computational intelligence based secure clustering
TRANSCRIPT
Computational Intelligence based secure
clustering techniques for Vehicular ad hoc
Networks (VANETs)
By
Atif Ishtiaq
Reg. No. 1793-315028
Doctoral Thesis
In
“Computer Science”
Iqra National University, Peshawar- Pakistan
Spring 2019
Iqra National University
Computational Intelligence based secure clustering
techniques for Vehicular ad hoc Networks (VANETs)
A Thesis Presented to
Iqra National University, Peshawar- Pakistan
In partial fulfillment
Of the requirement for the degree of
Doctor of Philosophy
In
Computer Sciences
By
Atif Ishtiaq
Reg# 1793-315028
Spring, 2019
Computational Intelligence based secure clustering
techniques for Vehicular ad hoc Networks (VANETs)
A Post Graduate Thesis submitted to the Computer Science Department as
partial fulfillment of requirement for the award of Degree of Doctor of
Philosophy in Computer Science.
Name Registration Number
Atif Ishtiaq 1793-315028
Supervisor:
Dr. Sheeraz Ahmed
Associate Professor,
Department of Computer Sciences
Iqra National University
Peshawar, Pakistan.
Co-Supervisor:
Dr. Farhan Aadil
Assistant Professor
Department of Computer Sciences
COMSATS University Islamabad, Attock Campus
Attock, Pakistan.
Final Approval
This thesis titled
Computational Intelligence based secure clustering
techniques for Vehicular ad hoc Networks
(VANETs)
By
Atif Ishtiaq
1793-315028
Has been approved
For the Iqra National University, Peshawar
External Examiner ________________________________________
Dr. ------------------------------------------------
------------------------------------------------
Supervisor:
________________________________________________________________
Dr. Sheeraz Ahmed
Associate Professor, Department of Computer Science, INU, Peshawar
Co-
Supervisor:___________________________________________________________
Dr. Farhan Aadil
Assistant Professor, Department of Computer Science, CUI, Attock Campus
Declaration
I Atif Ishtiaq, Registration # 1793-315028 hereby declare that I have produced the
work presented in this thesis, during the scheduled period of study. I also declare that
I have not taken any material from any source except referred to wherever due that
amount of plagiarism is within acceptable range. If a violation of HEC rules on
research has occurred in this thesis, I shall be liable to punishable action under the
plagiarism rules of the HEC.
Date: __________________
______________________
Atif Ishtiaq
Reg # 1793-315028
Certificate
It is certified that Atif Ishtiaq, Registration # : 1793-315028 has carried
out all the work related to this thesis under my supervision at the
Department of Computer Science, Iqra National University, Peshawar
and the work fulfills the requirement for the award of PhD Degree.
.
Date: _______________
Supervisor:
___________________________
Dr. Sheeraz Ahmed
Head of Department:
______________________
Dr. ________
DEDICATION
Dedicated to My Family, and Teachers
List of Figures
Figure 1.1 Applications of WSNs ................................................................................. 8
Figure 1.2 Block Diagram of WSNs ........................................................................... 09
Figure 1.3 Block Diagram of Sensor Node ................................................................. 03
Figure 1.4 Architecture of Wireless Visual Sensor Netwrks (WVSNs) ..................... 13
Figure 1.5 A schematic of ITS services in VANETs ............................................... 138
Figure 1.6 Vehicular Communication Infrastruture VCI .......................................... 139
Figure 1.7 VANET Infrastructure ............................................................................... 13
Figure 1.8 Network layer operations in VANETs ...................................................... 13
Figure 2.1 Clustering Technique for Target Tracking in VANET ............................. 26
Figure 3.1 Clustering in VANET's ............................................................................. 51
Figure 3.2 Communication in vehicular ad Hoc Networks ........................................ 52
Figure 3.3 Another schematic of ITS services in VANETs........................................ 53
Figure 3.4 Transverse orientation of moth flame........................................................ 58
Figure 3.5 Flow chart of ICMFOs .............................................................................. 60
Figure 4.1. Number of clusters vs Nodes vs Transmission range in ICMFO, MOPSO,
CLPSO and CACONET for nodes 30 to 60, and Grid Size = 1000 m ....................... 67
Figure 4.2. Number of clusters vs Nodes vs Transmission range in ICMFO, MOPSO,
CLPSO and CACONET for nodes 30 to 60, for Grid Size = 2000 m ........................ 68
Figure 4.3. Number of clusters vs Nodes vs Transmission range in ICMFO, MOPSO,
CLPSO and CACONET for Nodes 30 to 60, for Grid Size = 3000 m ..................... 668
Figure 4.4. Number of clusters vs Nodes vs Transmission range in ICMFO, MOPSO,
CLPSO and CACONET for Nodes from 30 to 60, for Grid Size = 4000m ............... 70
Figure 4.5. Load Balance Factor in case of CLPSO, MOPSO, CACONET and
ICMFO when Grid Size is 1000m×1000m and Transmission Range varying from 100
to 600 and Number of Nodes = 30. ............................................................................. 71
Figure 4.6. Load Balance Factor in case of CLPSO, MOPSO, CACONET and
ICMFO when Grid Size is 1000m×1000m and Transmission Range varying from 100
to 600 and Number of Nodes are 40. .......................................................................... 72
Figure 4.7. Load Balance Factor in case of CLPSO, MOPSO, CACONET and
ICMFO when Grid Size is 1000m×1000m and Transmission Range varying from 100
to 600 and Number of Nodes are 50. .......................................................................... 73
Figure 4.8. Load Balance Factor in case of CLPSO, MOPSO, CACONET and
ICMFO when Grid Size is 1000m×1000m and Transmission Range varying from 100
to 600 and Number of Nodes are 60. ........................................................................ 734
Figure 4.9. Load Balance Factor in case of CLPSO, MOPSO, CACONET and
ICMFO when Grid Size is 2000m×2000m and Transmission Range varying from 100
to 600 and Number of Nodes are 40. .......................................................................... 76
Figure 5.1 Design of VANETs Security Model.......................................................... 79
Figure 5.2 Vehicle to Vehicle Communication .......................................................... 80
Figure 5.3 Flow Chart of ARV2V Security Model..................................................... 83
Figure 6.1 Trust Computation Error vs Vehicle Density ............................................ 87
Figure 6.2 End-to-End Delay (106 Seconds) vs Vehicle Density ............................... 89
Figure 6.3 Average Link Duration vs Vehicle Density .............................................. 90
Figure 6.4 Normalized Routing Overhead vs Vehicle Density .................................. 92
List of Tables
Table 2.1 Summary of Related work on VANETs ..................................................... 20
Table 2.2 Summary of Related work on VANETs Security ....................................... 25
Table 2.3 Clustering Protocols .................................................................................... 38
Table 2.4 A survey on Swarm Intelligence Algorithms ................................................... 47
Table 3.1 Physics Based Algorithms .......................................................................... 53
Table 3.2 Evolutionary Algorithm .............................................................................. 21
Table 3.3 Swarm Intelligence Algorithm.................................................................... 55
Table 3.4 Proposed ICMFO Algorithm ...................................................................... 61
Table 4.1 Simulation parameters for MOPSO and CLPSO ........................................ 64
Table 4.2 Simulation parameters for ICMFOs ........................................................... 65
Table 6.1 Trust Computation Error per 200 Vehicles ................................................. 87
Table 6.2 End-to-End Delay (106 Seconds) per 200 Vehicle Density ........................ 89
Table 6.3 Average Link Duration (104 Seconds) per 200 Vehicle Density ................ 91
Table 6.4 Normalized Routing Overhead per 200 Vehicles ....................................... 92
Table of Contents 1 INTRODUCTION ..................................................................................................
1.1 Application Areas:............................................................................................
1.2 Scope of the Thesis ..........................................................................................
1.3 Vehicular Ad Hoc Networks (VANETs) .........................................................
1.3.1 Roadside Units (RSUs): ............................................................................
1.3.2 On Board Units (OBU): .......................................................................... 9
1.3.3 Vehicular Communication Infrastructure VCI ....................................... 9
1.4 Vehicle to Vehicle (V2V) Communication:.....................................................
1.5 Vehicle to Infrastructure (V2I) or Infrastructure to Vehicle (I2V)
Communication: ........................................................................................................ 9
1.6 Hybrid Vehicle Communication (HVC): ....................................................... 9
1.7 Distinguishing Features of VANETs: .......................................................... 11
Rapidly changing topology: ......................................................................... 12
Rich resources: ............................................................................................. 12
Frequently disconnected network: ............................................................... 12
Mobility models and prediction of future positions: .................................... 12
Hard delay constraints: ................................................................................. 12
Various traffic environments:....................................................................... 12
Geographical addresses: ............................................................................... 12
GPS equipped on board sensors: .................................................................. 12
1.8 Objectives ..................................................................................................... 14
1.9 Approaches ................................................................................................... 14
1.9.1 MOPSO ................................................................................................. 14
1.9.2 CLPSO .................................................................................................. 15
1.9.3 Moth Flame Optimizer (MFO) .............................................................. 15
2. Literature Review................................................................................................... 17
2.3 Clustering Technique for Target Tracking in VANETs ............................... 23
2.4 An Introduction to VANET Clustering Algorithms ..................................... 24
2.4.1 Total Forces (calculated based on distance, direction and relative
velocity) .............................................................................................................. 25
2.4.2 Velocity Difference ............................................................................... 26
2.4.3 Node ID as weight value ....................................................................... 32
2.4.4 MANET Clustering Algorithms ........................................................... 33
2.5 Swarm Intelligence Algorithms ................................................................... 40
2.5.1 Physics-based Algorithms ..................................................................... 41
2.5.2 Evolutionary Algorithms ...................................................................... 41
3. ICMFO: Intelligent Clustering using Moth Flame Optimizer for VANETs .......... 18
3.1 Mathematical Modeling ............................................................................... 23
3.1.1 Flow Chart: ........................................................................................... 26
3.1.2 Pseudo code for proposed ICMFOs Algorithm .................................... 27
3.2 Computational Complexity of ICMFO: ....................................................... 27
3.2.1 Solution construction by a single ant: ................................................... 28
3.2.2 Solution Quality / Fitness: .................................................................... 28
3.2.3 Searching, Encircling and Attacking: ................................................... 28
3.2.4 Complexity of while loop (i.e. batch of moths): ................................... 28
3.2.5 For ‘r’ rules creations in WHILE loop:................................................. 28
4 Experiments and Results ..................................................................................... 64
4.1 Experimental Setup ...................................................................................... 66
4.2 Transmission Range vs Number of Clusters ................................................ 69
4.3 Number of Clusters vs Network Nodes........................................................ 73
5. Modeling and Simulation of VANETs Security Scheme
5.1 Network Deployment ........................................................................................ 79
5.2 Framework for ARV2V Scheme ...................................................................... 81
5.3 ARV2V Mathematical Model ........................................................................... 84
6. Results and Discussions
6.1 Trust Computation Error ................................................................................... 87
6.2 End-to-end Delay .............................................................................................. 88
6.3 Average Link Duration ..................................................................................... 89
6.4 Normalized Routing Overhead ......................................................................... 91
7. Conclusion and Future Work 7.1 Conclusions .................................................................................................. 94
7.2 Limitations ................................................................................................... 96
7.3 Future Work ................................................................................................. 97
7.4 Contributions ................................................................................................ 97
References……………………………………………………………………… 98
ACKNOWLEDGEMENT
Thanks to Allah first, then I am highly indebted to my supervisor Dr. Sheeraz
Ahmed, Associate Professor Computer Science Department, Iqra National
University, Peshawar co-supervisor Dr. Farhan Aadil, Assistant Professor Computer
Science Department, COMSATS University, Attock Campus for their utmost
support and encouragement at all stages of the research work. Without their able
guidance and support it would have been impossible for me to accomplish this task
successfully.
I also express my thanks to my mother, my wife, my colleagues at INU and other
faculty members of Computer Science Department for giving me valuable
suggestions throughout my work. I sincerely thank them for their guidance and help
through the hard and easy timing during the development of research.
Then, thanks to my Chancellor, Mr. Obaid-ur-Rehman who have always been the
most inspiring person in my life.
Atif Ishtiaq
ABSTRACT
VANETs, an application of MANETs, enable ITS using IEEE 802.11p
standard which is in favor of DSRC specifically designed for WAVE scenario.
VANETs establish communication among vehicles (V2V) and road side
infrastructure (V2I); while V2I communication using IEEE 802.11a/b/g standard. In
VANETs vehicles, road side entities disseminate FSAMs about road conditions and
other vital circumstances to ensure safety and avoid losses of precious lives and
property. As in VANETs system vehicles move with high speed, so due to high
mobility environment and topology also changes with time. In VANETs system
accurate and on time delivery/reception of FSAMs is highly important to withstand
against maliciously inserted security threats affectively. Hence, there is no optimum
routing protocols which ensure on time delivery of FSAMs to destination. Due to
frequent alteration in VANETs topology path failure, inter vehicle distance change
and malicious node penetration may also result. So absolutely optimum protocols for
secured delivery of packets exchange is still challenging.
Clustering for VANETs is extremely beneficial but stability of existing
clustering algorithms for VANETs exhibit poor robustness due to their dynamic
nature. In this thesis, a new clustering algorithm is presented for VANETs by the
name of moth flame optimization-driven, reproducing the social behavior and hunting
approach of moth flames in designing proficient clusters. Due to the random range of
VANETs, stability is a major area of research which has gained much attention. The
main idea of presented algorithm is extracted from the living routine of moth flames.
Presented algorithm permits well-organized communication by generating the
amplified number of clusters and their unsupervised working make it as intelligent.
Intelligent Clustering using Moth Flame Optimization (ICMFO) scheme is
accomplished for determining and optimizing the clustering issues in VANETs; the
primary focus of which is to enhance the stability in such networks. ICMFO is then
validated by comparison with two other existing variants of Particle Swarm
Optimization (PSO), i-e; Multiple-Objective Particle Swarm Optimization (MOPSO)
and Comprehensive Learning PSO (CLPSO) and one existing scheme of Ant Colony
Optimization (ACO) known as Ant Colony Optimization Based clustering algorithm
for VANET (CACONET). Simulation results demonstrate that ICMFO is providing
optimal results in comparison to existing techniques.
It is also cleared from the proposed work results of different researchers, that
there is no such protocols that is best suited for clustering as well as security
implication in VANETs. Different routing schemes have different conduct
performance metrics. In our thesis we concentrated and inspected different routing
protocols. We have also presented a new security based scheme named ARV2V; and
compared its results with existing techniques which are Trust and Logistic Trust in
terms of TCE, EED, ALD and NRO. The scheme has presented security implication
in our clustering based scheme ICMFO. In terms of TCE, ARV2V is 11.6% and 7.3%
efficient than LT and Trust respectively. In terms of EED, we found ARV2V 57.6%
performance 5.2% better than LT, also Trust schemes met 52.4% more delay than LT.
Similarly, in term of ALD ARV2V provides 29.7% and 7.8% more stable link
duration than Trust and LT respectively, however LT has 21.9% proficient ALD than
Trust. ARV2V protocol have 27.5% and 14% lesser load than Trust and LT
respectively in terms of NRO, while Trust has approximately 13% more NRO than
LT.
CHAPTER 1
1
INTRODUCTION
Wireless Sensor Networks (WSNs) is a self-organizing, infrastructure less
network. WSNs are comprised of various minor nodes with small cost, low battery
power, lesser communication bandwidth and limited computational capabilities.
These nodes are used to collect information, integrate and transmit data in a wireless
fashion and handover it to the Base Station (BS) [1].
When sensor nodes are deployed they portrait themselves in earmark
infrastructure ensuring a multi hope communication with them. Sensor nodes collect
information and forward these information to a main location say sink where data is
detected and analyzed. Sink or BS is a port or interface between the network and the
applicant. Figure 1.1 shown WSNs applications in various scenarios. WSNs
comprised of power components, radio transceiver, computing and sensing devices.
Sensor are hundred and thousand in numbers communicating with each other through
radio communication [2].
