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COMSATS University Islamabad

Towards Attack Resilience of Scale-Free IoT

Networks with Topology Modiﬁcations (MS

Thesis without Source Codes)

A Thesis Presented to

COMSATS University Islamabad

In partial fulﬁllment

of the requirement for the degree of

MS (Electrical Engineering)

By

Muhammad Awais Khan

CIIT/FA18-REE-009/ISB

Spring, 2021

ii

Towards Attack Resilience of Scale-Free IoT

Networks with Topology Modiﬁcations (MS

Thesis without Source Codes)

A Post Graduate Thesis submitted to the Department of Electrical and Computer

Engineering as partial fulﬁlment of the requirement for the award of Degree of MS

(Electrical Engineering).

Name Registration Number

Muhammad Awais Khan CIIT/FA18-REE-009/ISB

Supervisor:

Dr Nadeem Javaid,

Associate Professor, Department of Computer Science,

COMSATS University Islamabad,

Islamabad, Pakistan

Co-Supervisor:

Dr Sardar Muhammad Gulfam

Assistant Professor, Department of Electrical and Computer Engineering,

COMSATS University Islamabad,

Islamabad, Pakistan

iii

Final Approval

This thesis titled

Towards Attack Resilience of Scale-Free IoT Networks with

Topology Modiﬁcations (MS Thesis without Source Codes)

By

Muhammad Awais Khan

CIIT/FA18-REE-009/ISB

has been approved

For the COMSATS University Islamabad, Islamabad

External Examiner:

Dr. Ataul Aziz Ikram

Professor, Department of Electrical Engineering,

FAST-NU, Islamabad, Pakistan

Supervisor:

Dr Nadeem Javaid

Associate Professor, Department of Computer Science,

COMSATS University Islamabad, Islamabad, Pakistan

Co-Supervisor:

Dr Sardar Muhammad Gulfam

Assistant Professor, Department of Electrical and Computer Engineering,

COMSATS University Islamabad, Islamabad, Pakistan

Head of Department:

Dr. Shurjeel Wyne

Associate Professor, Department of Electrical and Computer Engineering,

COMSATS University Islamabad, Islamabad, Pakistan

iv

Declaration

IMuhammad Awais Khan (Registration No. CIIT/FA18-REE-009/ISB) hereby de-

clare 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: 27 July, 2021

Muhammad Awais Khan

CIIT/FA18-REE-009/ISB

v

Certiﬁcate

It is certiﬁed that Muhammad Awais Khan (Registration No. CIIT/FA18-REE-009/ISB)

has carried out all the work related to this thesis under my supervision at the

Department of Electrical and Computer Engineering, COMSATS University, Is-

lamabad and the work fulﬁls the requirement for award of MS degree.

Date: 27 July, 2021

Supervisor:

Dr Nadeem Javaid

Associate Professor, Department of

Computer Science.

Co-Supervisor:

Dr Sardar Muhammad Gulfam

Assistant Professor, Department of

Electrical and Computer Engineering.

Head of Department:

Dr. Shurjeel Wyne

Associate Professor, Department of Electrical

and Computer Engineering

vi

ACKNOWLEDGEMENT

First of all, thanks to Almighty Allah who gave me the strength to complete this

thesis. After that, I will thank my honourable supervisor Dr Nadeem Javaid,

co-supervisor Dr. Sardar Muhammad Gulfam and my parents because without

their support I will not be able to complete my thesis with dignity and respect.

My supervisor Dr Nadeem Javaid helped me in every cause. Whenever I feel

unmotivated or depressed, he keeps pushing me. He gives me the time whenever

I need it the most and I did not see any supervisor giving that much extra time

to his students. Lastly, I am greatly thankful to my ComSens Lab colleagues for

providing me with a warm and friendly atmosphere.

vii

Conference Proceedings

1 Khan, M.A., Javaid, N., Majid, A., Imran, M. and Alnuem, M., 2016, March.

Dual sink eﬃcient balanced energy technique for underwater acoustic sensor

networks. In 2016 30th International Conference on Advanced Information

Networking and Applications Workshops (WAINA) (pp. 551-556). IEEE.

Download

2 Zain-ul-Abidin, M., Khan, M.A., Javaid, N., Khizar, M., Khan, Z.A. and

Qasim, U., 2016, March. Enhanced single chain-based scheme in cylindri-

cal underwater wireless sensor networks. In 2016 30th International Con-

ference on Advanced Information Networking and Applications Workshops

(WAINA) (pp. 343-348). IEEE. Download

3 Khan, M.A., Sher, A., Hameed, A.R., Jan, N., Abassi, J.S. and Javaid, N.,

2016, November. Network lifetime maximization via energy hole allevia-

tion in wireless sensor networks. In International Conference on Broadband

and Wireless Computing, Communication and Applications (pp. 279-290).

Springer, Cham. Download

4 Khan, M.A., Javaid, N., Wadud, Z., Gull, S., Imran, M. and Nasr, K., 2017,

June. Towards energy balancing in heterogeneous wireless sensor networks.

In 2017 13th International Wireless Communications and Mobile Computing

Conference (IWCMC) (pp. 786-791). IEEE. Download

5 Khan, M.A., Javaid, N., Javaid, S., Khalid, A., Nasser, N. and Imran, M.,

2020, June. A novel cooperative link selection mechanism for enhancing

the robustness in scale-free IoT networks. In 2020 International Wireless

Communications and Mobile Computing (IWCMC) (pp. 2222-2227). IEEE.

Download

viii

ABSTRACT

Towards Attack Resilience of Scale-Free IoT Networks with

Topology Modiﬁcations (MS Thesis without Source Codes)

Nowadays, the Internet of Things (IoT) provides beneﬁts to humans in numerous

domains by empowering the projects of smart cities, healthcare, industrial en-

hancement and so forth. The IoT networks include nodes, which deliver the data

towards their destination. However, the removal of nodes due to the malicious

attacks eﬀects the connectivity of the nodes in the networks. The ideal plan is to

construct a topology, which maintains the nodes’ connectivity after the attacks and

subsequently increases the network robustness. Therefore, in this thesis, we ﬁrst

adopt two diﬀerent mechanisms for the construction of a robust scale-free network.

Initially, a Multi-Population Genetic Algorithm (MPGA) is used to overcome the

premature convergence in GA. Then, an entropy based mechanism is used, which

replaces the worst solution of high entropy population with the best solution of

low entropy population to improve the network robustness. Second, two types of

edge swap mechanisms are introduced. The Eﬃciency based Edge Swap Mech-

anism (EESM) selects the pair of edges with high eﬃciency to increase the net-

work robustness. The second edge swap mechanism named EESM-Assortativity

transforms the network topology into an onion-like structure to achieve maximum

connectivity between similar degree nodes in the network. The optimization of

the network robustness is performed using Hill Climbing (HC) and Simulated An-

nealing (SA) methods. The simulation results show that the proposed MPGA

Entropy has 9% better network robustness as compared to MPGA. Moreover, the

proposed ESMs eﬀectively increase the network robustness with an average of 15%

better robustness as compared to HC and SA. Furthermore, they also increase the

graph density as well as network’s connectivity with high computational cost. Fur-

thermore, we design a robust network to support the nodes’ functionality for the

topology optimization in the scale-free IoT networks. It is because the compu-

tational complexity of an optimization process increases the cost of the network.

Therefore, in this thesis, the main objective is to reduce the computational cost of

the network with the aim of constructing a robust network topology. Thus, four

solutions are presented to reduce the computational cost of the network. First,

a Smart Edge Swap Mechanism (SESM) is proposed to overcome the excessive

randomness of the standard Random Edge Swap Mechanism (RESM). Second, a

threshold based node removal method is introduced to reduce the operation of the

edge swap mechanism when an objective function converges at a point. Third,

multiple attacks are performed in the network to ﬁnd the correlation among the

x

measures, which are degree, betweenness and closeness centralities. Fourth, based

on the third solution, the Heat Map Centrality (HMC) is introduced that ﬁnds the

set of most important nodes from the network. The HMC damages the network by

utilizing the information of two positively correlated measures. It helps to provide

a good attack strategy for robust optimization. The simulation results demon-

strate the eﬃcacy of the proposed SESM mechanism. It outperforms the existing

RESM mechanism by almost 4% better network robustness and 10% less number

of swaps. Moreover, 64% removal of nodes helps to reduce the computational cost

of the network. In addition, we also perform topology optimization using a new

heuristic algorithm, named as Great Deluge Algorithm (GDA). Afterwards, four

rewiring strategies are designed. The ﬁrst strategy is based on the degree dissor-

tativity, which performs rewiring if maximum connectivity among similar degree

nodes is achieved. In second strategy, we propose a degree diﬀerence operation

using degree dissortativity to make sure that the connected edges possess low dis-

sortativity and degree diﬀerence. Whereas, the other two strategies consider nodes’

load capacity as well as improved GDA to maximize the network robustness. The

eﬀectiveness of the proposed rewiring strategies is evaluated through simulations.

The results prove that the proposed strategies increase the network robustness up

to 25% as compared to HC and SA algorithms. Besides, the strategies are also

very eﬀective in increasing the graph density and network connectivity. However,

their computational time is high as compared to HC and SA.

