
system has computational complexities and overhead issues The first layer is an end-user lower layer, where the users
due to its unique messages types in the network. Another use computing and communication devices such as
pure cloud [11] system was proposed and introduced a new smartphones, laptop, GPS and onboard computers. The users
term called Sensor as a Service (SenaaS) based on available establish the services requests from adjacent layers by
third-party monitoring applications, sensors, and devices. service access point. The next layer is the communication
The system does not consider the traditional cloud layer to makes the connection between end-user, RSUs
computing resources which are usually requested from user’s devices and service access points. In this level, all
side. communication technologies are defined and fixed according
to the devices such as 3D/4D standards. The third layer is
Cloud computing is in early stages and has many serious the infrastructure layer, where several communication
challenges such as security and privacy The issue becomes devices are installed such as cellular base stations, RSUs, and
more challenging when cloud onboard units in vehicles are private networks. The last layer is a cloud, where cloud
used as a server. These systems need more security and services are available. This layer consists of traditional
privacy to ensure the data integrity and control and stationary cloud data centers and other computing devices
preventing data losses. Another main challenge is sensing entities. These services are available for end-users by using
and aggregation data capabilities, where new solutions are
service access points.
required to advances the sensing capabilities including traffic
data, health and environment data for further analysis. Cloud
data centers are consuming more energy and resources [12].
New approaches and models are required to reduce the
energy cost. Due to high mobility and dynamic topologies
network suffered from disconnectivity, packet dropping, and
delay issues [13].
In order to address the aforementioned challenges, new
research directions are required to tackle the different
challenges. In the next section, we discuss the proposed
model for VANETs.
III. VEHICULAR CLOUD MODEL (VCM)
Vehicular Cloud Model (VCM) extends the traditional
cloud infrastructure with stationary nodes. This enhancement
achieves by integrating new computing resources, which are
installed in vehicle nodes such as onboard units. In vehicle
nodes, drivers access the computing resources using
stationary and moving nodes in the virtualized manner in the
network. The system is not only for the drivers but also for
other users. VCM model is based on four layers namely end-
user layer, communication layer, infrastructure and cloud
layer. The proposed model will improve the traffic Fig. 1. Vehicular Cloud Model
applications performance. The system also contributes to
other functions such as data storage, processing power, and The cloud services are divided into three main types:
networking capabilities. Next subsections describe the layers SaaS, IaaS, and PaaS. The SaaS service contains
in detail. entertainment services, email services and the IaaS provides
storage, processing and virtualization services. PaaS uses to
provide operating systems, data warehouse and web services.
A. Model Architecture The service providers are responsible to make
VCM is divided into two sub-models: primary interconnection between primary and secondary sub-models
(permanent) and secondary (temporary) cloud models. The based on different protocols and techniques. Each vehicle
primary model belongs to cloud computing, where it is node in primary sub model (RSU, end-user, vehicle) can
providing processing, storage, virtual environment, access the secondary cloud sub model. Fig. 2 shows the
bandwidth, facilities to the RSUs and vehicle nodes. This sub vehicular cloud sub models.
model has virtualized and stationary data centers to access
the instant cloud computing infrastructure. These centers are
interconnected with other networks and have various
functionalities and services and then further connected with
data centers. Data centers have different applications and
services for end-users.
The secondary or permanent model contains vehicular
computing resources and other devices. In this model, the
vehicle nodes and RSUs utilize their computing resources for
other clients such as when vehicles are in parking lots and
passengers are waiting for the bus. Therefore, VCM provides
all available computing resources. The VCM is divided into
four layers as shown in Fig. 1.
Fig. 2. Vehicular Cloud Sub Models
 IV. PERFORMANCE EVALUATION This degradation is because of existing models
The experiment results are done using NS-2.34 with architecture and limited resources. Both experiments results
SUMO mobility model [14, 15]. This network is already show that the proposed cloud-based model VCM is more
based on cloud computing services. We compare the feasible for VANETs in the presence of dense traffic in the
proposed model with existing models in terms of network.
