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

66

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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