xi

TABLE OF CONTENTS

Acknowledgements vii

Conference Proceedings 99

Journal Publications 100

Abstract x

List of Figures xv

List of Tables xvii

1 Introduction 3

1.1 Introduction ............................... 4

1.1.1 Research Background ...................... 4

1.1.2 Problem Statement ....................... 5

1.1.3 Thesis Contributions ...................... 7

1.1.4 Organization of thesis ..................... 8

2 Literature Review 9

2.1 Literature Review ............................ 10

3 Modiﬁcation Strategies 19

3.1 Summary of the Chapter ........................ 20

3.1.1 Construction of a Scale-Free Network ............. 21

3.1.2 Attack Model .......................... 21

3.2 Multi-Population Entropy based Mechanism ............. 22

3.3 Proposed Edge Swap Mechanisms ................... 25

3.3.1 Eﬃciency based Edge Swap Mechanism ............ 26

3.3.2 Eﬃciency based Edge Swap Mechanism with Assortativity . 28

3.4 Performance Evaluation ........................ 29

3.4.1 Comparison of Network Robustness of Scale-Free Topologies

under Malicious Attacks .................... 30

3.4.2 Comparison of Graph Density of Scale-Free Topologies un-

der Malicious Attacks ...................... 33

3.4.3 Comparison of Robustness of Scale-Free Topologies with dif-

ferent Network Parameters under Malicious Attacks ..... 35

3.4.4 Comparison of Connectivity of Initial Topology and SAEA

under Malicious and Random Attacks ............. 36

3.4.5 Comparison of Computational Time of Scale-Free Topolo-

gies under Malicious Attacks .................. 36

3.5 Conclusion of the Chapter ....................... 38

4 Computationally Eﬃcient Topology Optimization 39

xii

4.1 Summary of the Chapter ........................ 40

4.2 Scale-Free Network Modeling ..................... 40

4.2.1 Construction of a Scale-Free Network ............. 40

4.2.2 Network Robustness Measure ................. 41

4.2.3 Network Optimization through Selection of Independent Edges 42

4.3 Computationally Eﬃcient Topology Optimization: Overview . . . . 43

4.3.1 Smart Edge Swap Mechanism ................. 44

4.3.2 Threshold based Node Removal ................ 46

4.3.3 Optimization of Network Considering Multiple Attacks . . . 47

4.3.4 Heat Map Centrality ...................... 48

4.4 Simulation Results and Discussion ................... 51

4.4.1 Power Law Distribution .................... 51

4.4.2 Measuring the Extent of Damage Caused by Each Attack . . 52

4.4.3 Execution Time of Diﬀerent Attacks ............. 53

4.4.4 Convergence of Robustness using Degree based Node Removal 54

4.4.5 Initial Robustness Evaluation considering Multiple Attacks . 55

4.4.6 Robustness Analysis using High Degree Node Attack and

HMC Attack .......................... 56

4.4.7 Swap Cost using High Degree Node Attack and HMC Attack 59

4.4.8 Topology Comparison ..................... 61

4.5 Conclusion of the Chapter ....................... 62

5 Topology Rewiring Strategies 63

5.1 Summary of the Chapter ........................ 64

5.2 Modeling the Topology of the Scale-Free Network .......... 64

5.2.1 Construction of Scale-Free Network Topology ........ 64

5.2.2 Attack Types and Robustness Metric ............. 65

5.3 Topology Optimization ......................... 66

5.3.1 Optimization Algorithms .................... 66

5.3.2 Edge Rewiring Strategies .................... 67

5.3.2.1 Rewiring Strategy using Degree Dissortativity . . . 67

5.3.3 Rewiring Strategy using Degree Diﬀerence Operation with

Dissortativity .......................... 69

5.3.4 Rewiring Strategy using Nodes’ Load Capacity ........ 71

5.3.5 Optimized Edge Rewiring Strategy using Nodes’ Load Ca-

pacity .............................. 73

5.4 Performance Evaluation ........................ 75

5.4.1 Comparison of Robustness under Malicious Attacks ..... 75

5.4.2 Comparison of Graph Density under Malicious Attacks . . . 77

5.4.3 Comparison of Connectivity under Malicious and Random

Attacks ............................. 78

5.4.4 Comparison of Computational Time of Diﬀerent Topologies

under Malicious Attacks .................... 80

5.4.5 Comparison of ROSE-DDO and RS-DDOD ......... 80

5.5 Conclusion of the Chapter ....................... 81

6 Conclusion and Future Work 82

xiii

LIST OF FIGURES

3.1 Nodes Joining Mechanism. ....................... 20

3.2 Steps Involved in Modeling the Entropy based GA. ......... 23

3.3 Comparison of Network Robustness of MPGA Entropy and MPGA

with Number of Iterations. ....................... 30

3.4 Comparison of Network Robustness of MPGA Entropy and MPGA

with Number of Nodes. ......................... 31

3.5 Comparison of Network Robustness of Scale-Free Topologies under

Malicious Attacks with respect to Number of Iterations. ....... 32

3.6 Comparison of Network Robustness of Scale-Free Topologies under

Malicious Attacks with respect to Number of Nodes. ........ 33

3.7 Comparison of Graph Density of Scale-Free Topologies under Ma-

licious Attacks. ............................. 34

3.8 Comparison of Robustness of Scale-Free Topologies with diﬀerent

Network Parameters under Malicious Attacks. ............ 35

3.9 Comparison of Connectivity of Initial Topology and SAEA under

Malicious Attacks. ........................... 36

3.10 Comparison of Connectivity of Initial Topology and SAEA under

Random Attacks. ............................ 37

3.11 Comparison of Computational Time of Scale-Free Topologies under

Malicious Attacks. ........................... 37

4.1 (a) Initial Topology (b) Swap 01 (c) Swap 02 ............. 42

4.2 Limitations Identiﬁed and their Proposed Solutions ......... 43

4.3 Pearson Correlation Bar Graph (Combined Attacks) ......... 49

4.4 Power Law Distribution ........................ 52

4.5 Extent of Damage ............................ 53

4.6 Execution Time (Single) ........................ 54

4.7 Execution Time (Combined) ...................... 55

4.8 Convergence of Initial Network Topology ............... 56

4.9 Robustness of Initial Network Topology ................ 57

4.10 Robustness Analysis (Degree) ..................... 58

4.11 Robustness Analysis (HMC) ...................... 59

4.12 Number of Swaps (Degree) ....................... 59

4.13 Number of Swaps (HMC) ....................... 60

4.14 (a) Initial Topology (b) Hill-Original (c) Hill-Smart (d) ROSE-

Original (e) ROSE-Smart ........................ 61

5.1 Onion-Like Structure. .......................... 65

5.2 Edge Rewiring Strategy. ........................ 71

5.3 Comparison of Network Robustness under Malicious Attacks with

respect to Number of Iterations. .................... 75

5.4 Comparison of Network Robustness under Malicious Attacks with

respect to Number of Nodes. ...................... 77

5.5 Comparison of Graph Density under Malicious Attacks. ....... 77

xv

5.6 Comparison of Connectivity under Malicious Attacks. ........ 78

5.7 Comparison of Connectivity under Random Attacks. ........ 79

5.8 Comparison of Computational Time under Malicious Attacks for

N=100. ................................. 79

5.9 Comparison of Robustness of ROSE-DDO and RS-DDOD. ..... 80

xvi

List of Abbreviations and Mathematical Symbols

ASO Angle Sum Operation

AI Artiﬁcial Intelligence

BA Barabasi-Albert

BP BackPropagation

CC Closeness Centrality

DAO Degree Associativity Operation

DDD Degree Diﬀerences using the Dissortativity

DDLP Deep Deterministic Learning Policy

DDO Degree Diﬀerence Operation

DD Degree Dissortativity

DDOD Degree Diﬀerence Operation with Dissortativity

DDLP Deep Deterministic Learning Policy

EES Eﬃciency based Edge Swap mechanism

GDA Great Deluge Algorithm

GA Genetic Algorithm

HC Hill Climbing

HDO High Degree Operation

HMC Heat Map Centrality

IoT Internet of Things

MCS Maximum Connected Subgraph

MPGA Multi-Population Genetic Algorithm

MA Multi Agent

ML Machine Learning

NF Node’s Farness

1Thesis by: Muhammad Awais Khan

NNF Node’s Neighbor Farness

ORS-Cap Optimized Rewiring Strategy using nodes’ load Capacity

RESM Random Edge Swap Mechanism

RS Rewiring Strategy

SA Simulated Annealing

SESM Smart Edge Swap Mechanism

VSet of Nodes

WSNs Wireless Sensor Networks

AAdjacency Matrix

rAssortativity

BetiBetweenness Centrality

CiCapacity of a node i

Π(i)Connection Probability of a node i

℘ki Connection Probability of a node i

kDegree of a node

mEdge Density

GGraph

BLower limit

βNodes’ Capability to process the load

σjk (i) Number of shortest paths between nodes jand kthat pass

through node i

NNumber of Nodes

αPower Law Exponent

RRobustness

sShortest Path

∆BUpper limit

2Thesis by: Muhammad Awais Khan

Chapter 1

Introduction

3

Chapter 1 Introduction

1.1 Introduction

This section consists of research background,problem statement,contributions

and organization of the thesis. The details are as follows.

1.1.1 Research Background

The Internet of Things (IoT) has become an essential technology nowadays [1–

3]. Its integration with the Wireless Sensor Networks (WSNs) [4–7] provides

good support to the research community. The IoT-WSNs [8–12] have various ap-

plications including healthcare [13–15], industrial [16], intelligent transportation

[17,18], smart environmental monitoring [19], smart cities [20,21], blockchain

[22–25] and underwater communications [26–30]. An important characteristic of

the IoT-WSNs is that they are operational even in hostile environments [31]. The

nodes in the WSNs are used for eﬃcient data delivery towards the destination

[32–34]. However, due to limited energy resources of the nodes [35–39], their com-

munication capability, lifetime [40–42], etc., are greatly compromised. In the IoT,

the nodes communicate through the Internet [43,44], however, the frequent cyber

attacks [45–50] on the network greatly aﬀect their connectivity and reduces the

network robustness. Therefore, the ultimate goal of the researchers is to provide

eﬀective ways to improve the network resiliency against the cyber attacks.

The network topologies [51,52] provide layouts of various communication activities

occurring inside the networks. The resilience of the network topologies against the

attacks depends upon the arrangement of the nodes present in the networks. The

robust network topologies maintain the connections of maximum number of nodes

in a subgraph after the node removal due to the cyber attacks. Diﬀerent types of

attacks occur in the networks. Two types of attacks are generally considered in

the networks: random and malicious [53]. The random attacks [54–56] target any

random node and remove it from the network, while the malicious attacks [57–

59] remove the most important node from the network. The nodes’ importance

is measured using degree, betweenness, etc. Thus, the malicious attacks have

greater aﬀect as compared to the random attacks. The attacks split the network

into multiple independent graphs [60] and paralyze the network with time.

A lot of researchers have put their eﬀorts into studying the properties of diﬀerent

networks such as wireless networks [61,62], social networks [63,64], body area

networks [65–67], vehicular networks [68–71], robotic networks [72,73]. Most

of these networks are complex networks [74–77] and have high importance due to

4Thesis by: Muhammad Awais Khan

Chapter 1 Introduction

their dense nature. A complex network theory [78–80] is one of the classic network

theories. It consists of two models, namely, small-world network [81,82] and scale-

free network [83,84] models. The small-world networks have two features, which

diﬀerentiate them from other networks. These features include a small average

path length and high clustering coeﬃcient [85]. Moreover, the properties of the

nodes in the networks are heterogeneous [86]. On the other hand, the nodes in

the scale-free networks are homogenous [87] and their degree distribution follows

the power-law [88]. It means that most of the nodes in the network are low

degree nodes while there are few nodes with high degree in the network. The

networks with high number of low degree nodes are robust against the random

attacks. However, these networks show vulnerability against the malicious attacks

due to presence of few number of high degree nodes. Therefore, the researchers

are focusing on the construction of robust scale-free network topology against the

malicious attacks.

Since, the attack on low degree nodes does not aﬀect the performance of the

network; therefore, the main concern of the research work is: how to make the

network robust, when the network faces malicious attacks? In that case, the high

degree nodes are isolated from the network and the performance of the network

tends to decline with the passage of time. Therefore, enhancing the network

robustness against the malicious attacks is a focal point of the research in the

scale-free networks.

The network robustness is measured through a metric proposed by Schneider et al

[89] by analyzing the behavior of the network during node removal. The research

studies [90–92] reveal that the network robustness can be improved through dif-

ferent optimization techniques. Therefore, the authors in [93] use edge rewiring

mechanism to construct a robust network topology. The proposed mechanism

achieves success in constructing an onion-like topology [94–97], which shows high

robustness against the malicious attacks. However, the proposed mechanism falls

into the local optima and reduces the robustness. Similarly, the edge rewiring

mechanism in [98] involves temperature parameter to modify the network topol-

ogy. However, it uses many redundant operations in the network and limits the

network robustness.

1.1.2 Problem Statement

In this thesis, the problems of constructing a robust scale-free network topology are

addressed so that it can tolerate the attacks. It is known that scale-free network

5Thesis by: Muhammad Awais Khan

Chapter 1 Introduction

topologies are vulnerable to malicious attacks. Therefore, many scale-free topolo-

gies are constructed including the one proposed in [99] where the authors overcome

the premature convergence in the existing Genetic Algorithm (GA) model. Due to

the premature convergence, similar solutions are produced in the network. Thus,

to avoid it, several populations are considered to bring diversity to the network.

However, the proposed mechanism does not consider degree distribution of nodes

in each population. Moreover, in [93,98], the authors use the edge swap mech-

anism to rewire the independent edges for the construction of robust scale-free

network topologies. They have checked the robustness for both alternative con-

nection methods. Still the robustness reduces because the proposed mechanisms

damage the property of the scale-free network by reducing the connections among

similar degree nodes. Besides, the degree to degree correlation is important as it

helps to construct a network with better onion-like topology. However, the previ-

ous mechanisms [93,98] reduce the robustness when nodes with diﬀerent degrees

are connected with each other, damaging the property of onion-like structure.