WSNs have diversified applications, like area monitoring to monitor specific
conditions like temperature, pressure, vibration and sound the detected event is
recorded in a BS. War fare or military applications, health applications like micro-
surgery, environmental conditions like land slide detections or air pollution
monitoring. Also industrial, Structural, agriculture and in other numerous fields
monitoring, WSNs are used [2].
2
Figure 1.1: Applications of WSNs
Figure 1.2: Block diagram of WSNs
Sensor networks cater everlasting opportunities, but also facing certain
challenges. Sensor networks have no centralized controller i.e. infrastructure less ad
hoc networks and medium of communication amongst nodes are pure wireless, so
that’s why encountering loss or attenuation.
3
Other challenges are including limited battery power which die out soon
depending upon on the performance of computational activities. Batteries of sensor
nodes are non-renewable. To increase the lifespan of WSNs efficient energy
utilization protocols needed to design from the start. Block diagram of sensor node is
shown in figure 1.3. Sensor nodes are indefensible because they are mostly deployed
in dangerous zones so that’s why protocols design for it should be capable to tolerate
fault and detect the failure and re-rout the packet or data through another rout as soon
as possible [2].
However, present advancement in low power Very Large Scale Integration
(VLSI), Micro Electro-Mechanical Systems (MEMS), embedded computing,
communication hardware devices, and convergence of communications and
computing, made the WSNs an emerging technology a reality. If the production cost
of sensor nodes is made cheaper than WSNs can compete the traditional gathering
information technologies approaches. Sensor nodes have power constrain, nodes
should be design to consume little amount of energy efficiently and provide best
output so lifetime of the network should be prolonged [1].
Figure 1.3: Block diagram of sensor node
1.1 Applications of Wireless Sensor Networks (WSNs)
There are dozens of WSNs applications in which few are Under Water Sensor
networks (UWSNs), Body Area Networks (BANs), Wireless Multi-media Sensor
Networks (WMSNs), and Terrestrial Sensor Networks [3].
Wireless Underground Sensor Networks (WUSNs), Wireless Visual Sensor Networks
4
(WVSNs), Vehicular Ad hoc and Sensor Networks (VASNETs) and Vehicular Ad
hoc Network (VANETs).
1.1.1 Under Water Sensor Networks (UWSNs)
Under Water Sensor Networks (UWSNs) is an application of WSNs using
acoustic signal, because radio frequency signal do not work in under water scenario.
Another difficulty in underwater communication is the propagation of acoustic signal,
whose magnitude is few times smaller than that in RF. In under water acoustic
network the propagation delay of the signal is significant but in RF signal propagation
delay is unimportant [3].
Different protocols were used in UWSN to mitigate the drawbacks in different
factors. Some of these protocols are Depth Based Routing (DBR), Energy efficient
Adaptive Hierarchical and Robust Architecture Enhanced (EDETA-e), Autonomous
under Water Routing Protocols (AURP) used for controlling of Autonomous
Underwater Vehicles (AUV), Vector Based Forwarding (VBF), Minimum Cost
Clustering Protocol (MCCP) and Multi Path Routing (MPR). MPR protocols reduce
delay of packets delivery in UWSN [3].
1.1.2 Wireless Body Area Networks (WBANs)
Nowadays WBANs are under industries focus for their worthless
performances in different categories of daily life, especially in healthcare crises.
However, Biomedical Sensors (BMSs) are reasonable in price, and owning specific
communication and computation capacity. Main objective applications of BSNs are
the procurement of medical healthcare services in emergency situations.
BSNs recording accurate data from different sensors and easily detect the
patient condition at early stage and hence result in reduce health cost. In medical
healthcare BSNs is used for measurement of temperature, heart rate, blood Pressure,
electro cardiogram (ECG) etc. Different routing mechanisms are implemented in
BSN for efficient on time reliable data delivery to the control center [3].
1.1.3 Wireless Multi-media Sensor Networks (WMSNs)
5
Recent fast improvement in MEMS and wireless communication make WSNs
Possible. Such networks consist of enormous number of low power, low cast sensor
nodes having the capacity of performing multiple tasks. Today sensor nods have the
capability of collecting audio as well as visual information, these nodes are equipped
with low budget hardware devices like array sensor, which make possible the design
of WMSNs. In WMSNs nodes capture audio and video multimedia streams from the
environment [3].
WMSNs provide healthcare, traffic avoidance, industry process control etc. In
Case of disaster, battlefield, indoor security and other emergency situation real time
multimedia streaming are used. Now in a bounded transmission region WMSNs
communicate wirelessly with Wireless Video based Sensor Networks (WVSNs) to
capture video information of the zone under observation and transmit information via
multi hope to the Base Station (BS) [3]. In WVSNs two major disputes arises which
are energy efficiency and video quality. Senor nodes have limited battery power so
die out soon which is a challenge in energy, other video quality is suffered due to few
factors including limited power, memory, RF, and processing of information in VSNs
[3].
6
1.1.4 Terrestrial Wireless Sensor Networks (TWSNs)
Commonly Terrestrial wireless Sensor Networks (TWSNs) comprised of
numerous ranges of inexpensive sensor nodes inaugurated in an identified
topographical region. The arrangement of nodes in the network can be pre planned or
in ad hoc manners. If the network pattern is Ad hoc one so node are fired from plane
to the targeted region. For pre-planned, arrangement four different ways for nodes
installation are used which are Grid, Optimal, 2-D and 3-D classification models [4].
In TWSNs Free Space Optical (FSO) link utilized as fundamental medium
while RF link is used for backup in the absence of Line Of Sight (LOS) for optical
communication. FSO optical communication medium have better results by utilizing
low communication energy, thus FSO links in WSNs are pile. FSO/RF links are
greatly effect by weather and have a close concern with weather conditions, like
snow, fog and rain. For terrestrial applications, the everlasting performance can be
achieved by choosing a specific threshold value in hybrid WSNs and FSO/RF WSNs
and hence we will discover the best power efficient FSO link [4].
1.1.5 Wireless Underground Sensor Networks (WUSNs)
Wireless Underground Sensor Networks (WUSNs) is a particular type of
terrestrial WSNs. The study exhibited that the connectivity practice in WUSNs is
more complex than in terrestrial WSNs and Ah hoc networks, because WUSNS has a
heterogeneous network design and channel feature. To get ride up connectivity issues
a mathematical model was introduce, which have the ability to collect the effect of
environmental factors like soil moisture, soil composition and few system parameters
i.e. the depth of sensor grave, antenna height of the sink, the operating frequency and
acceptable delay of networks [4].
1.1.6 Wireless Visual Sensor Networks (WVSNs)
WVSN is an Application of WSNs, and like WSNs it is also self-configurable,
self-organizing network responsible for image data communication in environment
monitoring. Sensors sense the area, collect data, process and convey the gather data
for further necessary action. Figure 1.3 shows the architecture of WVSNs;
incorporate of user, sensor nodes and sink used for environmental monitoring. Sink
7
node gather data from all nodes through wireless link and forward the dissect data to
user over a network [5].
WVSNs in comparison with other sensor network is quite unique and most
challenging, it producing two dimensional data due to which it require more power
and processing requirements. Power consumption in WVSNs is planned in such a
way that the network remain alive for more time, for this purpose dozen of techniques
are applied. For a single bit transmission dozen of arithmetic operations took place.
Different compression algorithms are used to ensure an elegant trade-off between
image quality and power consumption/energy utilization providing data compression
through Discrete Wavelet Transform (DWT). This technique efficiently utilize energy
and hence prolonged network life span [5].
Figure 1.4: Architecture of Wireless Visual Sensor Networks (WVSNs)
1.1.7 Vehicular Ad hoc and Sensor Networks (VASNETs)
In WSNs the boundaries are comprised of intellectual sensors nodes, also
called motes. Senor nodes are minor in size and have the ability to perform multiple
task, sensor nodes have a transceiver unit use for wireless communication, limited
memory with a processing unit. The researchers tried and successfully advent a
network with the collaboration of WSNs and VANETs called as VASNETs [6].
VANETs is an application of MANETs which differ in few ways like (i)
power constraint: an issue in MANETs but in case of VANETs power is not a
challenge at all due to wonderful battery. (ii) Moving Pattern: in VANETs nodes
move coherently, while in MANETs nodes moment are random (iii) Mobility:
mobility ratio in VANETs is larger than in MANETs [6].
8
Similarity between MANETs and VANETs is that both are self-organizing
and infrastructure less networks. VASNETs is Designed to ensure safety on highways
and propagate emergency situation in the surroundings where GPS is a method for
localization of vehicles. Algorithm used for data dispersion and localization are Track
Detection (TRADE), Optimize Dissemination of Alarm Messages (ODAM), Distance
Defer Time (DDT), Role Based Multicast (RBM) [6]. VASNET ensure safety and
comport for vehicles and driver on highways [7].
1.2 Application Areas:
There are multiple new applications which are becoming apparent in the field of
VANETs considering the enhancement in requirement of latest approaches in
vehicular ad hoc Networks to integrate next generation wireless networks to vehicles
[1,3-4].
VANET applications are categorized into these major groups.
Safety and Warning Applications
General information services and Comfort Applications
Figure 0.5: A schematic of ITS services in VANETs [6]
The basic components required for Vehicular communication are
9
Roadside Units (RSUs):
On Board Units (OBU):
Vehicular Communication Infrastructure VCI
The vehicular communication domains can be categorized as follows
Vehicle to Vehicle (V2V) Communication:
Vehicle to Infrastructure (V2I) or Infrastructure to Vehicle
(I2V) Communication:
Hybrid Vehicle Communication (HVC):
Figure 0.6: Vehicular Communication Infrastructure VCI
VANET's applications are divided into the following main categories:
10
1.2.1 Navigation safety and driver safety application
The main purpose behind VANET deployment is defined as providing a safe driving
environment as well as pleasant driving experience. The main focus of inter vehicle
communication is navigation safety. These applications include warnings about road
problems, traffic sign conflicts, road conditions, assistance in lane-changing, crash
prevention and survivability, and reporting driver’s condition [33]. According to the
research in [34], safety-related applications are classified by the Vehicle Safety
Communications into traffic light conflict warnings, emergency brake lights, pre-
crash sensing, cooperative forward collision warning, and stop sign movement
assistant. Some of these applications require V2V communications, whereas others
necessitate V2I communication.
1.2.2 Emergency routing
These applications include forwarding information during an earthquake,
thunderstorm or other natural disasters when network infrastructure is not able to
work properly to send data [30]. In the case of natural disasters like an earthquake or
a hurricane, the power lines may go down. Therefore the communication
infrastructure will not function properly either because of loss of power or due to the
congestion in the network. VANET is a network that can still operate under these
conditions since it can reconfigure itself to be able to send and receive information.
VANET’s protocols are designed in such a way as to be capable of functioning
without any infrastructure which makes it well suited for emergency situations.
1.2.3 Entertainment and advertisement applications
Entertainment applications include social networking, content sharing, and location-
based roadside advertisement aimed at providing a convenient and pleasant travelling
experience for passengers. In this regard, some content sharing protocols are
introduced, which may be described as follows [33].
Car Torrent is proposed by the UCLA group [35]. This protocol is a content sharing
protocol in WSNs which uses a proximity-based content sharing method instead of
the rarest first piece selection.
Ad Torrent [36] uses network coding for downloading content. This scheme is
based on the idea that downloading from a multi-hop access point or Long-Term
11
Evolution might be time consuming and not practical because of traffic overload.
Therefore, in this scheme downloading from neighbors is proposed. A vehicle will
download any needed piece of information from the nearby vehicles and third parties.
The difference between Car Torrent and Ad Torrent is the dissemination of segments
in Ad Torrent [36].
1.2.4 Monitoring and Tracking
VANET has been used for monitoring traffic conditions and as a
communication infrastructure for transmission of monitoring information gathered for
various applications. Some of these applications include traffic monitoring and
congestion prediction[37, 38], acoustic noise pollution monitoring [39], monitoring of
pollution in urban areas [40], and medical monitoring during disasters when most
network infrastructures are unavailable [41]. All of these applications use VANET as
a framework for transmitting the gathered information due to availability of vehicles
and VANET system in most of the areas. The other surveillance application of
VANET is monitoring and tracking the moving vehicles based on their visual
characteristics. We refer to this application as target tracking using vehicular
networks. The VANET monitoring and tracking system requires vehicles to be
equipped with cameras capable of detecting particular visual features including
license plate, color, accident damage, etc. Our proposed cluster-based VANET
tracking systems [42, 43] may also be used as a framework for monitoring and
reporting of a specific region for a variety of reasons as long as vehicles exist in the
area.
1.3 Distinguishing Features of VANETs:
12
VANETs have certain inborn highlights. Based on these highlights, they stay huge
from other specially appointed systems [3, 4, 10]. For building up a hearty VANET
convention, referenced underneath attributes of VANETs must be considered.
Rapidly changing topology:
Rich resources:
Frequently disconnected network:
Mobility models and prediction of future positions:
Hard delay constraints:
Various traffic environments:
Geographical addresses:
GPS equipped on board sensors:
The network layer of VANETs holds the following types of routing operations [4, 11-
15].
Broadcast
Clustering
Position based
Delay tolerant
Topology - based based
Unicast/Forwarding
Multicast/ Geo-cast
Beaconing
13
Figure 0.7 VANET Infrastructures
Till this point, various steering plans in vehicular impromptu systems with the
future difficulties for improving these conventions have been talked about. Be that as
it may, finding ideal steering approach in urban zones for powerful information
sending, reasonable for ITS applications with improved start to finish QoS is as yet a
standout amongst the most basic prerequisites. In addition, engineering structure of
the VANETs must be engaged while creating VANET steering conventions.
Figure 0.8 Network layer operations in VANETs
14
1.3 Objectives
Main aim is to perform detailed study over current methods for the mentioned
problem and their gaps regarding the research point of view. Afterwards, the
comparison will be held to show the results of proposed algorithm. After comparisons
the best algorithm can be discussed easily. Clustering is considered as the one of NP-
hard problem [16]. The optimal features are making network lifetime longer and
simultaneously reducing the required clusters for the networks. We are using the
meta-heuristic algorithms for solving the mentioned issues. Gray Wolf Optimizer
(GWO) is used for the clustering. This method is extracted by the natural routine of
gray wolves. The crux of this method is to find the suitable pray for the hunt with
respect to their positions.
The use of internet is now becoming compulsory for all type users globally. In V2V,
scalability is primary issue with respect to designers point of view, clustering is an
elucidation for the VANETs [17]. After implementation of GWO, the comparison is
taken with the MOPSO [18], CLPSO [19]. The comparison is held by using the
different features, vehicles direction, transmission range, speed, size of the grid,
clusters formed in the network, number of nodes/vehicles, and neighbors. The
connectivity of the network can be boosted by all such features. Our future procedure
offers an operative methodology to create vehicular clusters in VANETs.
1.4 Approaches
The main approaches that are used to do the comparison with the proposed
framework; short description of those existing algorithms are given below.
1.4.1 MOPSO
MOPSO is one of the main variant scheme of meta-heuristics algorithms known as
PSO [20]. This algorithm was designed by James Kennedy in 1997. The main
concept was taken from the social functioning of the bird flocks or fish packs.
MOPSO is used to get the more than one solution for any problem. This technique
can be used for the maximization and minimization objective. There are many order
of approaches used accordingly to the required objectives. The Pareto approach is
used for making multiple solutions in VANETs clustering. In this approach after
15
extracting the multiple solutions, the fitter one is considered as a result [18]. This is
also the main benefit of MOPSO.
1.4.2 CLPSO
Comprehensive Learning Particle Swarm Optimization (CLPSO) is also the PSO
based technique [19]. In this method, list of parameters (node mobility, transmission
power, direction and speed) are provided to the algorithm for the execution. Weights
are also assigned to the different parameters accordingly. It is considered as the
effective and efficient method to solve the different problems.