performance parameters such as data packet delivery ratio
and data delay in the network. In these experiments, all V. CONCLUSION
vehicle nodes are exchanging information for different In this paper, we proposed a vehicular cloud model based
applications and access the cloud models in the network. on two sub models and four layers. The model provides
When the numbers of vehicle nodes are more in the network cloud services to vehicular users to avail low cost
and access more processing power, data storage’ then all computational and other services. It can also enhance the
network suffered from data loss and delay issues in the traffic management services and improve road safety by
network. gathering and sensing traffic data from vehicles and roadside
Fig. 3 shows the packet delivery ratio of the proposed units. The proposed model can help the drivers and
model with existing models namely VCR and CROWN. All passengers to avail their computing needs during travel. The
data is collected from a cloud based data server. Results model can support various services allowing road users to
indicate that the existing models are not able to perform all avoid road accidents and collisions.
services efficiently and drop the data packets. In the presence
of more vehicle nodes, the proposed VCM model is more REFERENCES
efficient compared to state of the art models. [1] F. J. Ros, J. A. Martinez, and P. M. Ruiz, "A survey on modeling and
simulation of vehicular networks: Communications, mobility, and
The next experiment in Fig. 4 shows the average delay in tools," Computer Communications, vol. 43, pp. 1-15, 2014.
the network in the presence of different numbers of vehicle [2] S. Al-Sultan, M. M. Al-Doori, A. H. Al-Bayatti, and H. Zedan, "A
nodes. The existing cloud models have suffered when the comprehensive survey on vehicular Ad Hoc network," Journal of
numbers of vehicle nodes are more in the network. network and computer applications, vol. 37, pp. 380-392, 2014.
[3] S. Abolfazli, Z. Sanaei, E. Ahmed, A. Gani, and R. Buyya, "Cloud-
based augmentation for mobile devices: motivation, taxonomies, and
open challenges," 2013.
[4] M. Shiraz, A. Gani, R. H. Khokhar, and R. Buyya, "A review on
distributed application processing frameworks in smart mobile
devices for mobile cloud computing," Communications Surveys &
Tutorials, IEEE, vol. 15, pp. 1294-1313, 2013.
[5] N. Fernando, S. W. Loke, and W. Rahayu, "Mobile cloud computing:
A survey," Future Generation Computer Systems, vol. 29, pp. 84-106,
2013.
[6] M. Gerla, "Vehicular cloud computing," in Ad Hoc Networking
Workshop (Med-Hoc-Net), 2012 The 11th Annual Mediterranean,
2012, pp. 152-155.
[7] N. Tekbiyik and E. Uysal-Biyikoglu, "Energy efficient wireless
unicast routing alternatives for machine-to-machine networks,"
Journal of Network and Computer Applications, vol. 34, pp. 1587-
1614, 2011.
[8] S. Olariu, T. Hristov, and G. Yan, "The next paradigm shift: from
vehicular networks to vehicular clouds," Mobile ad hoc networking:
Fig. 3. Packet Loss Ratio cutting edge directions. 2nd ed. NJ, USA: John Wiley & Sons, Inc.,
Hoboken, 2013.
[9] K. Mershad and H. Artail, "Finding a STAR in a Vehicular Cloud,"
Intelligent Transportation Systems Magazine, IEEE, vol. 5, pp. 55-68,
2013.
[10] D. Baby, R. Sabareesh, R. Saravanaguru, and A. Thangavelu, "VCR:
vehicular cloud for road side scenarios," in Advances in Computing
and Information Technology, ed: Springer, 2013, pp. 541-552.
[11] N. Zingirian and C. Valenti, "Sensor clouds for intelligent truck
monitoring," in Intelligent Vehicles Symposium (IV), 2012 IEEE,
2012, pp. 999-1004.
[12] Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: state-of-the-
art and research challenges," Journal of internet services and
applications, vol. 1, pp. 7-18, 2010.
[13] Qureshi KN, Abdullah AH, Altameem, “A. Road Aware
Geographical Routing Protocol Coupled with Distance, Direction and
Traffic Density Metrics for Urban Vehicular Ad Hoc Networks.”
Wireless Personal Communications, vol 1;92(3):pp.1251-70,2017.
[14] Qureshi KN, Abdullah AH, Kaiwartya O, Ullah F, Iqbal S, Altameem
A, “Weighted link quality and forward progress coupled with
modified RTS/CTS for beaconless packet forwarding protocol (B-
PFP) in VANETs”. Telecommunication Systems.1-6, 2016.
Fig. 4. Average Delay
[15] R. Kim, H. Lim, and B. Krishnamachari, "Prefetching-based data
dissemination in vehicular cloud systems," IEEE Transactions on
Vehicular Technology, vol. 65, pp. 292-306, 2016.