Most networks in real world have scale-free nature. Therefore, the nodes, which

act as hubs are considered the most important nodes in these networks and the

removal of these nodes create a serious threat to the connectivity of other nodes.

The cost of the network is important while performing topology optimization us-

ing the algorithms proposed in [93,98]. However, there are some other factors

that increase the cost of the network. For example, it is known that every objec-

tive function has a certain convergence limit and performing further optimization

always provides similar results. For topology optimization case, performing un-

necessary edge swaps may not provide an optimized network topology, except it

increases the cost of the optimization process. Also, the removal of nodes based

on degree and betweenness in [100] has shown success because of a strong positive

correlation between them. However, the betweenness centrality has high compu-

tational cost [101]. Moreover, making the network robust against diﬀerent types

of malicious attacks is a complex problem in the scale-free networks and the at-

tacker can attack the nodes having diﬀerent properties like degree, betweenness,

closeness, etc. In addition, the Degree Diﬀerence Operation (DDO) in [102] low-

ers the importance of the initial topology by ﬁxing the value of the threshold p,

which is used to limit DDO. It is because the initial topology always maintains

a high degree diﬀerence for a ﬁxed value of p. Thus, it can not be considered as

an optimal choice to improve the network robustness. Furthermore, the authors

in [93,98] do not consider nodes’ load capacity during the node removal process.

Thus, when the capacity of the node exceeds the maximum capacity limit, it fails.

The nodes’ failures in the network aﬀect the connectivity of other nodes and reduce

6Thesis by: Muhammad Awais Khan

Chapter 1 Introduction

the network robustness.

1.1.3 Thesis Contributions

The main contributions in this thesis are as follows.

1. A multi-population environment is considered called MPGA to reduce the

network complexity in achieving an optimal solution. The introduction of

an entropy based mechanism for the replacement of one’s bad solution with

other’s good solution increases the network robustness.

2. The fundamental edge swap mechanism has less robustness against the mali-

cious attacks because it does not consider nodes degree for rewiring. There-

fore, the Eﬃciency based Edge Swap mechanism (EES) is introduced to

increase the robustness of the network against the malicious attacks.

3. Considering the importance of onion-like structure, a new edge swap mecha-

nism is introduced based on the assortativity to make the topology onion-like.

4. Aiming to overcome the randomness of edge swap, a smart edge swap mech-

anism is proposed to evaluate the network robustness against the malicious

attacks.

5. A threshold based node removal method is introduced to reduce the com-

putational complexity of the network. It tackles the problem of performing

unnecessary topology optimization when a convergence is achieved.

6. For the construction of a robust topology, we consider three important cen-

tralities named as degree, betweenness and closeness. The Pearson correla-

tion coeﬃcient is used to ﬁnd two strong positively correlated measures that

can be used simultaneously.

7. Considering that multiple attacks can occur on the network, the topology is

optimized using a centrality measure named as Heat Map Centrality (HMC).

8. Considering that diﬀerent types of rewiring can construct an optimized scale-

free topology, the optimization algorithm GDA is introduced to increase the

network robustness.

9. As random edge rewiring increases the network dissortativity, a new edge

rewiring mechanism is proposed to construct a topology with low degree

dissortativity.

7Thesis by: Muhammad Awais Khan

Chapter 1 Introduction

10. Ensuring successful connections among similar degree nodes, we modify

DDO for the construction of robust onion-like topology.

11. Considering that the failures of nodes reduce the network robustness, a topol-

ogy is constructed, which ensures minimum nodes’ failures in the network

using the nodes’ load capacity.

12. Based on the improved performance of edge rewiring using the nodes’ load

capacity, we have redesigned the GDA method to increase the network ro-

bustness.

1.1.4 Organization of thesis

The remaining thesis is organized as follows. Chapter 2presents a detail overview

of the literature. Chapter 3,4and 5show the proposed strategies and their

simulation results. The conclusion and future work are given in Chapter 6.

8Thesis by: Muhammad Awais Khan

Chapter 2

Literature Review

9

Chapter 2 Literature Review

2.1 Literature Review

In this section, we discuss some of the researchers’ work in detail. Sohn et al.

[103] propose a new optimized mechanism rooted from artiﬁcial neural networks.

It takes the topologies of the scale-free networks as an input data and gives the

topologies of the hill climbing as an output data. A three-layer mechanism is

designed as a part of network operation, which consists of a single input layer,

multiple hidden layers and a single output layer. The network performs better

in terms of achieving high tolerance against the random and targeted attacks.

Nevertheless, it fails to provide the state of the art understanding for network

reconstruction. The work proposed in [104] selects two vertices randomly from

a given set of vertices, which are paired to form a link. After the establishment

of links, the network attains the shape of a complex network. Then, it picks up

diﬀerent combinations of vertices based on the probability distribution, which leads

to the emergence of diﬀerent complex networks. The proposed mechanism achieves

low computational complexity for generating the complex networks. However, this

type of practice cannot be applied to a network that incorporates extremely high

degree vertices, because in that case, it has a weak impact in increasing the network

eﬃciency for large scale networks. The authors introduce a mechanism in [105] for

the identiﬁcation of a sinkhole and the malicious attack. In this mechanism, the

responsibility of the data forwarding is done through a clustering mechanism [106].

Nodes’ practice during data routing is observed through a watchdog reputation.

Then the misbehaving nodes can rapidly be recognized and parted away from the

network. Its network eﬃciency is remarkable with less energy consumption, yet, it

has to prove its worth in dense environment, where the density of the nodes is high;

thus, it becomes diﬃcult for the watchdog reputation mechanism to overcome the

malicious attack.

The heuristic algorithms are used in the research to ﬁnd a solution to a problem.

The GA based schemes are presented that aims to ﬁnd an optimal solution in

the complex networks. The MPGA proposed in [107] overwhelm the premature

convergence and drive the network towards the higher robustness. In the MPGA,

the crossover operation and mutation are applied to a limited set of chromosomes

extracted from the adjacency matrix in order to converge the solution from local to

global optima. To further enhance the performance of MPGA, the authors in [108]

introduce the migration operator that selects the worst solution of a population

and replaces it with the good solution of the other population, which reduces

the premature convergence in the MPGA. The proposed methods attain eﬃcient

10 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

robustness in expense of low computational eﬃciency and high data overhead.

The proposed work in [109] includes the two heuristic strategies, CDA and DBA,

that exploit the information of community detection structure and the degree of

nodes severally. After utilizing the necessary information, an attack strategy is

implemented, called the GA-based-Q Attack, where the ﬁtness function is designed

using the modularity Q. The proposed GA-based attack minimizes the attack

probability for small area networks. Nonetheless, the problem for the large area

network is still confusing, which needs to be studied in order to obtain a robust

network.

Aiming to explore the invulnerability of the clustering in the networks to cascad-

ing, FU et al. [110] develop a model for WSNs, where they instigate two new

notions, i.e., “sensing load” and “relay load”. Their focus goes around the analy-

sis of the invulnerability to cascading failure in clustering WSNs, which includes

the random and the scale-free networks. Besides this, the network vulnerability

is improved by the division of the capacity expansion problem for the selection of

eligible nodes and the eﬃcient utilization of the resources. The proposed method-

ology does not consider the energy consumption, which is the main part of the

network enhancement. In contrast to this, the proposed capacity expansion can

only be applied to static WSNs, it could be quite tough for this model to achieve

success in the clustering WSNs with the nodes and sink mobility [111]. The pur-

pose of the work in [112] is to overwhelm the limitations of the Elephant Herding

Optimization (EHO) and raises the performance of the original EHO. For this rea-

son, the authors propose three novel algorithms, namely, “alpha-tuning, cultural-

based, and biased”. The alpha-tuning algorithm enhances the EHO operation by

continuously tuning the alpha value with the increasing number of iterations. The

cultural-based algorithm replaces the worst solution with a novel solution, which

originates randomly from a belief space. The biased algorithm initializes the ﬁrst

population to determine the good ﬁtness candidate. It forces the ﬁrst population

to obtain a minimum good ﬁtness population within the given threshold. These al-

gorithms work promise to attain eﬃcient robustness, however, further validations

are required to understand the latent eﬀects of the belief space.

The Ant Colony Optimization (ACO) is another useful technique for ﬁnding the

optimal path for the network robustness. A novel ACO based optimization scheme

in the heterogeneous environment is introduced by the Qiu et al. [113] to highlight

the global importance of the optimal paths to eﬃciently reduce the number of links

that emerged from a node towards the destination. The Small-world topology is

constructed with the advent of the shortest path, thus, reduces the average path

11 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

length, which at the same time also reduces the communication delay of the net-

work. The critical node is selected that acts as an endpoint for the added shortest

paths. It increases the nodes’ withstand capability in case of an attack at the

cost of the longer running time. To get an optimize solution for a problem and

improve the network robustness, the authors introduce the concept of ACO [114].

The motivation behind this work is to establish a quantitative model to produce

a feasible solution and increases its convergence speed. The proposed methodol-

ogy enhances the computational eﬃciency and the robustness of the network in

the sparse case, however, its performance in a large scale network has yet to be

explored.

Many complex networks introduce the edge swap mechanism for increasing the

network robustness. A Simulated Annealing (SA) algorithm proposed by Busser

et al. [98] uses the same concept of the edge swapping mechanism to make the

structure look like an onion keeping the degree distribution of the nodes unchanged

to improve the network robustness. SA optimizes the performance of the scale-

free network, even so, the algorithm causes high computational cost. Su et al.

[115] propose a mechanism to turn the non scale-free networks into the scale-free

networks by highlighting the importance of the preferential attachment in [116].

The strategy proposed in [117] introduces the betweenness centrality for checking

the node’s eligibility as a data forwarder. It calculates the betweenness centrality

of each node and sorts the degree of nodes in the decreasing order. Weights are

assigned to the nodes after ﬁnding the shortest path between any two neighbors

of the high degree node. The proposed link adding strategy proved to be eﬃcient

in terms of increasing the network robustness against the random attacks, still, it

becomes fragile when the network faces the malicious attack.

Community structural topology is a useful technique to ﬁnd the convergence of the

solution towards the global optima. In [118], Yang et al. propose a 3 steps strat-

egy for the community structure to improve the network robustness by keeping

in mind the degree distribution of the nodes constant. They allow each commu-

nity to exhibit an onion like shape through the edges swap, which allows only the

similar characteristics nodes to connect with each other. In addition, during the

malicious attack, the nodes prefer to connect with the high degree node in the

same community, which drives the network towards the higher robustness. The

proposed methodology considers the attack on the vertices, however, its eﬀects on

the edges is still a meaningful task to do. The authors in [119] measure the net-

work robustness by considering the attack on the nodes. A novel robustness index

is designed to check the nodes’ vulnerability to cascading failures, and the network

12 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

depletion against the link-based cascading failures. Based on their observed ex-

perimentation, their work provides a better solution for small area networks. Still,

for large area networks, their robustness suﬀers a downward trend, that is against

our observations. The proposed scheme in [120] considers the interdependent links

as one of the leading causes in the network fragility. Two optimized models are

designed to check the feasibility of a solution towards the higher robustness. These

are the generic edge swapping model and the optimized interdependent network

model. The proposed mechanism optimizes the network robustness against the

targeted links, nevertheless, its performance leans towards negligence when the

number of interdependent links is increased.

To generate the scale-free topology, a new modeling strategy is proposed in [102].

The proposed scheme considers the WSN constraints, i.e., arbitrarily large commu-

nication range of the nodes and the degree of the available nodes in the network.