1.4.3 Moth Flame Optimizer (MFO)
Moth flame belongs to family of butterflies having a variety of species. Moth flames
follow the moon light to travel in straight lines using mechanism of transverse
orientation. This aids them to fly their entire journey in same angle [25, 26] as shown
in Figure 2. Yellow line highlights the flying direction of moth towards the moon
while red indicates the straight surface from which moth will fly. Green circle is used
to display the angle of elevation made by the moth taken from surface and its flying
direction. Transverse orientation endorses the movement of moth flames in sustaining
the consistent angle. It is observed that flying pattern of moth flames varies during the
journey towards the artificial lights. Consequently, moth flames process accuracy for
the far-off distances but fails its technique of transverse orientation for near locations.
The key contribution of the this work is summed up as follows;
The main contribution is to provide a new framework for the clustering in
VANET domain. The framework is completely based on the MFO.
It is used to handle the optimization problem, for that various weights are
designated to the user requirements.
Some of the limitations are also used for making it valid in MFO.
Each step of the proposed method is also modeled mathematically.
In the last, evaluation is also done by making the comparison with CLPSO,
CACONET and MOPSO to show the optimal solution.
16
1.6 Scope of the Thesis
Very limited clustering based algorithms exist in VANETs. Some of these
techniques lie on the meta-heuristics. This gap became motivation for conducting
research in respective area. The meta-heuristics can be implemented in ITS for
solving the clustering problem. The main issue that arises here is how to cluster the
nodes/vehicles. This clustering must maintain the mobility, routing, data transmission
and connectivity of nodes/vehicles. This concept is used in the intelligent
transportation system.
Life time of a network is also important for the nodes so that the continuous
dissemination of data can occur. In wireless networks, it becomes more difficult to
continue the life of networks, due to the different parameters such as; battery power,
transmission range and mobility.
In VANETs, scalability is also an issue to be addressed. Due to low scalability
of networks, it creates a research gap for the researchers to be tackled. According to
some authors or researchers, one of the best solution for solving the scalability issue
in network or increasing the life time [7, 8]. Through clustering, the load of the
network can be equalized easily and the distribution of resources will be more
efficient.
CHAPTER 2
Literature Review
2.1 Related work on VANETs
In paper [34], authors had shown efficiency and reliability of abiding geo-cast
for the scenario of VANETs, which continually disseminate information in particular
time interval in given area. Authors demonstrated a demarcation among already
present scheme and with the scheme designed by them. In case of conventional
broadcast techniques, during each cycle the broadcast should be done in multi hope
way for all the entire destination area, resulting substantial network overhead.
Moreover, reliability was not ensure in case of poor link quality. Authors proposed
technique of, Abiding Geo-Cast Protocol Based on Carrier Sets (AG-CS) fully based
on stability estimation index. Abiding messages can be received via one hope
delivery by disclosed vehicles passing over in carrier sets when they needed
messages, the procedure not only forbid significant overhead build up by multi-hop
broadcast, also increase probability of receiving. Author’s simulation result reveal
that their designed schemes were more efficient and valid.
In [35], experimenters presented that in, VANETs scenario safety messages
propagating wirelessly among V2I and V2V ensuring ITS. For better results
transmission and reception of alert messages in VANETs network, all participating
nodes should be in contact properly. Authors observed density of vehicles “ρ”, and
communication range “R” of vehicles and showed a relationship with connectivity
probability. Where, “Sd” was minimum safety margin between contiguous nodes and
named as connectivity probability. The end results showed that a minor fluctuation in
“Sd” results significant alteration in connectivity of the network. So therefore, in
designing process “Sd” should be planned properly.
In paper [36], research worker showed propagation of emergency alerts in
VANETs were of greater importance to reduce traffic complexities in tomorrow ITS.
But, in case of urban expressways, for accurate data disseminations efficient
algorithms design was difficult due to complex road nature, but essential for better
Quality of Service (QoS). The authors suggested an algorithm named Fast and
Reliable Warning message Dissemination (FRWD) protocol, which guaranteed
messages delivery of any crisis event in a particular locality (entrance/exit) on
motorways for a certain interval of time. Results obtained from simulations of the
authors suggested technique, revealed lower delay and higher reliability index.
In [37], researchers proposed an algorithm named Low Delay Forwarding
with Multiple Candidates (LDMC), a newel geographic routing scheme. In LDMC,
forwarders were chosen through sender, and receiver selects multiple rivals. Selection
procedure for candidate’s placement was based on its information of position and
speed. The authors designed scheme showed advancement in forwarding delay, and
the technique was best suited for applications which were delay sensitive, suchlike
cooperative positioning or coordinated driving.
In paper [38], researchers designed novel hybrid technique to minimize delay
in VANETs. Primary aim of the D2D technique, among vehicular nodes were to
eliminate contention delay and also workable for longer distance. In proposed hybrid
system D2D links were managed through cellular base stations in the covered
technique. Vehicular nodes checkout its packets lifespan instantly and forward a
message to BS for routes establishment for D2D if required.
The BS faced problem in optimal resource allocation in choosing optimal
destination node, to setup D2D link connection and also assign appropriate links for
them, to guarantee minimal latency. The suggested greedy based mechanism were
found an effective scheme shown via different simulations environments.
In paper [39], authors described the operation of multichannel Medium
Access Control (MAC) technique through using revised analytical model as well as
simulation results. Authors designed a navel technique on the bases of hypothetical
results, which enhanced throughput and reduced packets delay. According to authors
that with increasing data retry limit, the data rate increases and delay in service packet
decreases. According to [40], in VANETs scenarios data routing was done through
efficient protocols. Authors examined that VANETs had no infrastructure with
deficiency of trusted nodes and high mobility of vehicular nodes required proficient
protocols for data disseminations among vehicular nodes, in order to ensure network
security and reliability of the constituent nodes. In order to achieve reliability,
minimum routing delay authors proposed trust based routing protocol scheme
applying fuzzy logic.
In paper [41], authors had shown their proposed technique a taxi based
broadcasting algorithm, named TaxiCast, Taxis act like an advertising source,
disseminating multimedia advertisements to the vehicles in its neighborhood. Authors
had studied already existing vehicular advertisement methods which was best in line
of sight scenarios, but when barrier comes in way the efficiency decaying quickly.
Further, Wireless means were used for advertisement dissemination in VANETs.
However, with the passage of time decomposition in advertisements were
observed, so fast delivery was necessary for better performance. The proposed
TaxiCast performance was best in resolving contradiction among bounded
communication capability and enormous sized data. The designed scheme followed
coding and decoding of information to accomplish contiguous vehicles requisites.
The researchers performed simulations for fixed reward as well as for decayed reward
processes, and examined that their designed technique had more efficiency rather than
already available techniques. Related work on VANETs shown in table 2.1 bellow.
Table 2.1: Summary of Literature Review on VANETs
Protocol Method Domain Parameters
Tackled
Shortages
Carrier set [34] Abiding Geo-
cast
Based on
carrier set
(AG-CS)
VANETs Avoid overhead,
enhance probability
of reception,
reliability
enhancement
One dimensional
problem
Connectivity
probability model
[35]
Minimum
safety distance
(Sd)
VANETs Improved network
connectivity,
successful
propagation of
safety messages
Network
connectivity
Fast and Reliable
Warning Message
Dissemination
(FRWD) [36]
Sender
oriented
broadcast
method
VANETs Low latency and
high reliability
Sender oriented
multi-hope broad
casting, Time
sensitive, Specific
area
Low Delay
Forwarding with
Multiple
Candidates
(LDMC) [37]
Geographic
routing
protocol
VANETs
High packet
delivery, best for
delay sensitive,
improvement in
forwarding delay
Destination
position, location
service, prefer
nodes close to
intersection
Hybrid device to
device (D2D) and
IEEE 802.11p [38]
Cellular base
station
VANETs Improved delay
performance,
removed contention
delay, support
longer distance
Interference, link
selection and
channel
assignment
Multi-channel
Medium Access
Control (MAC)
Revised
analytical
model with
VANETs Improved
throughput and
packet delay, less
Limited retry best
up-to 7, longer
single packet
[39] limited retry retransmission delay
Trust based
routing protocol
[40]
Fuzzy logic VANETs Effective end to end
latency, overall
network efficiency
improved
Further routing
improvement
Taxi based multi-
media
advertisement
broadcasting
scenario [41]
TaxiCast VANETs Efficient utilization
of limited channel
capacity and big
multimedia data
size
First come first
serve based
broadcast scheme,
end to end delay
2.2 Related Work on VANETs Security In paper [42], authors presented an authentication mechanism for secure
message transmission for VANETs. Researchers shown that the existing techniques
were based on combined signature technique and because of which, RSU sometime
transmitted fake authenticated messages. Hence, the authors proposed a cumulative
message authentication code technique which verifies the integrity and authenticity of
messages. Pseudo RSU’s were mounted in locality of RSU to restrict false
information dissemination, to ensure exchange of rectified authenticated messages.
Their results were obtained from simulations and security parameters which reduced
considerably the communication overhead and enhanced validity of disseminated
information.
In [43], the researchers invented procedure for security aspects of V2V
communication employing Radio Frequency (RF) transceiver. Main part of VANETs
is position-based information of vehicular node. The use of RF transceiver improved
the trust on received data about vehicles location. Motive was to found a vehicular
communication system with minimum cost, effective data distribution, and to confirm
passenger’s protection, safety, and relaxation ability. RF transceiver verifies reported
information in network, also approves position of the malicious vehicle, and hence
ensured security of the network. The scheme enhanced VANETs security through
prohibiting malicious entities from penetration, hence reduced the chances of invalid
data about position information of vehicles.
Researchers in [44] designed a technique of detection, Greedy Detection for
VANETs (GDVANs), to reduce greedy conduct frequency. The motive was to assure
road and passenger safety and enhance transportation quality. The authors proposed
technique incorporated of two phases, suspicion phase and decision phase. The
advantages of designed scheme were, the nodes had the capacity of execution and no
change was required in standard IEEE 802.11p protocols at any stage. The scheme
had the ability of greedy behavior type threats detection and listing of potentially
compromised nodes, with newly defined metrics.
In paper [45], authors established that VANETs had the opportunity of safe
wireless communications with threats avoidance, but still, security threats were a
disputing task, like confidentiality, data integrity, nonrepudiation, and data privacy.
The suggested model was regarding VANETs protecting against threats, Attack
Resistant Trust Management (ART) algorithm. ART had not only detection capability
of malicious nodes, but also the ability to deal with attacks. ART judged the
trustworthiness of data and mobile nodes together. Assessment of data trust was done
on the basis of sensed and collected data from various vehicles; judgment of node
trust was done on two means i.e., functional and endorsement. Also, the scheme ART
had broad applications to enhanced traffic experimentation in terms of secure
mobility, with reinforced reliance.
Scientist in [46] developed a theory to assure security inside educational
institutions, medical institutions/health care centers, residential places, etc., to prevent
careless driving. In the model, entry and exit points (gates) were defined. Authors
suggested wireless hardware type “GPS” arrangement to supervise moving vehicles,
velocity and region of entry. At entryway orthodox vehicle obtain device from guards
on duty and return device back on exit way to authorized guard on duty. When the
device activated, it set up a communication path among security depot and driver
inside the specified region. For vehicles inside particular region had speed threshold,
on crossing threshold, warning messages were disseminated. In the depot, receiving
unit holds previous record of derived vehicle about each individual drive separately,
and in terms of any misconduct penalizing action was taken. In paper [47], authors
considered VANETs as a complicated network, in which all vehicular nodes
movement was random. In VANETs nodes position changes, hence data
dissemination was a problem; also new links creation took place each time for data
packet transmission. An attack could wind up all communications running amongst
nodes. In this research, authors conferred impacts of Sybil attempts on VANETs
communication schemes. Also, they examined and scrutinized variety of VANETs
routing hierarchies, and found AODV routing scheme results more efficient in term
of attacks launched in VANETs fencing.
Authors in [48], exhibited that as vehicles in VANETs move with maximum
acceleration and network topology dynamically changes, makes it hard to wipe out
false invalid nodes totally and ensure safe dispersion of data among nodes. Hence,
writers illustrated different security threats to VANETs and pointed out possible
remedy algorithms to mitigate those attacks. They categorized defensive mechanisms
and examined them on dissimilar performance point of view. Ultimately, research
workers had found different research subjects based upon VANETs security threats,
and impelled scientists to work and discover an efficient method to resolve attacks.
Paper [49] presented detection problem of DOS attacks existing in VANETs.
The contribution was conceptualizing a new security model based on games pattern
for DOS attacks. Secondly, researchers expressed two conditions about games theory,
strategic and extensive type games. Thirdly authors studied DOS attacks on the basis
of practical suppositions, utilizing the actual mobility models based on actual map.
Finally authors analyzed their designed model and stated about their contribution in
research that no such type of game related model was designed earlier. In paper [50],
researchers had shown that with the growth of security techniques in VANETs,
threats are also growing relatively. They proposed a trust based management
algorithm called Threshold Adaptive Control Technique, to detect malicious and
selfish nodes, and they fixed themselves inside the network intelligently. Authors
showed that previous detection techniques were failed to some extent in detecting
these malicious nodes. Authors had designed an adaptive detection threshold
technique, which motivate the attackers to act well and finally the designed technique
catches the malicious behavior and hence abled to detected the malicious nodes
immediately.
In Paper [51], researchers had shown that only authentication of nodes was
not enough for secure data transmission in VANETs network, because sometime even
authentic nodes disseminated fake information and on/off attacks lead to network
applications threatened. To avoid such threats and attacks authors presented a
technique called logistic trust mechanism, to detect and identify the malicious false
messages. The proposed scheme identifies the correct event first through information
collected from trusted sources and also from the receiver observations itself. On the
basis of this information the behavior of the nodes was identified through receiver
own observation which was complemented by the opinion from other nodes. The
scheme had 99% accuracy in detection of malicious nodes, which shows the efficacy
of the technique.
Table 2.2: Summary of Literature Review on VANETs Security
2.3 Clustering Technique for Target Tracking in VANETs
Many grouping methods have been advanced for checking and following in
WSN and MANET [60-62]. As referenced in segment 2.2, different properties of
MANET and WSN make their calculations non-pertinent to VANETs. The grouping
structure required for following a moving target vehicle modifies from other
applications. Correspondingly the grouping measurements and bunch heads (CH)
determination criteria vary from different applications. For instance, in group based
directing calculations, the bunch is made on development closeness of hubs yet in
target following application every one of the measurements are characterized based
on target's development design. For example, transition similarities among the nodes
and its target ought to be employed for determining CH choice and cluster
association. Real reason behind target following is that the hubs which can identify
the objective can pick up data about target and do not forget about target. Such hubs
join a bunch which moves alongside the objective. The part hubs send their data to
the CH as opposed to sending to focal substance. The CH must be a hub having most
comparative development example to the objective so it can follow the objective for
greatest time interim. Thusly, all hubs should contrast their development design with
target and the most suitable hub ought to be chosen as CH.
Figure 0.1: Clustering Mechanism for Target Tracking
2.4 VANET Clustering Algorithms
Most basic versatility measurements incorporate relative speed and separation
between two vehicles. Some different conventions utilize relative speeding up which
makes the convention increasingly material to genuine situations. There are other
group participation factors like bundle transmission delay, got flag quality, and
connection termination time that can be utilized dependent on convention
prerequisites. Here we clarify some grouping calculations utilized for VANET
conditions. We have arranged the calculations dependent on their bunch head choice
criteria. In Table 1, Qualities of Cluster-Based VANET Algorithms the bunch
participation rules are recorded and can be a classification highlight for the
calculations. Most conventions send comparative portability highlights to analyze
versatile hubs. Notwithstanding, the determined portability metric and bunch
participation principles and CH determination rules are among the distinctive
highlights of the conventions. The portability highlights utilized by the majority of
the calculations to ascertain their CH determination metric incorporate separation and
relative speed. A few calculations consider speeding up in their system and the result
is progressively down to earth and pertinent genuine conventions as referred to prior.