It improves the robustness of the generated scale-free topology by exploiting the

node’s degree and the position information. Additionally, it rearranges the nodes

into an onion like structure in order to make the topology robust against the

malicious attacks. Meanwhile, it keeps the node’s degree same to keep the topol-

ogy scale-free. A Comparative analysis of the proposed scheme is also performed

against the existing robustness schemes. Simulation results validate the eﬃcacy

of the implemented scheme in counterpart, however, the proposed model is only

valid for the homogeneous scale-free network, its eﬀectiveness in terms of the het-

erogeneous environment is yet to be ﬂourished.

The proposed work in [121] develops a link strategy to mitigate the eﬀects of

a malicious attack on nodes. The authors observe that constructing a limited

cost dependent links are far better than the simple link adding strategy. The

results prove to be eﬀective in developing a robust network, still, the cost of

adding edges is the key problem that is to be considered. In order to enhance

the community robustness, a two-level learning strategy is introduced in [122],

which initializes the population to perform the crossover operation in the hunt for

a better solution in the global area without altering any change into the original

community structure. The prime aspect of the learning strategy is to circumvent

the intercommunity links, in case if these links dissipate, the performance of the

network declines sharply, leaving behind the isolated nodes. The edge swap criteria

assemble those important links to be constructed in the same communities. Thus,

when the community-level learning strategy is applied, the network leans an onion

like structure, which notably improves the network robustness. In spite of that,

the proposed strategy does not assure optimal routes for the case of oﬀspring

13 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

production, which can be put into the pitfall of the proposed strategy. In [123],

the authors propose an algorithm that considers the cost during the attack on high

degree nodes. The proposed algorithm ﬁnds the combination of nodes with the

minimum cost that helps to optimize the network performance. The algorithm is

based on a redesigned objective function to ﬁnd a better solution in case of the

malicious attack. It shows good performance in enhancing the network robustness

in case of a malicious attack. However, it does not tell whether the proposed

solution converges to a local optimum or a global, which is considered to be a

major obstacle in overcoming the premature convergence.

Aiming to explore the optimality in the network structure, the authors in [124]

have observed three strategies for checking the network behaviour, namely, ran-

dom rewiring, greedy rewiring and second neighbor rewiring. In random rewiring,

changing the edge of a node aﬀects the network robustness as every node has the

information of all other nodes. Introducing the greedy rewiring results in lim-

iting the connection of nodes, thus, it is not eﬀective in increasing the network

resilience. The second neighbor strategy is a good technique to be applied in the

case of node removal, still, it increases the data overhead of the network, which

is not acceptable. In order to mitigate the eﬀects of a malicious node, the work

presented in [125] holds the data of a malicious node. During the malicious attack,

the network identiﬁes the malicious node and stops forwarding the data of other

nodes towards that node. However, holding the forwarding process may cripple

the network because there might be some nodes that are only associated with this

node. Moreover, holding the data forwarding of these nodes increases the time

complexity of the network. The authors in [126] propose a two-step algorithm

namely: the detection of nodes and its optimization and secondly the nodes rein-

sertion. They also propose two strategies for the network robustness, named as

the static attack and the dynamic attack. However, due to the slow convergence

speed of the proposed mechanism in solving the problem, its performance in the

time complex network is ignored.

The proposed work in [100] is based on a fault-tolerant model, which shows high

network robustness against random node failure. However, it fails to improve the

network robustness against the malicious attacks. In [127], the authors analyze

the threat of malicious attacks on the Artiﬁcial Intelligence (AI) community and

inform that many algorithms involving Machine Learning (ML) are fragile against

malicious attacks. However, these algorithms do not ensure a reliable and robust

network. According to [128], the scale-free IoT networks show vulnerability to

malicious attacks. Thus, designing a robust mechanism against the attacks is

14 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

challenging. The previous optimization strategies have optimized the network

topology by maintaining the network connectivity; however, the computational

cost of the optimization strategies is relatively high. Due to the increasing demand

for IoT devices, increasing the network robustness against malicious attacks is one

of the challenging issues in the scale-free network [129].

In [130], the authors ﬁnd that the addition of the links increases the cost of the

network. The conventional Genetic Algorithm (GA) is a good example of an evolu-

tionary algorithm that optimizes the network robustness. However, the premature

convergence in GA reduces the exploration capability and lowers the performance

of the network against the malicious attacks [99]. A similar issue is raised in [131],

where the authors state that improving the topology of the scale-free network

against the malicious attacks is a complex problem. The authors in [132] address

the malicious attacks on a Multi Agent (MA) network. However, the research

focuses only on constructing a robust MA network without considering its cost.

The research in [133] proves that it is necessary to involve both cooperation and

robustness in constructing a robust network. However, this research is only lim-

ited to undirected network. The authors in [134] reveal that node attacks and

link attacks are negatively correlated. Therefore, multi-objective optimization is

a better choice in this case. However, the computational cost for calculating the

robustness of node and link attacks is diﬀerent due to the more number of links in

the network as compared to the nodes. Another attack strategy is introduced in

[135], which measures the impact of the intentional attacks and analyze that they

are harmful to the stability of the network. However, the optimization strategies

only deal with optimizing the network against one of those attacks.

According to [123], most attack strategies remove all the nodes according to a

speciﬁc order. However, in general, the attacker does not consider attacking the

nodes in a speciﬁc order. Moreover, removing high degree nodes from the network

costs more than removing low degree nodes. Therefore, controlling the network

robustness by considering the attack cost is a major problem in a network [136].

Furthermore, according to [137], the degree distribution of the nodes during the

attack process is dynamic as it changes with each attack.

The majority of the research studies in [138] test the network robustness against

the random node removal, however, in general, the attacker tries to attack the

most critical nodes in the network. Based on the analysis of [139], measuring the

network robustness against node and link removal is an open issue in the complex

15 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

networks. The former measures for network robustness including natural connec-

tivity, controllability robustness, etc., are based on edge and node’s connectivity,

size of the largest connected component, etc. However, these measures have failed

to express the network’s capability in preserving the connectivity of the network.

Due to the growing demand of the complex networks according to [140], there is

also a concern of security issues related to these networks. According to [141], the

link addition strategy increases the cost of the network and changes the degree dis-

tribution of the nodes in the network. To keep the degree distribution unchanged

and reduce the cost of the network, the edge swap mechanism is designed. How-

ever, the random edge swap mechanism increases the computational complexity

of the network and performs many redundant operations during the optimization

process.

According to [142], the assortativity rand the power-law exponent αare important

factors in the scale-free networks, which help to create a strong interaction between

the nodes. The value rclose to 1 tends to make strong interaction between the

nodes of similar degree. However, there is no evidence where the proposed model

highlights the correlation between Rand these measures. The literature work

in [143] reduce the cascading failure by considering the capacity of the nodes.

However, they fail to analyze its eﬀect on network recovery. According to [144],

the degree to degree correlation is an important factor for enhancing the robustness

of the network. However, the Newman’s research reveal that the enhancement of

degree to degree correlation is limited to a certain degree threshold under malicious

attacks.

Schneider et al. in [93] propose an eﬃcient rewiring mechanism based on the

robustness Rto make the network robust against the malicious attack. However,

further study reveals that the random rewiring mechanism uses the series of steps

for ﬁnding the network robustness, which increases the computational cost of the

network. The State of the Art Comparisons is described in Table 2.1.

16 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

Table 2.1: State of the Art Work

Limitations already

addressed Contribution already done Validation already done Limitations to be

addressed

The acceptance of solution

with the good ﬁtness value

makes the solution stuck in

the local optima [98]

Accepts the worst solution

with a speciﬁc probability to

converge the solution towards

the global optima

Network robustness is

increased High computational cost

The fault-tolerant model is

only robust against the

random attacks [100].

The network robustness

against the malicious attacks

is increased using two

operation: HDO and DAO

High robustness is achieved.

The convergence operation

slows down due to redundant

operations.

The degree of nodes and the

communication range of nodes

is not considered [102]

Considers both the degree of

nodes as well as the

communication range of nodes

Improves the network

resilience after the edges

removal

Time and cost complexity is

compromised

Computational complexity of

hill climbing [103] is high

Lower the computational

complexity on the basis of

adjacency matrix

Robust network and high

tolerance in case of the

random and the malicious

attack

Fails to provide state of the

art understanding for network

construction

Isolated nodes are added into

the network that increases the

computational complexity

[104]

Avoiding isolated nodes by

restricting their connection to

only one other vertices

Achieves low computational

complexity in enhancing the

network robustness

Cannot be applied to large

scale networks

Selective forwarding [105]Nodes forward the data to

selected cluster head Enhances network robustness

Fails to perform in dense

environment due to high data

overhead

Single population and

premature convergence in

traditional GA [107]

Multi-population GA is

applied to increase the

diversity in the solution space

Improves the network

robustness and the premature

convergence

High computational time is

involved in ﬁnding the desired

solution

Slow convergence of solution

in [108]

Introduces the immigration

operator to further improve

the premature convergence

Robustness is enhanced Computational time is

increased

Link deletion and addition

weaken the network

performance [109]

Link rewiring instead of

deletion and addition

Makes the network robust in

small area networks

Its performance is

compromised in large area

networks

Capacity expansion problem

[110]

Increasing the node capacity

by keeping in mind the

preferential attachment

Network eﬃciency is increased It can only be applied to static

WSNs

The random replacement of

the worst solution and the

ﬁxed parameters for all

iterations [112]

Replacing the worst solution

with the good one and tuning

the value of the alpha for each

iterations

Robustness of the network is

improved Time complexity is increased

The global optima problem

and the average path length

[113]

The ACO globally ﬁnds the

optimal path and reduces the

average path length

Enhances the network

robustness

Increases the computational

complexity of the network

The solution converges

towards local optima [114]

ACO with travelling salesman

problem is introduced to

converge the solution towards

the global optima

Makes the network robust and

decreases the computational

complexity

Fails to ﬁnd an optimal

solution in dense area network

Optimizing the network

topology is a major problem

[115]

Scale-free network is

constructed from non

scale-free network with the

help of non linear rewiring

method

Network robustness is

increased The data overhead is increased

The link selection with high

robustness is selected without

considering the path emerges

from that link [117]

Nodes betweenness centrality

is considered to minimize the

average path length

Increases the network

robustness against random

attacks

Fragile against the malicious

attack

Nodes deletion decreases the

network performance [118]

Considering the edge swap

without eﬀecting the degree

distribution

Network robustness is

improved High computational cost

The robustness metric Ris

introduced only for nodes

failures [119]

The new robustness metric R

is introduced for link

cascading failure

Makes the network robust in a

small area network

Robustness decreases for large

area networks

A small perturbation in the

network leads to network

failure due to the dependency

between the nodes [120]

Edge rewiring mechanism is

introduced to change the inner

structure of the network

Network robustness is

improved

Performance degrades with the

increase of interdependent

links

Same topology exists for all

types of attack [121]

Changes topology on the bases

of nature of the attack

Improves the network

resilience

Cost is involved in

construction of new edges as

well as the computational time

is increased

Change in the community

structure alters the network

topology and can damage the

network functionality [122]

Propose a new metric Rfor

preserving the community

robustness

Network robustness is

improved

Does not assure the optimal

route

17 Thesis by: Muhammad Awais Khan

Chapter 2 Literature Review

Limitations already

addressed Contribution already done Validation already done Limitations to be

addressed

More nodes are aﬀected due to

the high degree attack in many

heuristic techniques [123]

Uses the high degree attack

with the cost to minimize the

aﬀect of the high degree attack

Makes the network robust

against the attack

Fails to provide convergence of

solution from local to global

optima

Experimental validations of

both the random and the

malicious attack have not done

simultaneously on same

strategies [124]

It validates the eﬀectiveness of

the proposed solution with

both the attacks to compare

the nodes’ performance

Makes the system robust

against the targeted attack Increases the network overhead

Selective attack and reliability

is compromised [125]

Overloaded nodes are

controlled by selective drop

and defective links are

disabled to improve reliability

Enhances the network

robustness Network delay is increased

Targeting the high degree

nodes is a NP hard problem

and its eﬀects do not remain

the same, therefore, it is

diﬃcult to measure [126]

Makes the optimization

problem combinatorial and

solves the issue using the

global information of nodes

Improves the network

robustness

Slow convergence speed and

high computational complexity

Vulnerability of scale-free

network to malicious attack

[128]

Uses back-propagation neural

network to optimize the

network topology

Accuracy of the network is

improved and loss function is

minimized

Time consuming and

additional learning features

are required for training

Rcannot be optimized further

[129]

Optimizing the value of R

through deep neural network

training

The value of Ris optimized Higher network complexity

due to complex training

Cost of the network increases

for high degree node attack

[136]

Modes of attacks are changed

based on attack cost

The validity of the network is

done using s(q) and network

eﬃciency

Optimization after node

removal is missing

Degree distribution of nodes

during the attacks is dynamic

[137]

Eight attack strategies are

introduced

Network performance is

validated based on the extent

of the attack

Optimization is missing

Network is robust for random

node removal [138]

Construct a robust network

based on high degree node

removal

The performance of the

network is validated by

considering the network

robustness for both in and out

degree nodes

Hard to implement in real

scenario

The cost for evaluating the

robustness for nodes and link

removal is high [139]

Protect the link whose removal

maximizes the RGvalue

The validation is done on

diﬀerent datasets

Slow convergence of GA

increases the time complexity

of the network

Attack cannot be limited to

nodes [140]

Consider link-attack strategies

on k-core network

The shell-max strategy

validates the eﬀectiveness of

the proposed model

The optimization of network

robustness after the attack is

not done in k-core

decomposition

Removal of nodes in a

community impacts the

structure[141]

Preferential rewiring method

is applied to preserve the

community structure of the

network

The performance of the

network is validated on both

synthetic and real-world

networks

Redundant operations

increases the complexity of the

network

The previous BA model ﬁxed

the value of aand rand

generated a scale-free model

[142]

The proposed scheme focuses

on tuning the value of a and r

for both malicious and random

attacks.

Optimization of the network is

missing.

The eﬀectiveness of the

proposed model is validated

through the malicious and

random attack on both nodes

and links

Not enough evidence to show

the impact of resource

allocation on the performance

of the network [143].

Highest capacity node is

removed from the network and

the recovery process recover

those nodes whose recovery

timing reaches the threshold

Feasible only for static

environment

The resilience loss is used as a

recovery metric to quantify

the recovery process of the

network.

Enhancement of degree to

degree correlation is limited to

a certain degree threshold

under malicious attack [144].

Increases the network’s

robustness by considering the

information of loops in the

network.

Addition of edges increases the

network cost.

The network’s performance is

validated suing R, FVS, and r

18 Thesis by: Muhammad Awais Khan

Chapter 3

Modiﬁcation Strategies for Scale-Free IoT

Networks

19

Chapter 3 Modiﬁcation Strategies

3.1 Summary of the Chapter

In this chapter, two diﬀerent mechanisms are adopted for the construction of a

robust scale-free network. First, a Multi-Population Genetic Algorithm (MPGA)

is considered to overcome the premature convergence in the GA and optimize

the network robustness against the malicious attacks. Then, an entropy based

mechanism is used, which replaces the worst solution of one population with the

best solution of other population to improve the network robustness. Second, two

types of edge swap mechanisms are introduced. The eﬃciency based edge swap

mechanism selects the pair of edges with high eﬃciency to increase the network

robustness. The second edge swap mechanism based on assortativity transforms

the network topology into an onion-like structure to achieve maximum connectivity

between the similar degree nodes in the network. The optimization of the network

robustness is performed using Hill Climbing (HC) and Simulated Annealing (SA)

methods. The simulation results show that the proposed MPGA Entropy has

9% better network robustness as compared to MPGA. Moreover, the proposed

edge swap mechanisms have eﬀectively increased the network robustness with an

average of 15% better robustness as compared to HC and SA.

i

jk

l

7

3

5

Figure 3.1: Nodes Joining Mechanism.

20 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

3.1.1 Construction of a Scale-Free Network

Due to the limited communication range of nodes in the WSNs [145], the newly

added node may not have enough neighbors. Thus, the preferential attachment

property [145][146–149] is not applicable for the networks having limited commu-

nication range. In the preferential attachment, the high degree nodes have high

probability to become the neighbors of a newly added node. Thus, large num-

ber of connections makes the network more dense and this is the reason why the

importance of scale-free networks is increasing day by day. A scale-free network

consists of an undirected graph G= (V, E ). Where Vdenotes the set of nodes

and Eis the set of edges in the graph [102].

The network modeling starts with a few number of connected nodes, which are

initially deployed in the network. The newly added node selects the highest degree

node as its neighboring node in the network. The connection probability ℘ki of a

neighbor node iis deﬁned in [102] and mathematically, it can be written as:

℘ki=ki

Pn

q=1 kq

.(3.1)

From Equation (4.1), kiindicates the degree of a node iand kqsigniﬁes the overall

degree of the neighbouring nodes of i, whereas, nis the number of nodes in the

network. Considering the model in [102] where a newly added node iwants to

establish a connection with one of the nodes in its communication range. Suppose,

j,kand lare the nodes in the communication range of a node i. The degrees of

these nodes are 7, 5 and 3, respectively, as shown in Figure 3.1. The connection

probabilities of the nodes are calculated using Equation (3.1), which are 0.3888,

0.2777 and 0.1666, respectively. The selection of the neighbor nodes for a node i

is based on their edge densities represented as m. Roulette wheel method [102] is

adopted for the selection of nodes’ neighbor, which generates random numbers in

the range 0 to 1. The nodes with large number of connections have high probability

of being selected as they occupy large space on the roulette wheel.

3.1.2 Attack Model

The scale-free networks have high resilience against the random attacks, however,

they are vulnerable to malicious attacks. Therefore, the aim of the topology

optimization is to construct a network, which shows high robustness against the

malicious attacks. This reason is that the removal of high degree nodes eﬀects the

connections of most nodes in the network.

21 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

For the evaluation of network robustness, the equation used in [102] is adopted,

which is given as:

R=1

N+ 1

N−1

X

n=0

MCSn

N.(3.2)

From Equation (3.2), M CSnsigniﬁes the total number of nodes in the maximal

connected subgraphs in response of eliminating the nth highest degree node from

the network. The factor 1

N+1 is utilized to set the value of the robustness in

the range of 0 to 1. However, the maximum value of the network robustness is

restricted to 0.5.

3.2 Multi-Population Entropy based Mechanism

In this section, an entropy based model is proposed for increasing the robustness

of the scale-free networks. The entropy is the measure of the uncertainty in the

degree information of the nodes in each population. Besides, the major problem in

the conventional GA is the occurrence of the premature convergence, which pro-

duces similar solutions in the networks. The authors in [107] propose MPGA using

several populations to overcome the premature convergence in the network. Each

population in MPGA produces diﬀerent solutions and brings diversity in the pop-

ulation. However, the proposed MPGA fails to analyze the probability of nodes

having diﬀerent degrees in each population during the replacement operation. If

the population with high probability of diﬀerent degree nodes are considered dur-

ing the replacement operation, the network robustness can be increased because

this population produces large varieties of solutions in the networks.

In the topology optimization, the probabilities of diﬀerent degrees nodes decide

the importance of a topology in the scale-free networks because of having a large

varieties of solutions. Thus, the entropy is used to ﬁgure out the probability of

diﬀerent degrees nodes in diﬀerent populations. At the beginning, diﬀerent in-

dividuals are evolved to bring diﬀerent solutions in the networks. Later on, the

crossover is performed between the two ﬁttest individuals in a population. The

crossover brings diversity by searching the local solutions in the network. Among

the solutions, some are identical and the process of the crossover brings no change

in the network. Therefore, the mutation is performed to lead the solution towards

the global optima. The entropy [150] of the population is calculated after the

mutation and before the replacement operation and its general formula is given in

Equation (3.3):

22 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

Step 1: Scale-Free Topology Step 2: Multi-Population Environment

Step 3: Crossover

Step 4: Mutation

Step 5: Replacement

Step 6: Termination

Ends the operation if

global solution is achieved

If H(X) is greater than H(Y), replace

the worst solution of H(Y) with good

solution of H(X)

Figure 3.2: Steps Involved in Modeling the Entropy based GA.

H(P) = −X

k

pPklog2(pPk).(3.3)

23 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

Where pPkdeﬁnes the probability of nodes of degrees kin a population Pand

H(P) deﬁnes the entropy of a population P.

The entropy of each population is calculated by ﬁnding the probability of diﬀerent

degree nodes in the network. After the evaluation of entropy, the immigration

or replacement operator is used, which replaces the worst solution of low entropy

population with the best solution of high entropy population to increase the solu-

tion diversity. The high entropy population has better solutions as compared to

the low entropy population because the low entropy population provides low di-

versity [151]. The network model of the GA is shown in Figure 4.2. The operation

of MPGA is referred from [108] and the steps involved in implementing the GA in

the multi-population environment are described as follows.

Population Initialization: The ﬁrst step in GA is the selection of the chro-

mosomes, which are selected from a set of individuals called a population. The

chromosomes are the set of possible solutions for each node and they deﬁne the

connections of nodes with other nodes. In GA, the set of solutions is represented by

string 0 and 1 where 1 deﬁnes the node’s connection with other node and 0 shows

no connection. For population initialization, several things should be considered

when dealing with the GA. First, the diversity of the population is important as

low diversity might lead to premature convergence. The premature convergence

is a state where the solution converges without bringing optimal solutions in the

network. Second, the size of the population must be kept normal. A large popula-

tion might slow down the performance of the GA while a small population might

not bring optimality in the network.

Selection of Fittest Individual: The selection of the ﬁttest individuals is im-

portant to continue the process of evolution in GA. The ﬁttest individual plays a

vital role in bringing optimality in the network. In GA, the ﬁttest individuals are

selected through roulette wheel method [108].

Crossover Operation: The crossover is applied on the ﬁttest individuals, which

are selected from each population to bring diﬀerent solutions in the population.

The ﬁttest individuals are selected to generate the topology of the child. The

child retains some part of the topology from both ﬁttest individuals. We consider

an example of crossover from [108] where the parent 1 topology has an exclusive

edge E12, which is not present in the parent 2 topology. Similarly, the parent 2

topology’s exclusive edge E34 does not belong to the topology of the parent 1. The

crossover generates the topology of parent 1 in parent 2 by replacing the edges E38

24 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

and E47 with E34 and E78. The details of the mechanism are already discussed in

[108].

Mutation Operation:In some scenarios, the topologies obtained from the crossover

operation are identical, which bring premature convergence in the network. The

mutation is performed to overcome the premature convergence as it brings diver-

sity in the population. Thus, the topology obtained from the mutation is diﬀerent

from both parents [108], as shown in Figure 3.2.

Use of Entropy in Replacement Operator: After the implication of GA, the

topologies obtained from two populations are compared based on their entropy.

Considering a population where diﬀerent degrees nodes are present. Suppose the

minimum degree of a node in a population is 1 and the maximum degree is 5.