In this segment, we have thought about these components for ordering the
calculations and have arranged them dependent on their CH determination criteria as
pursue:
2.4.1 Total Forces (calculated based on distance, direction and relative
velocity)
Maglaras et al. [67] put forward a clustering algorithm for vehicular networks
called spring clustering (Sp-Cl). The main idea behind Sp-Cl algorithm is to use
forces as the mobility metric between nodes and the basis of cluster creation and CH
selection. These forces are calculated based on relative mobility and distance among
two pairs of nodes and determine whether two nodes are eligible to join the same
cluster. The negativity or positivity of forces is based on the movement direction of
vehicles. Two nodes apply positive force to each other if they move in the similar
direction and negative forces if they are driving in the opposite direction. Nodes
moving in the opposite direction are not supposed to be in the same cluster. The
distance, movement direction, and relative speed of nodes, are the parameters used to
estimate the force between each pair of nodes. If the total forces applied to a vehicle
are negative, it is not considered a candidate cluster member candidate. Negative
value of total forces of a vehicle shows that all other nodes are moving away from it.
The total amount of forces applied to each node along the x-axis and y-axis is used as
CH selection metric. This value is referred to as "suitability value" and is calculated
based on neighbor nodes' mobility and distance information. A stable node is a node
with a movement pattern most similar to the nodes in its neighborhood. The most
stable node in the cluster is elected as CH. In case a CM's total forces value exceeds
its CH, the CM will leave its cluster and becomes a CH for the new cluster. Further, if
two CHs meet each other, their clusters merge and the CH with the highest value
takes over the CH duty. In order to select the most appropriate CH, a prediction-based
parameter is used to evaluate the driver's behavior. As mentioned in [63] vehicles that
keep a predictable movement pattern or stay at almost the same speed, are more
eligible to be selected as CH. A vehicle node with more stable movement patterns
may be detected by predicting its future behavior based on its previous driving
patterns.
The experimental result of the Sp-Cl shows a better performance of the
algorithm in comparison to Low-ID [67] method which is a MANET clustering
protocol. The average number of cluster changes is calculated for different
transmission ranges and various densities. The change rate increases as the
transmission range decreases. However, cluster change rate per node in Sp-Cl is less
than in the Low-ID algorithm. Furthermore, the average number of created clusters
increases by decreasing the transmission range. Still, the average number of clusters
formed in Sp-Cl algorithm is less than Low-ID. Besides, the average cluster lifetime
of Sp-Cl is higher than Low-ID and is decreased when the transmission range is
decreased.
2.4.2 Velocity Difference
In some clustering algorithms, the cluster membership metric is not calculated
based on distance or relative speed between nodes, but the received signal strength,
and packet delivery delay, which are useful metrics in multi-hop clustering scenarios.
Ahizoune et al. proposed clustering algorithm for VANETs (SBCA) [64] based on
stability. In SBCA, cluster membership is based on the strength of received signal
from the CH. However, the CH is chosen based on velocity difference between a
node and its neighbors. In this paper, the idea of selecting a secondary CH (SCH) to
take over the charge in case of loss of the primary CH (PCH) is advanced. Selection
of a secondary CH (SCH) helps in forming more stable clusters, and reduces the
overhead of re-clustering in case of losing the primary CH with less overhead. The
PCH selects the SCH at each time interval based on velocity and distance difference
of nodes compared to PCH. A mobility prediction method based on driver's behavior
is used on the PCH node to predict the time it will exit the cluster. This prediction
technique helps in informing the SCH to be ready to take up the PCH role when the
time comes. In this algorithm, the PCH is the central entity which makes all the
clustering decisions. This feature prevents re-clustering when the CH is altered.
Therefore, when the change occurs, the cluster structure remains stable and the
member nodes are informed about the new selected CH. To shed more light, in some
clustering algorithms, member nodes join a CH instead of a cluster. So, each time the
CH changes, the cluster should be formed again. The simulation results presented by
the authors show a better performance of SBCA in comparison to CCP [68]. By
increasing the density in the network, average cluster lifetime is increased. However,
overhead also increases as a result of increased density, which is due to more message
exchange between nodes. A drawback in the design of SBCA which makes it non-
applicable to real-world scenarios is a lack of rules for opposite direction vehicles
because it has been assumed that vehicles are moving in exactly similar direction on a
highway.
2.4.2.1 Network Criticality (based on Link Expiration Time (LET))
Li et al. put forward an algorithm called criticality-based algorithm (CCA) to
use local network criticality as basic metrics for clustering. Network criticality is a
global metric which demonstrates sensitivity of a network graph to topological
changes in the network. It has been argued in [69] that the idea of network criticality
is derived from the concept of “Random Walk Between-ness” of a node. Random
walk between-ness is the total number of times a node "k" is met when information is
sent from a specific source to a specific destination. The value of criticality in the
network is calculated as the normalized average number of random walk between-
ness of a node. The lower value of network criticality shows less sensitivity to
network changes. The value of network criticality for a node pair is calculated as
point-to-point network criticality which calculates the total commute time between
the node pair and illustrates the sensitivity of nodes to topology changes. Another
value called localized criticality of a node is determined by considering all the paths
between a node i and all its neighbors. Local network criticality shows robustness of a
node and its suitability to be the CH. The weight matrix is required to calculate
network criticality of a node pair. Therefore, link expiration time (LET) is introduced
as a mobility metric which is used to assign weight to network graph. LET represents
the amount of time two nodes stay connected to each other. LET value is a prediction-
based value calculated based on current information of nodes and assuming the same
pattern for the next time intervals. As mentioned in section 2.6 prediction improves
clustering performance in VANET environment, if the prediction intervals are
assigned properly. The simulation results reveal that the changes on average number
of clusters and average cluster size in CCA are less than MDMAC protocol.
Furthermore, CH changes and member changes in CCA are less than MDMAC [70],
which indicates a better performance of CCA algorithm compared to MDMAC
algorithm. It is noteworthy that CCA and MDMAC are implemented as 1-hop and 2-
hop algorithms. The results represent less CH and CM changes in multi-hop clusters.
2.4.2.2 Spatial Dependency (based on distance, relative velocity, and relative
acceleration)
Considering acceleration as a mobility parameter in the algorithm helps in
designing more realistic scenarios. The algorithms proposed in [65] and [71] consider
acceleration in their mobility metric calculations. Dynamic clustering algorithm
(DCA) proposed by Fan et al. in [65] takes acceleration value of nodes into account
for protocol design. The mobility metric used in DCA algorithm is called Spatial
Dependency (SD) which demonstrates movement similarity between two neighbor
nodes. The mobility parameters used in the SD calculation are distance, velocity, and
acceleration. The mobility value of each node in the cluster is calculated as the
normalized total SD value of the node with all its neighbors. This value is called
cluster relation (CR). The main characteristics of DCA algorithm as compared to
lowest-ID and Max Degree protocols include high cluster stability, and longer cluster
head life time when the transmission range of vehicles are increased.
2.4.2.3 Fuzzy Logic System (based on distance, relative speed and acceleration)
The authors in [71] assert that some factors of VANET systems such as
driver's behavior and inter vehicle distance are not predictable. Therefore they use
fuzzy logic to handle this situation. A learning mechanism is implemented to make
more precise predictions based on the driver’s behavior. Using prediction in
clustering approaches improves performance of the algorithm mostly in highly mobile
scenarios such as VANETs. The most important aspect of using prediction is to
decrease control messages overhead of cluster by reducing the number of required
communication messages to establish and maintain cluster structure. In some cases
the mobility metric is also calculated based on prediction and the decisions are made
based on future behavior of nodes which is quiet beneficial in VANET's dynamic
environment. In this system the membership functions of fuzzy system are defined as:
inter distance, relative speed, and acceleration functions. A Control Channel Interval
(CCI) is used as synchronization time period. Vehicles connect to control channel and
send their safety messages in this period. At every CCI, vehicles receive information
about their neighbors and calculate a value called "Stabilization Factor" (SF). SF
selects optimal cluster head in the cluster. The evaluation results clearly gives a better
performance of proposed fuzzy-based algorithm compared to APROVE [72] and
CMCP [68] in terms of average CH and CM lifetime and average cluster size.
Furthermore, the impact of increasing the vehicle density in the network and
increasing the prediction time interval on the protocol performance is studied in this
paper. The results demonstrate improvement in the average CH lifetime, average
cluster size, and average CM lifetime when vehicle density in the network is
increased. This is because of the reduction of inter vehicle distance and re-election of
previous CHs. Additionally, the accuracy of the algorithm degrades slightly due to the
increase of the prediction time interval. The reason for low changes is the learning
mechanism in the algorithm which allows the protocol to adapt to driver's behavior.
Another fuzzy logic based clustering protocol is proposed in [73] for visual
touristic guide on vehicles. This system can help tourists watch videos of touristic
areas around them based on their interests. This algorithm considers vehicles location,
velocity, movement direction, and user interest as clustering metrics. A value called
cluster head eligibility or CHE is calculated by their proposed fuzzy logic controller
for each vehicle and is broadcasted in the network to select the most eligible CH. The
CHE value is calculated by fuzzy logic controller based on the following inputs:
average velocity, average distance, and average compatibility which is related to
interest and is calculated based on a factor called interest vector.
2.4.2.4 Packet Transmission Delay
Most of the proposed algorithms can work properly under 1-hop cluster size;
however, designing multi-hop clustering protocols is challenging and requires
profound scrutiny and analysis of clustering features to assure performance in large
clusters (multi-hop). A multi-hop clustering approach is proposed by Zhang et al. in
[74] Packet transmission delay is used as mobility metric in this algorithm. The
aggregate mobility which is the basis of CH selection is calculated by using relative
mobility of vehicles. This idea helps in increasing cluster stability. The most common
metrics used to calculate relative mobility between nodes in VANETs are relative
speed, distance, and signal strength. As mentioned in [74], these metrics are not
helpful in multi-hop clustering scenarios. The main reason is fading effects caused by
obstacles between vehicles. Therefore, using packet transmission delay as clustering
metric is a beneficial idea mostly in multi-hop clusters. The proposed protocol has
been evaluated under two, three, and five hop scenarios on freeway mobility and
Manhattan mobility models. The results show that CH duration is higher in freeway
scenarios because of strong connection between vehicles and less mobility compared
to city scenarios. Also, by increasing the maximum allowed speed in the network, the
CH and CM lifetime in both scenarios are decreased. However, increasing the number
of hops has positive impact and increases CH and CM lifetime in all scenarios.
Similarity Function Based on Euclidean Distance in some VANET clustering
algorithms such as [72] statistical approaches are used to calculate mobility metrics
between vehicles. In this paper, a distributed mobility metric based on a statistical
approach called affinity propagation is proposed in order to increase cluster stability.
Cluster stability is defined as high CH and CM lifetime and lower CH change rate.
The concept of affinity propagation is referred to as a clustering technique used in
data mining and statistics. In this approach data points (nodes) send values to each
other by messages. The transferred values include availability and responsibility of
each data point. In each cluster, an exemplar is selected to be the representative of the
cluster. A similarity function is defined to show suitability of a node to function as the
cluster exemplar. In this algorithm, the concept of affinity propagation is applied for
clustering in VANETs. The proposed algorithm is called Affinity PROpagation for
vehicular networks or APROVE [72]. The basic features of this algorithm include
distributed function of the algorithm and stability of clusters due to using appropriate
mobility metric for similarity function calculation. Besides, the idea of predicting the
future position of nodes based on their current position and velocity is used in
similarity function calculation of APROVE algorithm. Consideration of future
distance requires using prediction-based on current velocity. Another parameter used
in similarity function calculation of nodes is self-similarity. The appropriate CH is
selected based on similarity function of nodes. Evaluation of APROVE protocol was
performed under various prediction intervals and maximum speeds. The results show
that performance decreases by increasing the speed. Also, the optimal prediction
interval is estimated to be 30 seconds in this algorithm which is a reasonable time
interval for a very dynamic network. Furthermore, the results show superior
performance of APROVE compared to MOBIC in terms of CH and CM lifetime and
cluster change rate. However, MOBIC creates fewer clusters in the network in all the
scenarios compared to APROVE. The problem with APROVE is the long
convergence time due to the need for exchanging all the affinity messages. Also, the
CH selection algorithm should run any time the timer expires, which causes high
overhead.
2.4.2.5 First Deceleration Wins (FDW)
Cluster management in VANETs requires a number of messages to be
switched periodically to obtain a comprehensive knowledge of the network. It would
be very helpful to lessen the number of communication messages in such a vast and
dynamic network. Passive Clustering (PC) is projected by Gerla et al. to reduce the
overhead triggered by exchanging periodic beacon messages to gain information
about neighbor nodes and avoid cluster initialization phase [75]. The principal point
of PC is to send essential clustering information in data packets. If there is no data
packet ready to be delivered, the delivery of clustering information will be postponed.
Wang et al. propose three different PC techniques called VPCs to use for VANET
routing purpose [76]. The proposed algorithms use passive cluster-based techniques
for VANET environment. PC algorithm [75] uses FDW method to select the CH, in
which the first ready node to be the CH, is selected as CH. VPC algorithms use the
same technique to elect the first CH in the cluster formation phase. However, the
random selection of CH and GW nodes is combined with some weight based methods
to assign priority to nodes. The distinction point of the three proposed algorithms is
the CH election metric i.e. vehicles density, link quality and link sustainability
respectively used in VPC1, VPC2, and VPC3. Vehicle density is calculated by
counting the number of reply messages each node receives from its neighbors after
sending an advertisement message and is used in VPC1 algorithm. A node with more
neighbors is suitable to be the CH. The link quality metric which is used in VPC2
algorithm is represented as reliability level of links. Expected Transmission Count
(ETX) is used to show reliability and high quality of links and depicts the bi-
directional transmission quality of a link. The other metric used for VPC3 is called
link sustainability. The connection time between two vehicles is used for evaluation
of sustainability of a routing path. This metric is called "Link Expiration Time" or
LET because it relies on the current status of nodes and determines the future
behavior to make clustering decisions.
2.4.2.6 Connectivity Degree (based on distance and relative speed)
Rawshdeh et al. propose a Threshold Based (TB) clustering algorithm in [66].
In TB, identification of candidate cluster members is made by using the degree of
speed difference. The neighbor nodes are classified into stable neighbors (SN) and
unstable neighbors. SNs are supposed to be candidate cluster members. Candidate
cluster members move in the same direction and have more similar speed. The
probability density function for speed of each vehicle is estimated to find the
probability, that relative speed of two vehicles are in a defined threshold or not. The
nodes which maintain their relative speed in the threshold are assumed to be
appropriate candidate cluster members. The suitability function is used to verify
eligibility of a node to be CH. To calculate the suitability function, a parameter called
connectivity degree should be defined. The nodes with closer distance are supposed
to have higher connectivity degree and are more probable to become CH.
2.4.3 Node ID as weight value
Modified DMAC (distributed and mobility-adaptive clustering) protocol is
proposed in [17] to make DMAC protocol appropriate for VANET environment.
DMAC [77] is a general clustering protocol for mobile environments and this feature
makes it less beneficial for VANET’s highly dynamic nature. Specific features of
MDMAC algorithm are mentioned as: avoiding to add nodes with short connectivity
time to the cluster, avoiding to add opposite direction nodes compared to cluster's
movement direction. The proposed algorithm uses the idea of weight based clustering
in which the weights of nodes are assigned based on their ID and node connectivity.
The cluster membership rule of MDMAC is based on prediction of connection time
of nodes. This value is referred to as freshness and is an estimated value based on the
current distance and velocity of nodes. MDMAC algorithm contradicts with some of
the DMAC algorithm properties as cited in [70]. MDMAC is a multi-hop clustering
algorithm and nodes can be n-hops far from CH. MDMAC helps in creating more
stable clusters with fewer changes compared to DMAC. However, the overhead of
MDMAC is higher due to its connectivity time estimation property, which requires
more messages passing between nodes.