Assuming the probabilities of the nodes having degree information in the range

1-5 are given as:

p11=7

14, p12=2

14, p13=3

14, p14=1

14, p15=1

14.(3.4)

The above calculation in Equation (3.4) is performed for a single population

(P= 1). The same steps are followed for P= 2 as well. A population with

a high entropy is selected as a better topology because its population has more

diverse solutions as compared to low entropy population. The replacement oper-

ator is introduced into the network, which replaces the worst solution from the

population of low entropy with the best solution from the population of high en-

tropy. The complete step by step process of GA with the entropy based selection

is described in Figure 3.2 and the total entropy across (P= 1) is calculated in

Equation (3.5):

H(P= 1) = −7

14log2(7

14)−2

14log2(2

14)−3

14log2(3

14)

−1

14log2(1

14)−1

14log2(1

14)=1.9213.

(3.5)

3.3 Proposed Edge Swap Mechanisms

In this section, considering the importance of edge swap mechanisms in [93,98],

two edge swap mechanisms are proposed to construct a robust topology against the

malicious attacks. The topologies presented in [93,98] select a random pair of edges

from the network. These pair of edges are swapped in both ways in search of an

optimized scale-free topology. The nodes related to the chosen edges are swapped

to maintain the connectivity of the nodes in the network. However, the proposed

25 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

random edge rewiring produces low robustness. Therefore, this chapter aims to

accomplish high network robustness through more precise selection of edges. The

details of the proposed edge swap mechanisms are discussed underneath.

3.3.1 Eﬃciency based Edge Swap Mechanism

In [93,98], the authors use the edge swap mechanism to rewire the independent

edges from a topology. The robustness is checked for both alternative connec-

tion methods. However, the mechanism fails to consider nodes’ degrees during

the edge swap, thus, it can remove the connections between similar degree nodes

and damage the property of an onion-like structure. Therefore, an Eﬃciency

based Edge Swap Mechanism (EESM) is proposed by maintaining the connections

between similar degree nodes to increase the network robustness against the mali-

cious attacks. The entropy is used to analyze the uncertainty in the nodes’ degree

information of the selected pair of independent edges in a chance to increase the

network robustness. To ﬁnd how accurate the type of rewiring is, the eﬃciency

of the nodes associated with the edges needs to be evaluated before the swap.

Initially, for calculating the eﬃciency, the random probability function rand(1, N)

assigns random values to all nodes present in the network. The random function

is used to reduce the computational complexity of the EESM and make the op-

timization process highly rely on the degree information of the nodes. With the

function, only the entropies of the nodes associated with the selected edges are

evaluated. The details of the proposed edge swap mechanism are discussed below.

Suppose the randomly generated probabilities of the nodes i,j,kand lare 0.034,

0.013, 0.026 and 0.004, respectively. The entropy is calculated using HT1, which

can be calculated in Equation (3.6):

HT1=−X

q

pqlog2(pq).(3.6)

Here, HT1denotes the uncertainty in the degree of the nodes for the edges (i, j)

and (k, l) in the initial topology and qdeﬁnes the node. For the initial topology,

the entropies for the edges (i, j) and (k, l) are 0.2743 and 0.1688, respectively. The

total entropy H(T1) across the edge swap is 0.4431. Similarly, for the edges (i, k)

and (j, l), the entropies are 0.3028 and 0.1133, respectively and the total entropy

H(T2) is 0.4341. Also, for the edges (i, l) and (j, k), the entropies are 0.1977 and

0.2183, respectively and the total entropy H(T3) is 0.4160. The expected degree

length LT1[150] is calculated as:

26 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

LT1=X

q

kq∗pq.(3.7)

From Equation (3.8), kqis the node’s degrees of the selected independent edges of

the initial topology. The eﬃciency of the edge η[150] is measured in percentage

and is calculated from Equation (3.8):

ηT1=HT1

LT1

∗100.(3.8)

Using η, the eﬃciency of all possible pairs of independent edges are calculated. The

pair of edges having high eﬃciency among other swap pairs is selected as a robust

swap for improving the network robustness. The process of EES is described in

Algorithm 1. Here, we only describe the process of edge swap mechanisms as the

rest of the processes are already described in [93,98].

Algorithm 1 EESM

Input: Adjacency Matrix (A), Set of edges (E), Graph (G)

Output: A

1: procedure EESM(A)

2: for all edges in Edo

3: Assign the random probabilities to all nodes in N

4: Select two independent edges and mark them as eij and ekl

5: Calculate HT1,LT1and ηT1

6: Remove edges eij and ekl from A. Add eil and ejk in A1

7: G1←Gand A1←A

8: Remove edges eij and ekl from A. Add eik and ejl in A2

9: G2←Gand A2←A

10: Calculate the edge eﬃciency ηT2,ηT3and ﬁnd ηmax=max(ηT1,ηT2,ηT3)

11: if R(A1)≥R(A) &&ηT2== ηmax then

12: A←A1

13: else if R(A2)≥R(A) && ηT3== ηmax then

14: A←A2

15: end if

16: end for

17: end procedure

In Algorithm 1, the random probabilities are assigned to all nodes in the network.

Two random edges are selected in the network, which are marked as ei,j and ek,l

(Line 4). For the nodes of the selected edges, the entropy H, length Land eﬃciency

ηfor the initial topology are calculated (Line 5). Moreover, the eﬃciency of all

the possible pairs of the edges is also calculated (Line 11). A pair of edges with

high eﬃciency is selected and the topology is updated accordingly (Lines 11-15).

27 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

3.3.2 Eﬃciency based Edge Swap Mechanism with Assor-

tativity

The assortativity is one of the important factors, which reduces the impacts of

malicious attacks on the scale-free networks. In assortativity, similar degree nodes

are connected with each other and the functionality of the nodes is handled by their

neighboring nodes when they fail. In the scale-free networks where the malicious

attacks occur on high degree nodes, the importance of assortativity is high. It is

because when a high degree node is removed from the network, its neighboring

node replaces its functionality to maintain the connectivity of the nodes. Thus,

the impacts of malicious attacks on the scale-free networks are greatly reduced.

This is the main reason why onion-like structure shows better network robustness

against the malicious attacks because the high degree nodes are connected with

each other in the structure.

Mathematically, the assortativity is denoted with rand is calculated as [100]:

r=PikiPij Aij kikj−(Piki2)2

PikiPiki3−(Piki2)2.(3.9)

From Equation (3.9), kdenotes the degree of a node and Ai,j is the adjacency

matrix (i, j = 1,2,3, ..., N ) [100]. The value of rranges from -1 to 1. When

r > 0, the similar degree nodes are connected with each other making the network

assortative. For r < 0, diﬀerent degree nodes are connected with each other

making the network dissortative. For r= 0, the network is neither assortative nor

dissortative.

The proposed schemes in [93,98] consider changing the network topologies to make

them onion-like. However, they do not consider the assortativity while altering the

connections between the nodes. Therefore, in our proposed Eﬃciency based Edge

Swap Mechanism with Assortativity (EESM-Assortativity), we modify the process

of EES by introducing the assortativity in the network. A pair of independent

edges is selected from the network, namely, eij and ekl and the eﬃciency of the

selected pair of edges and assortativity of the network topology are calculated. The

edges are swaped by removing eij and ekl and replacing them with eil and ekj . In an

alternate swap, the edges eij and ekl are replaced with eik and ejl . The eﬃciency

and the assortativity of the topology are evaluated for both swaps. The swap

which improves the robustness as well as increases the eﬃciency and assortativity

of the network is considered as a robust swap for topology optimization.

28 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

Algorithm 2 EESM-Assortativity

Input: Adjacency Matrix (A), Set of edges (E), Graph (G)

Output: A

1: procedure EESM-Assortativity(A)

2: for all edges in Edo

3: Assign the random probabilities to all nodes in N

4: Select two independent edges and mark them as eij and ekl

5: Calculate rG,HT1,LT1and ηT1

6: Remove eij and ekl from A. Add eil and ejk in A1

7: G1←Gand A1←A

8: Remove edges eij and ekl from Aand add edges eik and ejl in A2

9: G2←Gand A2←A

10: Calculate the edge eﬃciency ηT2,ηT3and ﬁnd ηmax=max(ηT1,ηT2,ηT3)

11: Calculate rG1and rG2

12: Find r=max(rG, rG1, rG2)

13: if r=rG1&& R(A1)≥R(A) && ηT2== ηmax then

14: A←A1

15: else if r=rG2&& R(A2)≥R(A) && ηT3== ηmax then

16: A←A2

17: end if

18: end for

19: end procedure

In Algorithm 2, the r,H,Land ηare calculated for initial topology (Line 6).

Then the pair of edges with high eﬃciency and assortativity is selected and the

topology is updated accordingly in (Lines 14-20).

3.4 Performance Evaluation

In this section, ﬁrst the important parameters are listed, which are used in the

simulation of the scale-free network topologies. The parameters are shown in

Table 4.1. Then, the performances of our proposed topologies are compared with

diﬀerent scale-free network topologies. The performance evaluation is divided into

two parts. In ﬁrst part, the performance of MPGA Entropy is compared with

MPGA to show the importance of Entropy in the multi-population environment.

In second part, the performance of our proposed EESM and EESM-Assortativity

is evaluated using HC and SA algorithms. The proposed schemes, which consider

EESM in HC and SA are denoted as HCE and SAE, respectively. On the other

hand, the EESM-Assortativity based schemes are named as HCEA and SAEA,

respectively. MATLAB 2018a is used for the simulations on a laptop having Intel

Core i7 with 4GB RAM.

29 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

Table 3.1: Network Parameters used in Simulations

Parameters Values

Number of Nodes 100

Maximum Degree 25

Network Area 500 ×500 (m2)

Transmission Range 200 m

Edge Density (m) 2

maxiter 100

3.4.1 Comparison of Network Robustness of Scale-Free Topolo-

gies under Malicious Attacks

For robustness analysis, MPGA [107] is used as a comparison scheme for MPGA

Entropy. For EES case, we compare the proposed edge swap mechanisms with HC

and SA in a single-population environment. Initially, 100 nodes are considered in

the network. Later on, the nodes are increased from 100-250 nodes. A maximum

100 iterations are performed for the simulations.

0 20 40 60 80 100

Number of Iterations

0.1

0.15

0.2

0.25

0.3

Robustness

Figure 3.3: Comparison of Network Robustness of MPGA Entropy and

MPGA with Number of Iterations.

In the ﬁrst scenario, as shown in Figure 3.3, our proposed MPGA Entropy shows

9% higher robustness as compared to MPGA. The use of entropy increases the

diversity of the solutions in the network because the worst solution of low entropy

population is replaced with the best solution of high entropy population. However,

30 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

no such phenomenon exists in MPGA; thus, its robustness is low. At the start,

MPGA has better robustness; however when the number of iterations increases,

the eﬀectiveness of the proposed MPGA Entropy increases. It proves that MPGA

converges too early as compared to proposed MPGA Entropy. Thus, MPGA

Entropy overcomes the premature convergence eﬀectively as it ﬁnds more optimal

solution through wide varieties of solutions. Moreover, the results in Figure 3.4

prove the eﬀectiveness of MPGA Entropy for high node density as well.

100 150 200 250

Number of Nodes

0

0.05

0.1

0.15

0.2

0.25

0.3

Robustness

MPGA

MPGA Entropy

Figure 3.4: Comparison of Network Robustness of MPGA Entropy and

MPGA with Number of Nodes.

The results in Figure 3.5 show that the proposed HCE and SAE have high robust-

ness against the malicious attacks as compared to HC and SA, respectively. One

of the major issues in HC is the number of redundant operations in the network.

There are some pair of edges, which do not improve the network robustness. Due

to randomness of the edge swap in the HC, these edges are selected again during

the iteration process. Therefore, HC fails to overcome the redundant operations

in the network. On the other hand, SA reduces the redundancy by marking the

pair of edges, which are selected during the optimization process. Thus, it has

high robustness as compared to HC. However, still the optimization of network

robustness in SA is too slow and it does not provide global solutions in the given

time of frame. Moreover, SA only considers improving the network robustness by

changing the connections of edges. Other factors like assortativity, degree diﬀer-

ence, etc., are neglected during the optimization process, thus, results in reduction

31 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

of the network robustness. In addition, SA does not consider nodes’ degree during

the edge swap operation and the edge swap process changes the property of the

onion-like structure. Therefore, the consideration of eﬃciency in HCE and SAE

for ﬁnding the optimal number of swaps in the network increases the network

robustness.

HCE and SAE consider constructing the topology with a better degree distribu-

tion, which helps to make the network robust against the malicious attacks. Over-

all, the proposed mechanisms HCEA and SAEA outperform all other mechanisms

because they consider eﬃciency and assortativity to make connections between

similar degree nodes. SAEA has high eﬃcacy and is considered the best among

all the proposed schemes for constructing a robust network topology. The use of

assortativity helps to select the topology, where the connections between similar

degree nodes are high. Thus, the eﬀects of malicious attacks are greatly reduced

when high degree nodes are removed from the network. This is because the other

high degree nodes in the network maintain the functionality of the removed nodes

after the malicious attacks. Thus, the topology shows better robustness by main-

taining the connections of most of the nodes in the subgraph. Moreover, the

eﬃcacy of the proposed SAEA is also tested on diﬀerent nodes’ density and the

results in Figure 3.6 show that SAEA has better network robustness against the

malicious attacks at low and high nodes’ density.

0 20 40 60 80 100

Number of Iterations

0.1

0.15

0.2

0.25

0.3

Robustness

Initial

HC

SA

HCE

SAE

SAEA

HCEA

Figure 3.5: Comparison of Network Robustness of Scale-Free Topologies under

Malicious Attacks with respect to Number of Iterations.

32 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

100 150 200 250

Number of Nodes

0

0.05

0.1

0.15

0.2

Robustness

Initial

HC

SA

HCE

SAE

HCEA

SAEA

Figure 3.6: Comparison of Network Robustness of Scale-Free Topologies under

Malicious Attacks with respect to Number of Nodes.

3.4.2 Comparison of Graph Density of Scale-Free Topolo-

gies under Malicious Attacks

The graph density is the ratio of the number of edges to the maximum possible

number of edges in the graph G. Mathematically, it is written as:

GraphDensity =2E

N(N−1).(3.10)

The graph density given in Equation (3.10) is directly related with the network

robustness. The higher graph density shows that a large number of nodes are

connected with each other. A network must maintains high connectivity among

nodes in order to make itself robust against the malicious attacks. Figure 3.8 shows

the graph density comparison of the proposed topologies with HC and SA. We have

performed 100 simulations to get the graph density of the mechanisms. SAEA has

the highest graph density value because it has better maintained the connectivity

between the nodes as compared to other schemes. If the connectivity between

the nodes is high, the eﬀects of the malicious attacks are greatly reduced and the

topology shows high robustness against the attacks. Moreover, for large number

of connections in the topology, the number of nodes in the maximum connected

subgraph also increases. The connections of more nodes in the graph increases the

graph density. The other reason that increases the graph density is the inclusion

33 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

HC

HCE

HCEA

Initial

SA

SAE

SAEA

0

0.01

0.02

0.03

0.04

0.05

Graph Density

Figure 3.7: Comparison of Graph Density of Scale-Free Topologies under

Malicious Attacks.

of assortativity in SAEA, which connects similar degree nodes with each other.

Thus, it constructs a better onion-like topology. Similarly, HCEA, HCE and SAE

outperform the scale-free topologies, which are based on HC and SA. The eﬃciency

based edge selection in HCE and SAE provides more robust topologies. Also, the

consideration of global information of the network using assortativity in HCEA

and SAEA makes the topology onion-like as it connects similar degree nodes with

each other. On the other hand, HC has lower graph density among other schemes

because of poor selection of edge swap and large number of redundant operations

during the optimization process. HC does not consider the degree information

of nodes while performing edge swap operation. Thus, the nodes tend to loose

connections with each attack and the network damages quickly as compared to

others.

Table 3.2: Network Parameters

Number of

Nodes (N)

Network

Field

(m2)

Maximum

Degree

Communication

Range (m)

50 50 x 50 10 25

100 100 x 100 20 50

150 150 x 150 30 75

34 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

3.4.3 Comparison of Robustness of Scale-Free Topologies

with diﬀerent Network Parameters under Malicious

Attacks

To verify the performance of our proposed mechanisms, we evaluate the robustness

of the network by considering diﬀerent network parameters. We vary the number

of nodes, network ﬁeld, maximum degree of nodes and their communication range

to analyze the robustness of the scale-free network topologies. The network pa-

rameters are given in Table 3.2. From Figure 3.8, it is obvious that the robustness

of the network is high when the number of nodes is set to 50. The small networks

have high robustness against the malicious attacks. This is because the robustness

is inversely related with the number of nodes present in the network. Therefore,

lowering the number of nodes increases the robustness. In all cases, our proposed

SAEA outperforms SA and HC, which shows the eﬀectiveness of the eﬃciency and

assortativity for making the network robust against the malicious attacks. The

eﬃciency based mechanism in SA selects pair of nodes with better degree distri-

bution as compared to the initial topology and the assortativity in SAEA is used

to construct an onion-like topology.

50 100 150

Network Parameters

0

0.05

0.1

0.15

0.2

Robustness

Initial

HC

SA

HCE

SAE

HCEA

SAEA

Figure 3.8: Comparison of Robustness of Scale-Free Topologies with diﬀerent

Network Parameters under Malicious Attacks.

35 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

3.4.4 Comparison of Connectivity of Initial Topology and

SAEA under Malicious and Random Attacks

We analyze the connectivity of our proposed scale-free topology SAEA by conduct-

ing malicious and random attacks. The results are simulated over 100 iterations.

From Figure 3.9, the connectivity of SAEA under malicious attacks is better as

compared to initial topology, which shows the eﬀectiveness of the edge swap mech-

anism performed in SAEA. For the case of random attacks as shown in Figure 3.10,

the initial topology and SAEA show high robustness against the random attacks,

which shows that both topologies are scale-free. Moreover, when the number of

removed nodes increases the subgraph size decreases. It shows that the connec-

tivity of the network is greatly eﬀected when more nodes are removed from the

network.

0 20 40 60 80 100

Number of Malicious Attacks

0

20

40

60

80

100

Maximum Connected Subgraph Size

Initial

SAEA

Figure 3.9: Comparison of Connectivity of Initial Topology and SAEA under

Malicious Attacks.

3.4.5 Comparison of Computational Time of Scale-Free

Topologies under Malicious Attacks

Figure 3.11 shows the evaluation of the proposed SAEA with HC and SA in terms

of computational time. It shows that HC has the lowest computational time

because it does not store the information of the previous solution thus, it does

not perform any extra calculation at each step. SA stores the information of

36 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

0 20 40 60 80 100

Number of Random Attacks

20

40

60

80

100

Maximum Connected Subgraph Size

Initial

SAEA

Figure 3.10: Comparison of Connectivity of Initial Topology and SAEA under

Random Attacks.

previous solution as it accepts the worst solution and tries to make it better thus,

its computational time is high as compared to HC. However, our proposed scheme

SAEA evaluates the eﬃciency of the edges as well as the network assortativity at

each edge swap. Thus, its computational time is high as compared to HC and SA.

HC SA SAEA

0

2

4

6

8

10

12

14

Computational Time (secs)

Figure 3.11: Comparison of Computational Time of Scale-Free Topologies

under Malicious Attacks.

37 Thesis by: Muhammad Awais Khan

Chapter 3 Modiﬁcation Strategies

3.5 Conclusion of the Chapter

The study of entropy in WSNs is not new. However, when focusing on improving

the robustness, a good choice is to perform topology optimization with entropy.

Therefore, this chapter has considered the concept of entropy in multi-population

and single population environments for ﬁnding the optimal solution in the scale-

free networks. The entropy in multi-population is useful during the replacement

of the worst solution of one population with the best solution of other population

as it helps to increase the network robustness. Moreover, the selection of edges

based on their eﬃciency makes the network robust. Also, the assortativity helps to

construct an onion-like topology. The network robustness using the proposed edge

swap mechanisms is optimized through HC and SA. The simulations results have

shown the adequacy of the proposed network topologies in terms of improving the

network robustness against the malicious attacks. Therefore, the proposed modiﬁ-

cation strategies are vital in maintaining the connectivity of the nodes during the

malicious attacks, which is proved from the result of the graph density. However,

the proposed edge swap mechanism SAEA has high computational time and it is

a trade-oﬀ in this chapter.

38 Thesis by: Muhammad Awais Khan

Chapter 4

Computationally Eﬃcient Topology

Optimization for Scale-Free IoT Networks

39

Chapter 4 Computationally Eﬃcient Topology Optimization

4.1 Summary of the Chapter

In this chapter, four solutions are presented to reduce the computational cost

of the optimization process in the scale-free IoT networks. First, a Smart Edge

Swap Mechanism (SESM) is proposed to overcome the excessive randomness of the

standard Random Edge Swap Mechanism (RESM). Second, a threshold based node

removal method is introduced to reduce the operation of the edge swap mechanism

when an objective function converges at a point. Third, multiple attacks are

performed in the network to ﬁnd the correlation between the measures, which are

degree, betweenness and closeness centralities. Fourth, based on the third solution,

a Heat Map Centrality (HMC) is used that ﬁnds the set of most important nodes

from the network. The HMC damages the network by utilizing the information

of two positively correlated measures. It helps to provide a good attack strategy

for robust optimization. The simulation results demonstrate the eﬃcacy of the

proposed SESM mechanism. It outperforms the existing RESM mechanism by

almost 4% better network robustness and 10% less number of swaps. Moreover,

64% removal of nodes helps to reduce the computational cost of the network.

4.2 Scale-Free Network Modeling

In this section, we discuss the construction of a scale-free network, its robustness

measure and the independent selection of edges from the network.

4.2.1 Construction of a Scale-Free Network

The authors consider a BA model [116] that utilizes the information of the ini-

tially deployed nodes to construct a scale-free network topology. The preferential

attachment property of the BA model allows the newly added nodes to make con-

nections with the high degree nodes in the network. However, due to the limited

transmission range, the newly joined nodes have limited neighbors in their commu-

nication range. Also, it is important for the nodes in the network to have suﬃcient

neighbors in their communication range due to the growing demand of dense net-

work topologies in the future. The ROSE emphasizes the importance of the dense

WSNs and takes into account the communication range of nodes in the network.

The communication range in ROSE allows the nodes to connect with 50% of the

nodes in the network, making it a dense scale-free network. Moreover, the ROSE

analyzes that for limited transmission range, the division of a network into multi-

ple clusters is a good choice to develop a robust network. Therefore, the following

40 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

aspects of ROSE are considered in the proposed model for the construction of the

scale-free network.

1. The preferential attachment property of a node is limited to its nodes, which

are within its communication range.

2. Considering the limited resources of the nodes in IoT-WSNs, their maximum

degree is limited to a certain threshold.

3. The high degree nodes must be located in the center of the network.

4.2.2 Network Robustness Measure

In the scale-free networks, the attacker can attack the nodes as well as the links

to destroy the connectivity of the nodes in the network. Generally, the attacks

can be random or malicious. The random attacks remove random nodes while

the malicious attacks remove the most important nodes from the network. In

the scale-free networks, we use malicious attacks, which remove the high degree

nodes and damage the connectivity of the network. Initially, the degrees of nodes

are calculated and the node with the highest degree is removed. Also, the edges

connected with the node are also removed. Then, the degree of the nodes is

recalculated and the highest degree node is removed again. The process is repeated

many times until all nodes are removed from the network.