2.4.4 MANET Clustering Algorithms
The main approaches used in VANET clustering algorithms are derived from
MANET protocols. As explained in section 2.2, MANET protocols are not
appropriate to be used in VANET environment due to their different characteristics
and features. However, adjusting MANET algorithms and considering vehicular ad
hoc Networks (VANAET)’s characteristics in the design procedure can be used as
methods to implement clustering algorithms suitable for VANET. Some of the most
popular MANET clustering protocols include MOBIC [78] and lowest-ID. In this
chapter, some of the most popular MANET clustering algorithms have been reviewed
briefly.
Lowest-ID is a 2-hop clustering scheme proposed by Gerla et al. for MANETs
[67]. This is a simple clustering approach which uses the ID of nodes as the only
clustering metric. Lowest-ID does not consider mobility of a vehicle in CH selection
decisions. Nodes are supposed to broadcast messages to their neighbors in order to
exchange clustering information. A node with least ID among all neighbors is picked
up as CH. The CH only receives messages from nodes which have higher ID than
itself. Any node which receives messages from more than one CH is a gateway (GW)
node and other nodes are ordinary members.
MOBIC extends the concept of MANET clustering by considering the idea of
relative mobility between nodes [78]. The main idea behind MOBIC is to compare
nodes with their neighbors based on their mobility metrics and to add them to
appropriate clusters. A node with lowest relative mobility compared with its
neighbors is selected as CH. A CH with high relative mobility compared to its
neighbors results in poor cluster stability. The mobility metric proposed in MOBIC
does not require location information about nodes.
MOBIC is a weight based and one hop clustering protocol. The clustering
scheme used for MOBIC is similar to lowest-ID algorithm [67]. A notable property of
MOBIC includes the merging process of two clusters. When two CHs meet, the
merging time is postponed for CCI time interval. The CCI or cluster contention
interval is introduced as a waiting time for cluster merging process. After this waiting
time, if two CHs are still in each other's range, their clusters are supposed to merge
and the one with lowest-ID takes over the CH responsibility. The evaluation results
represent a better performance of MOBIC in terms of CH changes because of using
relative mobility instead of node ID.
As mentioned earlier, PC is an advantageous technique to reduce control
overhead in clustering algorithms. There exists a considerable number of passive
clustering algorithms for wireless networks such as MANETs including FWD [75],
GRIDS [79], EFPC [80], EAPC [81], PCBRP [82], and KHPCBRP [83].
The idea of PC for wireless ad hoc networks was proposed by Gerla et al. in
[75].Cluster stability and faster convergence are the benefits of PC algorithm. A novel
CH selection technique called FDW is proposed in [75]. FDW suggests selection of
the first ready node as CH instead of using weight based methods. The network
activity and clustering state of a node represents its readiness as a CH. The selected
CH might not be the best eligible CH based on application requirements; but, it is
selected faster than weight based methods. However, the CH lifetime, which is an
important stability metric, can be affected adversely.
GRIDS [79] is an energy-aware PC protocol which uses periodic polling and
geographical repulsion. The CH and Gateway (GW) nodes selection criteria depend
on energy levels of nodes. The CH nodes do not change frequently unless there is a
CH collision which is entering the 1-hop neighborhood of another CH.
Rangaswamy et al. proposed a passive clustering algorithm for MANETs
which is called PCBRP [82]. PCBRP is a multi-hop (max 2-hops) algorithm and the
cluster formation is based on node proximity. The clusters consist of three node states
including CH, GW, and ordinary nodes. The ordinary nodes are not supposed to
broadcast any messages and the CH and GW nodes are the critical cluster nodes.
Among various nodes competing for CH state, a node with lowest-ID takes the
responsibility.
Table 0.3: Clustering Protocols
Protocol CH selection
metric Clustering
metric Stability
features Other
features Cluster
size Simulation
Environment
SP-CI [63] Total Forces
(Distance,
Direction,
relative speed)
Force based
(Distance,
Direction,
relative speed)
The lowest
mobile and
most
predictable
nodes become
CH Same
direction nodes
join cluster
Distributed - Highway Direction
DCA [65] Spatial
Dependency
(SD) (Distance,
relative velocity, relative
acceleration)
Spatial
Dependency
(SD) (Distance,
relative
velocity,
relative
acceleration)
Same direction
nodes join
cluster
Distributed No
prediction
- -
SBCA [64] PCH: velocity
difference SCH: Distance,
relative speed
Received signal
strength of two
consecutive
beacon
messages
Secondary CH, Prediction of
CH lifetime
Centralized Prediction
of
expiration
time of PCH
- Highway (4 lane) All vehicles are same direction
Fuzzy
Logic [71] Fuzzy logic rules Distance, speed,
acceleration
No clustering
metric
mentioned
Prediction of
speed and
position
Prediction-
based CH
selection
- highway (one directional,
4 lane)
Multi-hop
[74] Aggregate
relative mobility
based on
transmission
delay
Relative
mobility based
on
Using
transmission
delay to
overcome
fading effect in
multi-hop
scenarios
Distributed Multi-
hop Freeway mobility and
Manhattan mobility
model
APROVE
[72] Affinity
Propagation
Messages
Similarity
Function based
on current and
future Euclidean Distance
between nodes
Distance
prediction Distributed 1-hop Highway
DCTT [42]
TFP (Tracking
Failure
Probability)
based on relative
velocity and
distance
Target detection
and distance
from the target
Cluster member
level TFP threshold Same direction
nodes join
cluster
Distributed Direction-
based
Multi-
hop Multi-lane highway
PCTT [43]
OBT
(Observation
Time)
Target detection
and distance
from the target
Prediction-
based CH
selection metric Prediction-
based cluster
maintenance Same direction
nodes join
cluster Cluster member
level Resign Timer to
increase CH
lifetime
Candidate
cluster head
selection
Centralized Direction-
based
Multi-
hop Multi-lane highway
CCA [69] Localized
network
criticality of a
nodes
Node pair
network
criticality
Prediction-
based
calculation of
LET
1-hop
and
2hop
-
A multi-hop PC algorithm for MANET environment called KHPCBRP [83] is
based on CBRP [84] and simulation results show better performance of KHPCBRP in
comparison to CBRP in terms of overhead. The algorithm has been tested under 2-
hop and 3-hop scenarios and in both cases the overhead is reduced. The concept of
prepared CH (PCH) is proposed to reduce re-clustering overhead by replacing the
current CH with a more eligible node. The FDW rule is used to select the CH. Given
the fact that clustering procedure is an on demand process and the data messages are
used for clustering, the overhead is reduced considerably and the clustering is done
faster. Also, because of creating large clusters with multi-hop clustering approach, re-
clustering is reduced, resulting in higher cluster stability.
Richard et al. [85] combines biometric identification system for authentication
and use IDS for monitoring the network. The authors argue that continuous
TB [76] Suitability
value (Si) based
on average
distance from
neighbors and
speed difference
with neighbors
Relative speed
less than a
threshold and is
in a specified
range
Relative speed
threshold Distributed Weight
based
algorithm,
and TB
with
different
relative
speed
thresholds
2-hop Multi-lane highway
MDMAC
[70] Weight based
(node ID, and
node
connectivity or
number of neighbors)
Freshness value:
estimation of
connection time
Prediction-
based CM
selection metric
Same direction
nodes join
cluster
Distributed Direction-
based
Prediction-
based
(cluster
membership
rules)
Multi-
hop Multi-lane highway
Fuzzy
Logic II
[73]
CHE value
(fuzzy controller
output based on
average
velocity,
distance, and
compatibility)
Location,
direction,
velocity, and
passenger
interest
Same direction
nodes join
cluster
Distributed Direction-
based
Multi-
hop 2 and 4 lane highway
implementati on
VPC [76]
V
P
C
1
Vehicle
density - Passive
clustering to
reduce
overhead,
prediction-
based metric
(LET) Combination of
FDW and
weight based metric to
assign priority
Distributed Prediction-
based LET
metric (VPC3)
- Highway (one way, multi- lane)
V
P
C
2
Link quality
(ETX, bi-
transmission
quality of a
link)
-
V
P
C
3
Link
sustainability
(LET, link
expiration
time)
-
authentication is necessary in MANET and traditional authentication mechanisms
such as Passwords and Token based schemes are not very feasible and secure. They
believe that biometric authentication provides better results. The authors then provide
a brief overview of IDS. The authors believe that Multimodal biometrics (using
multiple biometrics for identification) based in distributed fashion along with IDS is a
very good solution for securing MANET. Further, multiple devices are used for
identification of a node. It is important to note that biometric identification requires
huge data to be transmitted and thus energy efficiency is an important factor. The
paper then provides an overview of IDS as well biometric identification.
In the proposed system model the authors argue that both authentication and
intrusion detection can be done in each time slot, however, this will ingest a lot of
energy. Therefore, the decision when to carry out the authentication is a user decision.
Some number of sensors (again user dependent) is chosen which monitor its local
environment. Information of all the sensors is merged to take the decision of the
intrusion. For fusing the information the authors have used Dempster Shafer theory
which is a classification algorithm based on Naïve Beyes classifier. This theory works
on the principle of belief and trust.
In the proposed model each sensor node sends its security state and energy
state, and information from all the sources are combined to take the decision of
intrusion. However, if a compromised/malicious sensor is present, the information
cannot be trusted. Therefore, careful consideration is required for combining this
information. The existing methods are Type-I classifiers which use majority voting
scheme for fusing information. Class II classifiers are classifiers which give ranking
to the information. Class III classifiers are based up on fuzzy logic and probabilities.
Dempster Shafer theory is a class III classifier which uses probabilities to obtain a
certain degree of belief about a sensor node. It assumes that if a node is trustworthy
then the information it is providing is also considered accurate.
In the proposed system, the decision of activating sensor node is taken by
taking in to account the history of a node so that it can be ensured that the node is
trustworthy. These nodes are asked to provide the information regarding the intrusion.
Noman Mohammad et al. [86] presented a very interesting paper which made use of
game theory and Mechanism Design for detecting intrusion in MANET. The paper
discusses leader election in a cluster and cluster less environment. The authors
assume that energy level is an important feature in electing a Leader for cluster.
Malicious node can take advantage of this fact and can state wrong energy level
(selfish node). The crux of the paper is to motivate the nodes to inform about true
values about their energy level and based on it a reputation system is established
which will increase the reputation of the node which will help them in many ways
such as routing Priority.
The paper provides a brief discussion about MANET and how a Leader is
elected in a cluster environment. Following it a brief overview of proposed solution is
given. The authors state that leader election is very important process because the
Leader’s utility is significant in two ways; Leader can be used for distribution of Keys
and it can be used in routing. The paper then discusses the leader election mechanism.
In leader election process a mechanism model is provided where all the nodes are
considered as agents. Each agent provides its value and based on this value a
preference is given to the node which is described as payment. This payment system
is based up on VCG model. The payment is given according to power function in
which a node states about its power level. If a selfish node states that it has less power
level it will be deprived of higher reputation which may not be acceptable for it. On
the other hand if it states a higher energy level than it will have to run IDS for the
other nodes. However, the node can state a higher energy level with the hope that it
will not run IDS and pretend to be running it. To counter this problem, some checker
nodes are introduced which will monitor the behavior of Leader node. If a Leader
node acts maliciously the checker node can inform the other nodes and the Leader can
be identified as malicious. The checker nodes act cooperatively for completion of this
task. The paper then discusses correctness of its algorithm and authors prove that their
algorithm is correct. Finally, the simulation results of the scheme is provided where
interestingly no parameter regarding the intrusion is provided rather it discusses the
energy levels and alive time of a normal node etc.
Tang et al. [87] discuss that Cognitive Radios is a relative new idea which is
adopted for better utilization of a specific spectrum. CR checks that if a user of a
spectrum finds that spectrum allocated to its service provider is busy, it can check for
the spectrum of other service providers which is underutilized. Two important
definitions have to be kept in mind i.e. Primary User and Secondary User. Primary
user is the user of the allocated spectrum whereas the secondary user is the one which
is not a member of this spectrum but wants to use this spectrum since its own
spectrum is busy.
There are two main security threats Incumbent Emulation (IE) and Spectrum
Sensing Data Falsification (SSDF). In IE an attacker’s behavior is such that it sends
wrong signals to ensure that it is concluded that the spectrum is fully utilized. In
SSDF there are different types of attacks which ensure that wrong assumption
regarding the spectrum utilization is spread.
The focus of the paper is to ensure that all the user should be sure that a
spectrum is available or otherwise. Since there is no centralized node to give this
information, all the nodes take this decision collaboratively by exchanging
information with each other. However, this will give an opportunity to an attacker to
send false information. To mitigate this threat a solution is introduced and discussed
in the paper. This solution comprises of Consensus based algorithm. As per the
solution, first all the secondary users sense the medium individually and in the second
phase exchange this information with their neighbor nodes. If a neighbor’s
information deviates from a given threshold it can be identified as attacker. CR-
vehicular systems [88] are rising as a conceivable arrangement for empowering
correspondences where a way among source and goal might be inaccessible. Be that
as it may, these systems have a few confinements (e.g., capacity limit, constrained
power, or transmission ranges) which must be set out to improve system execution. A
conceivable arrangement is to animate hubs to collaborate among them. Different
participation systems have been advanced in writing to distinguish and confine
childish hubs. This exploration stir aggregates up the most essential tasks, systems,
and recommendations about hub participation in vehicular correspondences. Such
systems may accumulate a few commitments from remote specially appointed
systems. Most methodologies for these systems based on two primary strategies:
notoriety based, and valuing based. At notoriety-based methodologies, helpful hubs
are remunerated by expanding their notoriety score in the system, while egotistical
hubs are rebuffed and precluded from getting messages. Then again, in valuing-based
methodologies, hubs are paid for their agreeable conduct. The two methodologies are
likewise considered to make collaboration systems for VANETs, DTNs, and VDTNs.
Vehicular systems likewise present another kind of helpful methodologies situated in
trust tokens to distinguish and detach narrow minded hubs. Many directing
methodologies for vehicular systems were additionally proposed dependent on
collaboration between hubs.
The investigations previously directed, demonstrate that vehicular systems
show outstanding increases regarding execution when hubs are invigorated to
coordinate. It is likewise essential that malevolent conduct of uncooperative hubs may
prompt decrement of system execution. To diminish the impact of such hubs in the
system, the incentives that hubs get while coordinating in a communication ought to
be cautiously examined.
2.5 Swarm Intelligence Algorithms
Swarm Intelligence (SI) contains numerous prominent algorithms Ant Colony
Algorithm (ACO) put forth by Dorigo et al. [89]; derived from the social conduct of
ants. At the point when ants move, a liquid pheromone is released by the ants which
aid different ants for finding briefest ideal way. There are numerous other SI
Algorithms; Particle Swarm Intelligence (PSO) [90] by Kennedy and Eberhard in
which idea is inspired from winged creatures running; Bat Algorithm [91] which is
additionally on the conduct of various bats. Bees Algorithm which depends on the
progressive system of honey bees, and its functioning is characterized based on their
position that how honey bees discover their sustenance and their undertakings to
achieve. Some SI procedures proposed are as per the following:
Marriage in Honey Bees Optimization Algorithm (MBO) in 2001 [92].
Artificial Fish-Swarm Algorithm (AFSA) in 2014 [93].
Termite Algorithm in 2005 [94].
Wasp Swarm Algorithm in 2007 [95].
Monkey Search in 2008 [96].
Bee Collecting Pollen Algorithm (BCPA) in 2008 [97].
Cuckoo Search (CS) in 2009 [98].
Dolphin Partner Optimization (DPO) in 2009 [99].
Firefly Algorithm (FA) in 2010 [100].
Bird Mating Optimizer (BMO) in 2012 [101].
Krill Herd (KH) in 2012 [102].
Fruit Fly Optimization Algorithm (FOA) in 2012 [103].
2.5.1 Physics-based Algorithms
In physics-based algorithms, streamlining is performed by physical principles of
nature. This is an alternate methodology from others since it pursues physical
principles (Rules of nature) for finding the ideal outcomes. Survey specialists are
conveyed arbitrarily, and move in hunt space following the physical practices of
common marvels. A portion of the popular physical streamlining calculations are;
Gravitational Local Search (GLSA) in 2013 [104]
Big-Bang Big-Crunch (BBBC) in 2014 [105]
Charged System Search (CSS) in 2010 [106]
Central Force Optimization (CFO) in 2007 [107]
Artificial Chemical Reaction Optimization Algorithm (ACROA) in 2011
[108]
Black Hole (BH) Algorithm in 2015 [109]
Ray Optimization (RO) Algorithm in 2014 [110]
Small World Optimization Algorithm (SWOA) in 2006 [111]
Galaxy-based Search Algorithm (GbSA) in 2011 [112]
Curved Space Optimization (CSO) in 2012 [113]
2.5.2 Evolutionary Algorithms
The third kind of meta-heuristic is transformative calculation, in which the
key thought is taken from development of nature. Hereditary Algorithm [114], is a
standout amongst the most acclaimed strategies, have a place with this class. In
this optimality, the procedure is finished by joining and changing the
arrangement. Along these lines, results give best people having more
opportunities to take part for better arrangement. A portion of the developmental
calculations are;
Differential Evolution (DE) in 2006 [115].
Evolutionary Programing (EP) in 2012 [116].
Evolution Strategy (ES) in 2013 [117].
Genetic Programming (GP) in 2016 [118].
Biogeography-Based Optimizer (BBO) in 2008 [119].
These all strategies are used, for streamlining purposes, for taking care of
issues. Paper in [120] built up the weighted group calculation (WCA) in which hubs
are chosen as CH; the decision is relied upon the heaviness of hub while the
heaviness of hubs is subject to various parameters, for example, transmission run,
battery power, and portability. The research in [121] proposed the CLPSO in
MANET's and takes a shot at different parameters versatility, transmission control,
perfect degree, and vitality of the hubs. It depends on WCA in which every hub have
some load for referenced parameters, and after that CH is named based on weightage
of hubs and CH's are in charge of all correspondence with in the groups and
furthermore with the adjoining CH's (entomb and intra correspondence). Authors in
[18] proposed the MOPSO for just a single arrangement of an issue which can't be
considered as enough in proceeds with nature issues. ACO Based Clustering
Algorithm for VANET (COCANET) [122] is addressing a breach in progression for
enhancing the amount of groups which ought to be focused so grouping in VANETs
will provide more enhanced arrangement. Researchers in [123] suggested that
different directing conventions have been created for VANETs which are influenced
by natural changes yet are typically overlooked despite the fact that it influences the
throughput and execution. They presented the calculation of ACO and Dynamic
MANET on Demand which copy the adjustments in a situation. There are diverse
plans of grouping in which [124] gave the arrangement for choice of bunch head in
which every hub have one of a kind ID and the hub having most minimal ID will be
chosen; as [67] gave the technique for topology based grouping in which CH is
named by the base on various neighbors associated with that hub. This is known as
level of a hub, so the hub with the most extreme degree is viewed as requiring
increasingly opportunity to be chosen as CH.
CHAPTER 3
ICMFO: Intelligent Clustering using Moth Flame Optimizer for
VANETs
Clustering is the method to combine nodes to form a group into some specific region. This
assembling of nodes is always done for some specific purpose. This gathering of nodes that can
be either mobile, devices or automobiles is done by following some pre-defined rules. These
rules help to form some meaningful cluster otherwise the purpose of the cluster cannot be
fulfilled. When designing of cluster take place, there are always some primary elements in
cluster such as; cluster head in the cluster for managing and controlling the environment of the
cluster. Second element is cluster node or cluster member. In clusters; neighbors play a vital role
which is dependent on the transmission range of cluster. The selection of cluster head is also an
important and significant task.
In current era, meta-heuristic techniques such as Genetic Algorithm (GA) [114], particle
swarm optimization (PSO) [144] and ACO [89], are becoming popular in domain of computer
science; due to reasons like Deviation-free method, Flexibility, Local-optima Avoidance ,
Simplicity/Easily Understandable, and many others. Also, such approaches are lenient and easy
to apply. These approaches initiate with random solution, which exclude the calculation for the
derivation of search space and increase its applicability for current problems. They get their
imitative from the natural working of animals, birds and insects etc. providing opportunities for
the researchers in their implementation [145]. VANETs are dynamic networks, in which nodes
having inconsistent/random motion cause frequently structural deviations. Network lifetime is
enhanced by predicting flow pattern or mobility pattern of vehicles [146]. Furthermore, QoS is
mandatory for efficient transmission of data. Scalability is another issue which causes damage in
sustainability of network [147]. Clustering is a method in which a collection of nodes (cluster) is
made and one of the cluster member is selected as CH [67, 148-150].
Figure 3.1: Clustering in VANET's
Larger range of transmission requires a larger size of cluster, hence more members in the
cluster and vice versa. A good performance of the network requires a longer age of cluster [16].
The responsibilities of CHs, include creation of clusters, resources allocation to the member
nodes, and considering the topology of network for maintenance. They also manage the
communication between clusters, not only within the cluster among members but also with other
available clusters. MOBIC [78] is a clustering algorithms [78] working effectively in MANETs,
used for the selection of CHs. Effectiveness of any network is measured by the stability of
clusters [151]. Cluster stability can also be categorized as: a) Ratio of changes of cluster head. b)
Ratio of conversion of cluster nodes to CH [152].
Figure 3.2: Communication in vehicular ad Hoc Networks
Figure 3.3: A schematic of ITS services in VANETs
Table 3.1: Physics Based Algorithms
Algorithm Description
Central Force Optimization in
2007 [5]
Method developed from the theory of gravitational
kinematics.
Gravitational Local Search
(GLSA) in
2013 [6]
The main features of this heuristics derived from
“Newton’s law of gravitation”, namely a
gravitational search algorithm.
Black Hole Algorithm in 2015
[27]
The theme of black hole is used to develop the bio-
inspired algorithm.
Charged System Search in 2010
[28]
An approach taken from the behavior of charges
and it is based on the Coulomb law and motion
laws.
Ray Optimization Algorithm in
2014 [29]
The law for light “Snell’s light refraction law” is
mapped into algorithm for solving different
problems.
Artificial Chemical Reaction
Optimization Algorithm
published in 2011
The behavior is taken from the nature and
occurrences of chemical reactions.
Small World Optimization
Algorithm
(SWOA) in 2006 [30]
Concept is taken from the phenomena of small
world and different searching operator’s, i-e; small
range, large range and random range operators are
used in it.
Big-Bang Big-Crunch (Bbbc) [31]
in 2014.
Theme from the universe evolution is extracted to
explore the non-deterministic polynomial-time
hardness problems.
Curved Space (CSO)
Optimization [32] in 2012.
The general relativity theory is used for the
curvature of space and for aptitude simple random
search.
Galaxy-based Search Algorithm
(GbSA) [33] in 2011.
Theme of galaxies is embedded in hill climbing
algorithm.
Table 3.2: Evolutionary Algorithm
Algorithm Description
Evolutionary Programing (EP) It is developed for the Different evolutionary
[34] in 2012. parameters, finite state machine.
Evolution Strategy (ES) [35] in
2013.
Evolution and adaptation features are used in it.
Differential Evolution (DE) [36] in
2006.
The crossover and mutation are used to for the
generations.
GP [37] in 2016. Genes are modified as per the problem.
Biogeography-Based Optimizer
(BBO) [38] in 2008.
The candidate solutions are optimized recursively
to measure the quality.
Table 3.3: Swarm Intelligence Algorithm
Algorithms Description
Fruit Fly
Optimization
Algorithm
(FFOA) [39] in
2012.
The algorithm is developed by using the behavior of foraging of fruit
fly.
Cuckoo Search
(CS) [40]in
2009.
Logic taken from the cuckoo bird searching method.
Artificial Fish-
Swarm
Algorithm
(AFSA) [41] in
2014.
Motivation extracted from the majestic behavior of fish.
Termite
Algorithm
[42]in 2005.
Biologically inspired algorithm, resembling the behavior of Termites.
Monkey Search
[43] in 2008.
Concept taken from the living of monkey. It contains watch-jump
process, climb process, and somersault process.
Wasp Swarm
Algorithm [44]
in 2007.
Algorithm inspired from the colonial method of living and searching
food of wasp.
Bee Collecting
Pollen
Algorithm
(BCPA) [45] in
2008
Developed by the method of collecting pollen by honeybees.
Marriage in
Honey Bees
Optimization
Algorithm
(MBO) [46] in
2001.
Algorithm developed for optimization from the concept of mating of
honey bees.
Firefly
Algorithm
(FA) [46] in
2010.
The irregular behavior of firefly to indicate the others.
Dolphin Partner
Optimization
(DPO) [47] in
2009.
A viewpoint of DPO was articulated and Nucleus was hosted to
calculate the best location permitting to the fitness and position of
Team Members.
Krill Herd
(KH) [48] in
2012.
Herding of krill is used to develop the algorithm.
Bird Mating
Optimizer
(BMO) [49] in
2012.
A novel version of EA, BMO, it is used for the Continuous
Optimization Problems which is extracted by the breeding
approaches of bird species during breeding season.
3.1 Mathematical Modeling
The moths are considered as candidate solution in the algorithm. These candidate
solutions can be in N dimensions. The moths can be represented mathematically as;
n: Shows the number of moths. d; shows the dimension.
There will be a fitness value for each moth
OM: Fitness value. n; moth number.
For each moth the fitness function returns the fitness value. First row in matrix M (position
vector) of each moth is delivered to the fitness (objective) function. Accordingly, the fitness
Figure 0.4: Transverse orientation of moth flame
function’s output is assigned to the relevant moth as its objective function. Flames are another
vital factor in the MFO algorithm. A matrix analogous to the matrix of moths is shown as
following:
The same is represented in Equation (3) the dimensions of arrays M and F are identical. The
subsequent fitness values for all the flames are stored in an array, as follows:
The moths are considered as search agents in the space while the flames are used to point
out the fittest solution obtained. These points are the fittest spots obtained by moths. Hence, the
remaining moths will also start searching near the fittest spotted region. So, the moths will
achieve the best solution and converge earlier.
MFO scheme calculates the global optima of the optimization problem and is a three-
tuple as given below:
P is the main function, in which the moths travel in the search space. It considers matrix
M as an input and its updated copy is returned as an output.
T function characterizes termination criterion. If the termination criterion is achieved, it
will return true and false otherwise.
Here M shows the complete search space or matrix of moths, i-th means the moths while
j-th shows the flame. Equation 10 shows the fitness function; where, w1, w2....wn are the weights
assigned to fitness parameters. While f1, f2.... fn are the fitness parameters based on the problem.
Iteration is represented by d.
3.1.1 Flow Chart:
Figure 0.5: Flow chart of ICMFOs
3.1.2 Pseudo code for proposed ICMFOs Algorithm
Table 0.4: Proposed ICMFO Algorithm
3.2 Computational Complexity of ICMFO:
In our computation, the symbols used are:
z=number of moth flames
r= iterations executed
n= number of vehicles/nodes
k = Average CHs formed
The complexity of ICMFOs is calculated in small steps and then merged togather to show the
overall complexity.
3.2.1 Solution construction by a single ant:
In the worst case, to agree for a CH to be a part of solution, O (n) time is mandatory for
GWONET. Probability computation is executed, for this decision, over pre-calculated values of
exploration and exploitation. As the decision is repeated for ‘k’ times, hence, the solution
computation takes O (k.n) time.
3.2.2 Solution Quality / Fitness:
For a solution with ‘k’ cluster heads, it takes O (k.n) time to estimate the fitness of the solution.
3.2.3 Searching, Encircling and Attacking:
ICMFO takes O (k) time to explore the search space for finding the best solution between the ‘k’
clusters heads related to solution. It takes O (n) time to fitter solution out of the alpha, beta, delta
and omega or on unused cluster heads. Since k <= n with tendency to less, this sums up to O (n)
for ICMFO. ICMFO needs O (n2) tasks to the optimized number of clusters for the scenarios.
3.2.4 Complexity of while loop (i.e. batch of moths):
ICMFO takes O (k.n) + O (k.n) + O (n) for one moth which collapses to: O (k.n) and for ‘z’
moths, it becomes O (z. (k.n))
3.2.5 For ‘r’ rules creations in WHILE loop:
Hence, the overall complexity of ICMFO is O (r.(z.(k.n)) + (n2)), where n
2 characterizes
exploration and exploitation process.
CHAPTER 4
64
Experiments and Results
In this chapter we have described our adopted experimental methodology and the result
along with comparisons have been explained accordingly. Results obtained from our proposed
ICMFO algorithm with the two common and known algorithms used in same domain of
clustering. These two algorithm are MOPSO [18] and CLPSO [19]. Experimental results
obtained from our proposed algorithm clarify the difference with the existing ones that the
proposed technique requires less clusters for the network, which definitely will reduce the
routing cost consequently. The effect of the technique results in decreasing the number of hops
along with reduced packet delay in cluster-based routing. Normally if the transmission range of
clusters are less, the more clusters will be required to cover a specific area. It is clear from the
final result that in a particular environment of VANET, proposed system depicts better
performance in terms of effectiveness and adaptability with respect to techniques and
functionality adopted by other algorithms for the same environment. Proposed algorithm uses
more optimized parametric values to attain optimized solution for VANET. The parameters used
in simulations are presented in table 4.1.
.
Table 4.1: Simulation parameters for MOPSO and CLPSO
Parameters Values
Population size (particles) 100
Maximum iterations 150
Inertia weight W 0.694
c11 2
c21 2
Vehicle velocity range 22 m/s - 30 m/s
Simulation area 100 × 100 m, 200 × 200 m, 300 × 300 m, 400 ×
65
400 m
Maximum acceleration m/s2 1.5
Minimum distance b/w Vehicles 2 m
Maximum distance b/w Vehicles 5 m
Lane width 50 m
Total lanes 8
Transmission range 10 m – 60 m
Mobility model Freeway mobility model
Nodes 30, 40, 50 and 60
Simulation runs 10
W1 (weight of first objective function) 0.5
W2 (weight of second objective function) 0.5
Table 4.2: Simulation parameters for ICMFOs
Parameters Values
Population size (ants) 100
Maximum iterations 150
c1* 2
c2* 2
Vehicle velocity range 22 m/s - 30 m/s
Simulation area 100 × 100 m, 200 × 200 m, 300 × 300 m, 400 ×
400 m
Lower Bound (lb) 0
Upper Bound (ub) 100
Maximum acceleration m/s2 1.5
Minimum distance B/W Vehicles 2 m
Maximum distance B/W Vehicles 5 m
Lane width 50 m
Total lanes 8
Transmission range 10 m – 60 m
Mobility model Freeway mobility model
Simulation runs 10
Nodes 30, 40, 50 and 60
Linearly Decreasing Factor ‘a’ 0-2
W1 (weight of first objective function) 0.5
W2 (weight of second objective function) 0.5
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4.1 Experimental Setup
The computer system used for the experimental purpose was equipped with 16GB RAM
with core i7 processor having clock speed of 3.8 GHz. All the experiments are performed with
variable number of clusters ranging from 30 to 60. The road used for the experiment was
logically devided into four segments. Segment dimensions are divided into grids of: 100m x
100m, 200m x 200m, 300m x 300m and 400m x 400m respectively. All nodes move in bi-
directional plane on x-axis with variable velocity ranging from 80 km/h (22 m/s) to 120km/h (30
m/s). Each node has a varying transmission range from 10m to 60m. In order to balance the load
in ad hoc network, we have taken the difference in degree as 10. CLPSO and MOPSO are the
two well-known algorithms for the evolutionary purpose are used along with proposed algorithm
ICMFO for the implementation and experimental environment of VANET. Parametric values
taken for each algorithm are common. The average taken from each algorithm represented in
graphs and results were taken from ten simulations.
Figure 4.1: No. of clusters vs Nodes vs Transmission range in ICMFO, MOPSO, CLPSO and CACONET
for Nodes ranging from 30 to 60, for Grid Size = 1000 m
67
For validation of our presented protocol, comparative investigation of ICMFO is done
with meta-heuristic algorithms i.e. CLPSO, MOPSO and CACONET. Result shows that our
technique is producing smaller number of clusters which are required as compared to others. This
lessening in clusters leads to reduce the needed resources for network management; as well as
the cost of routing and the number of hops. Reduced number of clusters will also minimize the
packet delays.
Figure 4.2: No. of clusters vs Nodes vs Transmission range in ICMFO, MOPSO, CLPSO and CACONET
by fixing Nodes from 30 to 60, for Grid Size = 2000 m
68
The outcomes appear in Figs. 4.1, 4.2, 4.3 and 4.4, Transmission Range in the x-pivot,
number of hubs in y-hub and number of groups in the z-hub. Transmission run is from 100 m to
600 m, the quantity of hubs is 30-60 while diverse matrix measure from 1000m to 4000m is
utilized. The ICMFO demonstrates the optimal number of groups, shown with green circles in
figure 4.2. Required groups are inversely proportional to transmission run. As estimation of
transmission go is enhanced, necessary number of bunches needed will diminish. ICMFO shows
beytter results as compared with CLPSO, MOPSO and CACONET in all scenarios. The span of
the lattice is additionally changed to make the outcomes more grounded and progressively
immaculate. Charts delineate the outcomes positive to the ICMFO. Additionally, the quantity of
hubs/vehicles are changed with the goal that the precision of the proposed strategy can be
estimated. Eventually in the system, MOPSO, CLPSO and CACONET covers with the presented
technique. Be that as it may, this is because of the irregularity idea of the calculation.
The outcomes are taken after ten runs of each situation and afterward the normal esteem
is taken to plot the outcomes. Despite the fact that MOPSO gives the various answers for the
issue yet at the same time ICMFO is giving the upgraded outcomes to the given circumstance.
Figure 4.3: No. of clusters vs Nodes vs Transmission range fixing Nodes from 30 to 60, Grid Size = 3000 m
69
Figure 4.4: No. of clusters vs Nodes vs Transmission range fixing Nodes from 30 to 60, Grid Size = 4000m
4.2 Transmission Range vs Number of Clusters
Each node has a variable transmission range, ranging from 30 to 60 along with variable
number of nodes with 30, 40, 50 and 60 resulting in four different solutions. Results obtained
from the experiments are generated against four variable sized segments for the road with
dimensions: 100m x 100m, 200m x 200 m, 300 m x 300 m and 400 m x 400 m. Figure 4.1 shows
the optimized results obtained by the proposed algorithm implemented in each transmission
range. In contrast with CLPSO and MOPSO, the solution covers the whole network. Figure 4.1
also shows the performance parameter for the average number of clusters. For the grid of 100m
X 100m, our proposed algorithm generates less number for clusters to cover the area as
compared to CLPSO and MOPSO. ICMFO produced optimized number for clusters the
MOPSO.
70
Figure 4.5: Load Balance Factor when Grid Size is 1000m×1000m and Transmission Range varying from
100 to 600 and Number of Nodes = 30
71
After analyzing the results on the road grid of 100m x 100m, we changed the segment
grid to 200m x 200m. Figure 4.2 shows that by taking the specified grid, number of clusters are
increased as transmission range goes down. The reason behind the fact is that the nodes are out
of range from each other and cannot access each other. As soon as the transmission range
increases, more nodes appear in each cluster resulting in decreasing the number of clusters.
CLPSO is used in ICMFO to perform all experiments in order to attain improved solution. Figure
4.3 (d) shows the overlapped results for MOSPSO and CLPSO against all transmission ranges,
showing almost same results but ICMFO, on the other hand produces less number of clusters.
Now the grid dimensions are changed to 300m x 300m as in Figure 4.3. Number of clusters
is almost same as the total number of nodes as shown in Figure 4.3 (a), due to the reason of
enlarged network area and reduced transmission range of the nodes. The range of node
transmission and size of road segment are co related directly with each other. In MOSPSO, if the
transmission range is increased, then number of solutions also increase.
Figure 4.6: Load Balance Factor when Grid Size is 1000m×1000m and Transmission Range varying from
100 to 600 and Number of Nodes are 40
72
Figure 4.7: Load Balance Factor when Grid Size is 1000m×1000m and Transmission Range varying from
100 to 600 and Number of Nodes are 50
Grid size is changed again to 4km x 4km. it is clear from the Figure 4.4(d) that CLPSO
results in same number of clusters with number of nodes as each node has small transmission
range which also keeps on decreasing up to 19 as the transmission range is increased gradually.
MOPSO shows the same trend as CLPSO does. In ICMFO, it is clearly shown that those clusters
have decreased to 15 from 37 as transmission range is increased.
73
Figure 4.8: Load Balance Factor in case of CLPSO, MOPSO, CACONET and ICMFO when Grid Size is
1000m×1000m and Transmission Range varying from 100 to 600 and Number of Nodes are 60
4.3 Number of Clusters vs Network Nodes
For the comparison of number of hubs in each cluster, simulations are carried out by
setting the transmission go as 10, 20, 30, 40, 50 and 60 with number of hubs fluctuating from 30
to 60. Figure 4.8 demonstrates the outcomes against the matrix estimate running from 100m X
100m to 400m X 400m, Figure 4.8 demonstrates the accomplished outcomes by fixing the
framework to 100m X 100m and changing the transmission run from 10, 20, 30, 40, 50 and 60.
By utilizing the three calculations MOPSO, CLPSO and ICMFO, transmission extend
increments on the off chance that we decline the quantity of bunches by taking hubs continue
expanding and taking the transmission go steady. Number of bunches stays same for ICMFO as
appeared in Figure 4.5 (c). Proposed calculation demonstrates the adaptability and heartiness as
far as metric qualities and shows better outcomes for a situation of normal number of groups
conversely with different calculations.
74
Figure 4.5 (d) unmistakably demonstrates the ICMFO made two bunches in beginning
and with the expansion in hubs to 60, number of groups changed to three. Examination plainly
demonstrates the better execution of the ICMFO in expanded heap of traffic.
New matrix estimate is taken as 200m x 200m as appeared in the Figure 4.6. It is
presumed that ICMFO has appeared and improved execution regarding two other calculations
MOPSO and CLPSO. Expanded matrix estimate is taken now to 300m x 300m with the
transmission scope of 10, 20 30, 40, 50 and 60 and the outcomes are appeared in Figure 4.7. By
contrasting the outcomes and Figure 4.5, it is clear that on expanding the magnitude of network,
number of bunches likewise builds depicting direct connection between system size and number
of groups.
Figure 4.9: Load Balance Factor when Grid Size is 2000m×2000m and Transmission Range varying from
100 to 600 and Number of Nodes are 40
75
Presently the new component of matrix is taken as 400m X 400m with the variable transmission
scope of 10, 20, 30, 40, 50 and 60. On expanding the matrix estimate, separate between the hubs
likewise builds which show direct connection and thus a hub is confined. On the off chance that
every one of the hubs are brought about disconnection state, at that point most extreme number
of groups ought to be delivered by all calculations. By contrasting the two Figures 4.8 (a) and
(b) ICMFO demonstrates better execution and results. 60 hubs appeared in the Figure 4.8 (d)
then again ICMFO demonstrates 46 % less groups as ((26 - 14)/26) × 100 = 46 %.
Figure 4.9. shows the combined results abstained above in this chapter to analyze them
comparatively all together. All the results were calculated by taking variable grid sizes as 100m x
100m, 200m x 200m, 300m x 300m and 400m x 400m. Figure 4.9. show the optimized and more
better results which are calculated on the bases of proposed algorithm as compaired to MOPSO
and CLPSO. The same figure also depict that average number of cluster are used for the
evaluation as in 100m x 100m grid size, the presented scheme produces the results for each
transmission range as compared to MOPSO and CLPSO. In most of the cases, less number of
clusters are produced by ICMFO in comparison with MOPSO and CLPSO even in the varibale
transmission ranges form 10 to 60. No doubt MOPSO gives multilple solutions for the explained
scenario of the network but proposed algorithm ICMFO produces less number of clusters.
Figure 4.9. shows another result based upon the the grid size taken as 200m X200m. It is
clear from the results that, if we increase the transmission range the number of vehicles in each
custer will decrease. Hence we can say that ICMFO gives more optimized solution then MOPSO
and CLPSO.
Now at later stage, we varied the grid size of segment to 300m x 300m and 400m x 400m
as shown in Figure 4.10. Number of clusters are same in MOPSO due to small range of
transmission. As we increase the transmission range, the clusters keep on decreasing to 29.
CLPSO almost works in a same passion as MOPSO. In ICMFO, thete were 49 clusters initially.
Upon increasing the transmission range, it kept on decreasing upto 15. It happens because
network area is bigger and the transmission range is smaller.
CHAPTER 5
79
Modeling and Simulation of VANETs Security Scheme
5.1 Network Deployment
The proposed system compose of Application Unit (AU) driver, vehicular nodes or OBU
and Road Side Units (RSUs) or cellular technologies which provides building block for ITS.
Inter Vehicle Communication (IVC) is mostly use for VANETs scenarios interchangeably or
synonymously. The protocol designed for interface among V2V and Vehicle to Infrastructure
(V2I) in WAVE are IEEE 802.11p based on DSRC. In VANETs vehicle’s also called OBU’s
and Application Unit (AU) or driver set in the car, has an interface with another OBU and also
with RSU’s communicating with each other.
The communication among vehicular nodes is called V2V, and communication among
OBU and Infrastructure is named as V2I communication. IEEE 802.11p is an amendment in Wi-
Fi (IEEE 802.11) WLAN standard utilizing seven allocated channel of Band Width (BW) within
75MHz with a frequency band of 5.9 GHz.
Figure 5.1: Design of VANETs Security Model
As we discussed previously DSRC in WAVE for VANETs. Also explained that
VANETs provided base for ITS which not only reduced the ratio of accident on
expressways/motorways but also facilitated the serving nodes regarding traffic jam situation. But
80
in VANETs information security is also a challenging task. To protect this information from
malicious nodes several security services and mechanisms are implemented and still new
implementation procedures are in way.
The methodology includes 3 vehicles and RSU’s communicating with each other via
IEEE 802.11 a/b/g. A scenario is created whereas a hidden node is moving towards some other
node. V3 and V1 are unaware of each other, because vehicle V1 and V3 are not in range of each
other’s. A vehicle V2 is in range of both V3 and V1 via DSRC and an RSU in access of all of
them. V3 broadcasts an alert about its speed and position to inform nearby vehicles through
DSRC and sends an alert towards the RSU. V2 receives the alert and propagats the alert to its
nearby vehicles as in figure 5.2.
Figure 5.2: Vehicle to Vehicle Communication
On reception of alert by V1 from V2 and also from RSU, V1 goes for
registration/authentication verification process; to make sure that the message is issued from an
authentic source or not. The communication existing among vehicles is called ad hoc mode,
while due to addition of an infrastructure, it switched into an infrastructure mode. VANETs
security model is described with the help of a flow chart in figure 5.3.
81
5.2 Frame work for ARV2V Scheme
A Flow Chart is a schematic plot that shows the successive processes performed to found
a solution of a problem. Our constructed flow charts investigating a solution for a problem face
in VANETs security. Our flow chart has different blocks and each block has its own process.
From the start, block vehicles and RSU constitutes a VANETs system. In initialization process,
vehicles and RSU register themselves to a registration server. This server authenticates their
authentication from a verification server to evade diffusion of malicious node and make the
system secure at crucial level.
There are three vehicles (V1, V2, V3) and an RSU participating in the session; V1
receives a Fundamental Safety Alert Messages (FSAMs) from V3 through V2. V1 inquired the
same alert message (FSAMs) from RSU to confirm wither the received FSAMs from V3 is
correct. If the alert FSAMs received from both entities are same then it will inform the driver
about the validity of node V3 also inform ConiaVai exchange about the validity of node V3
correctness where, ConaiVai exchange confirms the confidentiality of FSAMs to avoid
snooping. Check integrity of FSAMs to handle masquerading, repudiation and replaying
attempts of dishonest nodes is done. Also it ensures on time availability of FSAMs for requesting
vehicles. After he minimum acceptable threshold criteria of ConaiVai exchange node V3 and
other meet the same criteria are declared valid. These valid nodes are enlisted as true nodes and
allowed for broadcasting FSAMs in the network.
If received FSAMs from RSU and V3 does not match and decision block hold no
statement, then it switch into another block for further verification about node V3, to look over
the node V3 position validity is that node hold valid position or hold an unidentified position. If
position is identified then the FSAMs is forward to next block, to check FSAMs confidentiality
and for more further investigation about FSAMs is confirmed from ConiaVai exchange.
After position validity and FSAMs correctness, node V3 validity is endorsed and allowed
for broadcasting FSAMs. If V3 position is invalid then FSAMs are discarded, again if position of
node is valid but FSAMs does not hold, confidentiality check again makes FSAMs pumped into
the discard bin.
Sensors are also dispersed on highways which also gather data about events, the Cluster
Head (CH) forward FSAMs towards ConaiVai exchange, which is filtered there. If received
FSAMs from CH and that from RSU and V3 notify V3 and other nodes as valid, then they are
82
allowed for broadcasting. In case of any dissimilarity, FSAMs are pushed towards the discard
bin. All of these FSAMs received from different sources forward to ConiaVai exchange for
judgment, to check wither these alerts meet threshold value. If yes, it endorses the V3 trustiness.
Those alerts are also forwarded to drivers to make them assure about the malicious node
penetration. All these nodes alongwith CH hold on for next FSAMs alert message and also fake
formulated FSAMs are moved towards discard block. This reduces the level of V3 trustness, and
enables other nodes aware about the falsehood of received FSAMs from V3. Also, vehicle V3 is
forbid to pump any alert in the network because system declares it as invalid and fake node.
83
Figure 5.3: Flow Chart of ARV2V Security Model
84
5.3 ARV2V Mathematical Model
In ARV2V security model, on reception of any consequences from source node, the
target has numerous ways of validation about the legitimacy of Fundamental Safety Alert
Messages (FSAMs).
ARV2V scheme has variety of events; hence the occurrence of incorrect events may
exist. Results testified from a source node wishes to be confirmed before any exchange of data in
the network. ARV2V scheme collects sufficient proof to list the valid/invalid events, and correct
false data to evade nodes from misguidance.
The presented scheme forms a basis for all kind of trust models, and the precision of any
happened occurrence is recorded and based on the observation of participating nodes. Hence a
valid node forwards a valid event towards the receiving one, and with the passage of time more
nodes will also get aware of the event occurance. However, the trustiness of the said method may
face failure when a valid node in ARV2V model furbishes invalid/fake data.
To evade fake information broadcasting Mass Metric Procedure (MMP) is used to sanction
actual true or valid report and contradicting report.
MMP is used in decision making to allow the sender for data transfer or not. In equation
5.1, M v is valid mass metric.
M v
M v + (M ¬ v) < 1 - ξ……………………..……………………………………….… (5.1)
The scheme ARV2V is more safe and resistive against different attacks and thwart
malicious node penetration attempts. It is basically based on trust management approach. The
aim is to identify malicious data nodes. At the initialization, every node has information about
the network behavior and nature. It investigates event accuracy from information received or
from its own analysis. When a node undergoes unusual changes it forwards these changes to
surrounding nodes through a broadcast message and alerts nodes to switch into a safe mode. It is
also possible that malicious node misguide other nodes through falsified FSAMs and drive the
network for his benefits.
Any node who receives FSAMs goes into a verification phase to understand the nature of
information received before taking any decision. A process is required to judge the correctness of
received data. While the destination node holds a series of consequences achieved from received
data and sender to verify messages validity.
85
Before any judgment about the accuracy of received information from sender, a trust/
confidence value for that authenticity is established. The confidence value for any sender at time
interval n can be written as CS (n). The message correctness about a consequence verification
mass metric is used shown in equation (5.1); where CS (n) comes true if it follows equation
(5.2).
0 ≤ CS (n) ≤ 1…………………………….. ………….. (5.2)
A node has two containers, information containing a consequence is marked as P-container and
also represented by a binary digit 1, and bin with no consequence marked with NP-container and
also a binary digit 0 is assigned to it. Average confidence values are computed from these
containers utilizing sender confidence. Suppose P-Container has S sender and NP-container has
Q sender, so the average confidence of each container at time interval n is given as
C1 (n) = ∑ci
S
S i=1 and Co (n) =∑
cj
Q
Q
J=1 …………………………………….... (5.3)
where; C1 (n) is average confidence of an event and C0 (n) is average confidence of no
consequence. Normalized confidence of the node from each pot called Mass Metric of the given
container are expressed in equation (5.4); where mi (n) is mass metric of ith node for bin 1 and
mj (n) for jth node for pot 0.
mi(n)= ci(n)
C1(n), and mj(n)=
cj(n)
Co(n)……………………….………………….. (5.4)
When a node confirms a consequence in his previous report, that node cannot deny from
the previous submitted report. Similarly if node denies a consequence once, it cannot confirm the
same event later so masquerading is not allowed.
CHAPTER 6
RESULTS AND DISCUSSION
This chapter is the central part of our research analysis and simulations have been
performed to formalize our designed technique efficiently. The comparison of ARV2V is done
with prevailing techniques of TRUST and Logistic Trust in terms of TCE, EED, ALD and NRO.
Simulation is a procedure of resolving problems by watching the performance, of a dynamic
model of the system with respect to time.
6.1 Trust Computation Error
Figure 6.1: Trust Computation Error vs Vehicle Density
Table 6.1: Trust Computation Error per 200 Vehicles
Protocol 200 400 600 800 1000 Average % Improvement
ARV2V 0.03453 0.0280 0.0210 0.0166 0.00194 0.02041 4.463
Trust 0.001757 0.00719 0.0270 0.0718 0.0894 0.03942 7.30
L. Trust 0.001757 0.00445 0.00546 0.00890 0.0119 0.0054 1.00
Trust Computation Error (TCE) is the mean square error between the predicted and
actual trust values of the vehicles. Figure 6.1 is showing performance of ARV2V scheme which
has optimal working than TRUST and Logistic Trust (LT). Table 6.1 highlights that ARV2V has
consistency among the values of TCE with an increase of 200 vehicles in each step. While in
case of Trust and Logistic Trust technique there is no such consistency among the values of TCE
with 200 vehicles per step increase are documented.
ARV2V scheme is 11.6% and 7.3% more efficient in term of TCE than the LT and Trust
schemes respectively; while Trust scheme is 4.3% more efficient in terms of TCE than LT. The
enhancement in ARV2V is due to the fact that our model calculates trust for all nodes randomly
and identify malicious node from their negative feedback. ARV2V performed well in the
presence of large number of false or malicious nodes; due to the feature of feedback metric
credibility in ARV2V algorithm.
6.2 End-to-End Delay
Figure 6.2: End-to-End Delay (106 Seconds) vs Vehicle Density
Table 6.2: End-to-End Delay (106 Seconds) per 200 Vehicle Density
Protocol 200v 400v 600v 800v 1000v Average Improvement
ARV2V 0.67 0.55 0.39 0.29 0.17 0.414 1.00
Trust 2.8 0.31 0.09 0.031 0.031 0.6524 1.576
L. Trust 2.34 0.75 0.52 0.473 0.274 0.8714 2.104
End-to-End Delay: The time taken by FSAMs to travel in a VANETs model from
source to destination node. Due to high mobility scenarios in ARV2V, on time delivery of
FSAMs may be delayed. Figure 6.2 depicts that the performance of ARV2V scheme is better
than, Trust and Logistic Trust techniques. An increase in vehicle density also depicts a consistent
reduction in packets end to end delay. Such gradual reduction in end to end delay declares
ARV2V more logical then Trust and Logistic Trust schemes. There is no such consistency in the
values of EED recorded with the increase of vehicle density. From table 6.2 it is concluded that
average EED delay in case of ARV2V technique is approximately 0 %. The Trust and LT
schemes face 57.6% and 5.2% longer delay respectively than ARV2V algorithm; where Trust
scheme has 52.4% more EED than LT scheme.
6.3 Average Link Duration
Figure 6.3: Average Link Duration vs Vehicle Density
Table 6.3: Average Link Duration (104 Seconds) per 200 Vehicles
Protocol 200v 400v 600v 800v 1000v Average %Improvement
ARV2V 1.20 1.139 1.008 0.8775 0.4438 0.9337 2.594
Trust 1.06 0.3062 0.2139 0.1217 0.0978 0.3599 1.00
L. Trust 1.824 0.5221 0.9957 1.833 0.6466 1.1642 3.234
Average Link Duration: it is the communication link lifespan estimation establish
among source and destination vehicle to exchange FSAMs. In VANETs path choice is an
important parameter for good performance and better data rate. But in VANETs link duration
depends on various parameters like transmission range of the vehicle, inter vehicle distance,
vehicles density and vehicles velocity which made link duration stability a challenging job.
Figure 6.3 depicts that the scheme ARV2V is more stable and reliable. Also table 6.3
reveals that our proposed scheme has stable link duration. There is a uniform increase in vehicle
density of 200 vehicles per step. From table 6.3 we concluded that our designed ARV2V
technique provides 29.7% and 7.8% more reliable and stable ALD than Trust and LT techniques
respectively. However, LT ALD is 21.9% more than Trust algorithm. So, increase in vehicle
density ARV2V preserve link stability and very little gradual change noticed in the average link
values. It means that ALD in ARV2V scheme is more reliable and stable. However, the
remaining schemes Trust and LT undergoes sudden change in ALD with increase in vehicles
density and small consistency are observed in ALD values. So comparison results shows that our
proposed ARV2V scheme, has better efficiency in term of average like duration and very little
packets are lost.
6.4 Normalizing Routing Overhead
Figure 6.4: Normalized Routing Overhead vs Vehicle Density
Table 6.4: Normalized Routing Overhead per 200 Vehicles
Protocol 200v 400v 600v 800v 1000v Average %Improvement
ARV2V 34.53 28.01 21.07 16.63 1.94 20.5 1.00
Trust 1.781 14.39 54.07 143.6 178.8 78.53 3.83
L. Trust 7.028 17.8 21.85 35.61 47.7 26.1 1.28
Normalized Routing Overhead: it is a ratio of transmitted routing packets divided by
the number of data packets deliver at destination node. Figure 6.4 depict an overhead returned by
ARV2V, Trust and Logistic Trust. Effect of overhead of these schemes is shown with increase of
vehicles density respectively depicted in figure 6.4, and table 6.4. In figure 6.4, the vehicles
density are adjust at 1000 vehicles. We notice that our algorithm ARV2V has significant
reduction in load with increase in vehicle density. In ARV2V scheme overhead/load gradually
reduces with the increase in vehicles density. While; in other two algorithms overhead
enormously increases with increase in vehicle density.
From figure and table 6.4, it is concluded that overhead recorded in ARV2V is nearly 0%
while; Trust and LT schemes in comparison with ARV2V faces 27.5 % and 14% more NRO
overhead respectively, also Trust has 13.5% more load or NRO than LT. So, in conclusion
ARV2V is 27.5% and 14% more efficient than Trust and LT protocols respectively. The
particular improvement in our scheme is due to the fact that our designed scheme considerably
reduces Route Request (RReq) query to conceive routes and choose the most stable and reliable
rout for transmission of data packets. This result into minimal routes failure and considerably
small number of control messages i.e. overhead are required to detect a route for information
exchange. Table 6.4, shows a gradual reduction in NRO values with 200 increase in vehicles
density per step, while; Trust and LT procedure favor sudden change in NRO with 200 increase
in vehicles density per step. From these analysis our scheme outperform than the rest of two
schemes.
Next chapter is conclusion of our research work which shows advancement and
importance of our research in the field of trusted and secure transportation system. Further, it
also explains the future possible work in current research.
CHAPTER 7
94
CONCLUSION AND FUTURE WORK
7.1 Conclusions
Early VANETs were a Car to Car (C2C) communication basically designed for data
exchange among vehicles. Later on, the feature of vehicles to road side infrastructure was also
added to VANETs to make system more efficient for data exchange to ensure safety of humans
and avoid unpleasant situations. VANETs is building key block of ITS framework also known as
Intelligent Transportation Networks (ITNs); basically designed for dissemination of Cooperative
Awareness Messages (CAMs) in network for long distances among the vehicles and RSU’s in
range.
Much of research work has been done on VANETs that inspected various aspect and put
modification and improvement in those areas. Few of them worked on PDR, topology related
changes and suggested better protocols for dynamic environment, data rate, overall system QoS,
packets end-to-end delay, link or path stability interval, threats to information/data and security
of data. Researchers proposed verity of routing schemes aiming to enhance the performance of
vehicles information interchange among source and destination vehicles in VANETs system by
taking into account various performance parameters. It is not possible that a single algorithm is
rich so, that it has all good qualities in term of performance. From comparison of different
routing algorithms we demonstrated that if a scheme is better in one response faces certain
challenges in other response.
So optimum and absolute routing scheme having all good qualities with respect to
performance parameters as discussed in above paragraph is still a challenging job. ITS; purely
based on VANETs system and an essential key technology worked on DSRC. To avoid
hazardous circumstances FSAMs or any other emergency messages required priority based on
time dissemination among vehicular nodes and road side infrastructure and assurance of its
flawless delivery at receiving node is most critical task. In case of such critical situation link
failure occurs the packets of FSAMs may face delay and once can face worst tragic situation in
sense of loss of precious lives and property. In our research work we have designed technique
ARV2V, and compared with already existing techniques like Trust and Logistic Trust in terms of
95
metrics like Trust Computation Error (TCE), Average Link Duration (ALD), End-to-End Delay
(EED) and Normalized Routing Overhead (NRO) with respect to increase in Vehicles density.
In term of performance metric TCE ARV2V is 11.6% and 7.3% efficient then LT and
Trust respectively, while Trust scheme is 4.3% efficient then LT. From EED comparison we
found ARV2V 57.6% more efficient than Trust and 5.2% than LT, also Trust schemes faced
52.4% more delay than LT. Similarly, in term of ALD ARV2V provides 29.7% and 7.8% more
stable link duration than Trust and LT, however LT has 21.9% more efficient ALD than Trust. In
term of NRO our proposed ARV2V protocol have 27.5% and 14% lesser load than Trust and LT,
while Trust has approximately 13% more NRO than LT. From these observations we concluded
that performance of our designed schemes in term of these parameters is more valuable and
authentic than the Trust and LT algorithms. Our research shows ARV2V scheme has better
stability period, less latency, improved data rate over Trust and LT schemes.
In this thesis the vehicular node clustering problem in VANETs is addressed. Moth flame
optimization technique is used to optimize the clustering in VANETs. Afterwards the ICMFOs is
compared with the two variants of PSO, namely CLPSO and MOPSO. As these both algorithm
(PSO, MFO) works efficiently for the continuous value problems, same experimental
environment was set to do the comparison. As, ICMFO start learning from the very first iteration
due to the hierarchical model of its nature. Meanwhile PSO is based completely on the
randomness of initialization; moreover there is no hierarchy in it. Therefore, it starts learning
after the ICMFOs.
The main motivation is to optimize the performance of evolutionary algorithm for the
clustering in VANETs. As VANETs are always expected to be scalable i.e. the number of
automobiles can be increased any time on the highway.
Clustering technique supports the network by isolating the nodes into smaller divisions which are
easily manageable. It also assists in the complex networks, for the data aggregation and
managing the network. In MAC protocols, clustering helps in increasing network capacity by
controlling the topology, providing fair channel access, organizing medium access and reducing
channel contention.
There is a significant space in performance enhancement of clustering approaches by using the
evolutionary methods.
96
7.2 Limitations
Road size, number of nodes (static), transmission (random), re-clustering scenario/rule, urban
There are some constraints or limitation while optimizing the problem as discussed;
Moreover, the density of node also directly influences the application of VANETs [153].
The main objective of the proposed algorithms is to perform cluster-based communication such
that it has minimum routing overhead and low energy consumption. However, there are some
constraints and limitations e.g.
ICMFO does not guarantee to return global optimal results. Sometimes it may stick at
local optimal. It can further be improved by inclusion of some random variables that will
take it away from local best.
k-means is biased by the value of ‘k’. It produces ‘k’ number of clusters; irrespective of
network constraint. ‘k’ learning algorithm is based on random selection. Although, it
returns the most frequent value of ‘k’, it is not an optimal value. It does not guarantee that
all CMs lies in the direct transmission coverage of CH. That is why k-means have shorter
cluster lifetime as compared to CACONET and GWOCNET.
k-means is also sensitive to the initial centroid selection. Original k-means choose a
random selection, which does not produce unique results. The proposed model used the
uniformly distributed initial centroids. This function can further be improved to select the
best initial centroids which results in optimal formation of clusters.
While computing energy consumption for a node, we only consider the energy
consumption during packet transmission and reception. Whereas in real application, it
also includes energy consumption at idle and processing time.
Quality of experiments can be improved to obtain more accurate results. More variation
in grid size and number of nodes can be inducted. Performance can also be evaluated in
terms of overall delay and throughput.
97
7.3 Future Work
This problem can be enhanced by using the different meta-heuristics such as; Multi-objective
gray wolf optimizer and MFO, Dragon fly, [21, 154]. The solution can be designed as multi-
objective so that more than one solution can be extracted and better one is used for solving the
problem. The number of parameters can be changed to increase the performance. Parameters can
be modified at run time. As, technology is changing day by day we can use these algorithms in
vehicular ad hoc network (VANETs) [155].
This thesis has set out to explore the problems of efficient routing and energy consumption in
VANET. Limited battery energy and low computational power put hindrances on wide
applications of UAVs. The optimal use of these resources will enhance lifetime of UAVs. These
resources can efficiently be utilized by devising a communication mechanism among UAVs such
that it has minimum routing overhead, maximum throughput and low computational complexity.
One remedy for this scarce resources problem is clustering. Clustering is an approach for
arranging nodes having same geographical neighborhood, into multiple groups. It helps to make
the network more scalable, reduce routing overhead and maximize the throughput. This low
routing overhead will save the UAVs energy as well.
Selection of transmission power also plays a vital role in energy consumption of UAVs. There is
a direct relationship between transmission power and energy consumption. Selecting
transmission power above or below from optimal value will result in more energy consumption.
An optimal transmission power must be high enough to maintain good connectivity with
neighbors and low enough to avoid wastage of energy.
7.4 Contributions
In this dissertation, the focus was to optimize the routing and save the UAVs energy by means of
controlling the transmission range and efficiently clustering in the VANET. It was proposed
ICMFO and intelligent k-means algorithms for efficient communication. These algorithms are
very simple in nature and have very low computational complexity. ECRNET used the k-means
Sorted Fitness to make cluster in the network. Weighted fitness is calculated for each CM to
98
elect CH. residual energy, distance from neighbors and difference from ideal degree are
considered to compute fitness value. Constraints are added for the election of a valid CH.
k-means is very sensitive to the value of ‘k’ and selection of initial centroids. ‘k’ dictates the
number of clusters to be formed. It is made intelligent to learn the value of ‘k’ through a heuristic
approach. ‘k’ learning algorithm scan the entire network and test different values of ‘k’. The
most frequent value of ‘k’ is then opted. Clustering process starts with uniformly distributed
initial centroid for balanced network division. After clustering, k-means returns the nodes
assignment with a particular cluster. A CH is then elected from each cluster based on fitness
value.
We optimized the process of selecting transmission power while considering operating
environment and application scenario. We divided the operation area or grid into four sub-grid
and calculated the average distance from all nodes within sub-grid. This average distance is then
used to select transmission power level that is the most appropriate for all nodes in the sub-grid.
With the selected power level, SNR at the maximum range provides minimum PLR without any
wastage of energy.
In order to validate ECRNET and intelligent k-means, we empirically evaluate their performance
through an extensive set of experiments. A comparison is made between state-of-art artificial
intelligence techniques CACONET and GWOCNET. The performance is evaluated in terms of
no. of clusters, cluster building time, cluster lifetime and energy consumption.
99
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