For calculating the network robustness, a metric Rproposed by Schneider et al.

[93] is used based on percolation theory. When a node is removed from the net-

work, the graph is divided into multiple subgraphs. The connectivity of the nodes

is checked and the subgraph where the nodes are maximally connected is con-

sidered for the evaluation of R. We take the mathematical equation from [102]

for evaluating the robustness, which provides the information of the nodes in the

maximal connected subgraphs after removing nth nodes from the network. The

equation for evaluating the robustness in [93] also provides the number of nodes

information in the maximal connected subgraphs, however, it considers the frac-

tion of nodes, which needs to be removed in order to disconnect the entire network.

Both are similar in terms that they both provides the information of the network

connectivity after repeated removal of nodes in the network. The equation for

evaluating the network robustness [102] is given as follows.

R=1

N+ 1

N−1

X

n=0

MCSn

N.(4.1)

41 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

i

j

lk

(a)

i

j

lk

(b)

i

j

lk

(c)

Figure 4.1: (a) Initial Topology (b) Swap 01 (c) Swap 02

From Equation (4.1), Ndenotes the total number of nodes and M CSndenotes

the maximal connected subgraphs after nth highest degree node removal from the

network [102].

4.2.3 Network Optimization through Selection of Indepen-

dent Edges

In the scale-free networks, the optimization is performed through edge swap mech-

anism by selecting two independent edges from the graph G= (V, E). Where, V

represents the set of nodes and Erepresents the set of edges. The two selected

edges are said to be independent if they lie within the communication range of

each other and there is no extra connection between these two edges. Figure 1a

shows that ei,j and ek,l are the independent edges. Figure 1b and Figure 1c show

the edge swap performed on these independent edges.

The optimization of the network robustness against the malicious attacks is evalu-

ated by swapping the independent edges in the network. The edges are swapped in

such a way that the updated topology increases the network robustness against the

malicious attacks. If the ﬁrst swap increases the network robustness, the topology

is updated. If the ﬁrst swap has low robustness value, the second swap is per-

formed and the topology is updated only if it increases the robustness. If both

swaps fail to optimize the network robustness, the original topology is considered

in the network.

42 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

L1

S1

Independent Edges Selection

Selection of Nodes based on Importance

Edge Swap Process

Nodes’ Removal Process

Random Selection of Edges Unnecessary Optimization

Smart Selection of Edges S2 Threshold based Node Removal

Robustness Calculation

L3 L4

L2

Multiple Malicious Attacks Betweenness Centrality

increases Computational Cost

S3 S4

Combined Attacks are

considered HMC is considered

i

jlk

i

jlk

i

jk

l

Figure 4.2: Limitations Identiﬁed and their Proposed Solutions

4.3 Computationally Eﬃcient Topology Optimiza-

tion: Overview

This section describes our proposed topology optimization mechanism where we

identify four limitations. Each limitation is associated with the optimization of

the network robustness in the scale-free network, as shown in Figure 4.2. The

limitations are denoted as L1, L2, L3 and L4, while their proposed solutions are

provided using S1, S2, S3 and S4, respectively. Table 4.1 shows the mapping

of these limitations with their proposed solutions and validations. L1 and L2

mentioned in Table 4.1 are associated with the limitations in the previous edge

swap mechanism based on redundancy and computational cost of the network.

These limitations are tackled using SESM and threshold based node removal,

respectively. The validation for both these solutions is done using R, number of

swaps, MCS, etc.

For L3, the issue of multiple attacks on the network is tackled using S3, where a

combined attack strategy of the two strongly correlated measures is needed. In

contrast, L4 is associated with ﬁnding an attack measure to damage the network’s

connectivity in quick time, as mentioned in Table 4.1. Therefore, S4 introduces a

measure named as Heat Map Centrality (HMC) to overcome L4. We discuss the

solution of each limitation in the given subsections.

43 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

Table 4.1: Limitations Identiﬁed, Proposed Solutions and Validations

Limitations Identiﬁed Solutions Proposed Validations Done

L1: Random selection of

independent edges increases

the computational cost of

the network

S1:SESM reduces the

randomness through a

smart selection of edges

V1: The performance of the

network is validated

through R(Figure 4.10 and

Figure 4.11) and number of

swaps (Figure 4.12 and

Figure 4.13)

L2: Unnecessary removal of

nodes after the convergence

is achieved increases the

computational overhead

S2: We analyze the

robustness value and set a

threshold for node removal

to reduce the

computational overhead

V2: The eﬃcacy of the

network is validated using

MCS/N for node removal

in the network (Figure 4.5)

L3: Multiple attacks can

happen on the network

simultaneously

S3: Finding the two

strongly correlated

measures to make the

network robust against

multiple attacks

V3: The validation is

provided using Pearson

correlation coeﬃcient

(Figure 4.4), execution time

(Figure 4.6 and Figure 4.7)

and R(Figure 4.9)

L4: Finding the set of most

inﬂuential nodes in the

network that can damage

the network in less time is a

challenging task

S4: HMC reduces the

computational cost of the

network by damaging the

network to a greater extent

V4: The performance

parameters used for

validation are R

(Figure 4.11) and number

of swaps (Figure 4.13)

4.3.1 Smart Edge Swap Mechanism

Due to the involvement of randomness in the edge swap mechanism used in HC

and ROSE, many redundant operations are generated in the optimization of the

network robustness. Speciﬁcally, in HC, the edge swap mechanism increases the

number of redundant operations in the network because it does not mark the

independent edges after their selection. Thus, these edges are selected again in

the optimization process, which results in increasing the number of redundant

operations in the network. In ROSE, the marking of independent edges reduces the

redundancy of the network. However, the random edge swap mechanism happens

on low degree edges in the network, which results in providing low robustness

against the malicious attacks with the high computational cost. Therefore, a new

selection criteria for independent edges is required to overcome the redundancy

issue and increase the network robustness. It is known that the high degree nodes

are the main target of the attacker and the removal of high degree nodes damages

the topology of the network. Therefore, based on the information of the high degree

nodes, one can protect the connectivity of the nodes by altering their connections.

Furthermore, it is understood that one high degree node replaces other high degree

node in the network. Thus, changes in the connections of these nodes can bring a

44 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

signiﬁcant improvement in the network. Besides, an onion-like structure has strong

tolerance against the malicious attacks and in the structure, the high degree nodes

are tightly connected with other high degree nodes. Therefore, the edge swap

mechanism based on high degree nodes is a good choice to construct an onion-like

structure.

For L1, as shown in Table 4.1, the problem of edge randomization is controlled

through the selection of high degree nodes. The SESM is proposed to overcome

the random selection of the independent edges in the previous Random Edge Swap

Mechanism (RESM). For the initial topology, a set of high degree nodes is selected

from the network. From the set, two high degree nodes are selected before per-

forming the edge swap mechanism. The information of the neighboring nodes of

these two selected nodes is extracted. The selection process of ﬁnding a node

from the neighbors’ set is a complex problem as each node has multiple neighbors.

Therefore, to avoid this problem, a random neighbor is selected from the neigh-

bor’s set. The information of the selected neighbor is utilized for the selection of

independent edges. The independence of the selected edges is checked. If they

are independent, the edge swap operation is performed, else the selection process

continues to try further connections to ﬁnd the independent edges. The edge swap

mechanism swaps the edges of the network in search for a more optimized network

topology.

Algorithm 3 Smart Edge Swap Mechanism

1: procedure Smart Edge Swap Mechanism(A)

2: Input: A,N,G

3: for all N∈Gdo

4: Find a high degree node from N

5: Calculate neighbors of the high degree node and mark this node as i

6: Pick a neighbor randomly from the neighbors of node iand mark it as

j

7: Perform steps 3-5 again for 2nd high degree node kand its neighbor l

8: Swapcounter = 0

9: if (i, j) and (k, l) are pairs of independent edges in the set Ethen

10: Perform optimization using HC and ROSE

11: if swap is successful then

12: Update Aand G

13: Calculate R

14: Swapcounter=Swapcounter + 1

15: end if

16: end if

17: end for

18: end procedure

45 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

Algorithm 3describes the process of SESM. The high degree node is selected from

graph G, which consists of Nnumber of nodes (Line 4). The neighbor of the

selected node is chosen and the link between them is marked (i, j) (Line 6). The

steps 3-5 are followed for other high degree nodes (Line 7). The swap counter is

initialized (Line 8). If the selected edges (i, j ) and (k, l) are independent, the edge

swap mechanism is performed. For optimization, the operation of HC and ROSE

are used (Line 10). If the swap is successful and the robustness is increased, the

swap counter is incremented (Line 14).

4.3.2 Threshold based Node Removal

Several mechanisms including HC and ROSE increase the network robustness by

focusing on changing the network topology through swapping. Due to the struc-

tural complexity of the network, optimizing the network robustness using an edge

swap becomes a diﬃcult task. Moreover, there is not enough evidence to guide

the network to perform limited edge swaps. Besides, it is understood that the

performance of the network is greatly reduced when a speciﬁc optimization task

is performed continuously without any improvement. In the optimization pro-

cess, analyzing the convergence of an objective function is an important factor. It

is useless to perform unnecessary optimization for an objective function after its

maximum value is achieved. In the topology optimization scenario, the objective

is to maximize the network robustness by swapping the edges of the topology un-

til a single node is left in the network. However, this type of process consumes

excess memory and increases the computational cost of the network. Therefore,

based on these problems, a threshold based node removal method is considered

as mentioned in Table 4.1. The method considers removing the nodes one by one

until convergence is achieved. The details of the proposed solution are described

below.

Consider a network whose topology is constructed using the previous BA model.

The preferential attachment property of the scale-free network guides the newly

added node to connect with high degree nodes in the network. These nodes are

added into the network one by one until a topology of the scale-free is generated.

The network robustness is calculated for initial topology by removing the nodes one

by one. For node removal, we have performed several experiments and found out

that almost 60-65% node removal is enough to destroy the network’s connectivity.

The 60-65% node removal guides us to select a threshold value for node removal,

where the robustness value reaches its maximum. Therefore, the node removal is

performed based on the given threshold. For each node removal, the edge swap

46 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

mechanism swaps the independent edges within the given threshold. The topology

that maximizes the Rvalue is considered for the construction of a robust scale-free

network.

Algorithm 4 Threshold Based Node Removal

1: procedure Threshold Based Node Removal(A)

2: Input: A,N,G

3: for all N∈Gdo

4: for k= 1 : N−1do

5: Find a high degree node ifrom topology G

6: Remove the node iand update the topology from Gto G2

7: Calculate MCS and evaluate network robustness Ri

8: if Rk> Rk−1then

9: Continue the removal process

10: else if Rk==Rk−1then

11: Calculate the value for node removal where Rk==Rk−1

12: Update the threshold for node removal and perform optimization

using Algorithm 3 with the selected node removal phase

13: end if

14: end for

15: end for

16: end procedure

In Algorithm 4, the threshold based node removal is discussed. The high degree

node is removed from graph G(Line 6). The robustness Ris calculated for each

node removal (Line 7). If the value of Rkis greater than the value of Rk−1, the node

removal process is repeated again until all nodes are removed from the network.

If Rkis equal to the previous value Rk−1for consecutive node removal steps, the

threshold value is recalculated. The optimization is performed using Algorithm 1

with the node removal at the given threshold (Line 12).

4.3.3 Optimization of Network Considering Multiple At-

tacks

The authors in HC and ROSE have analyzed the network robustness against high

degree node removal. They consider that the degrees of the nodes can provide

more inﬂuential nodes from the network. However, the research performed in

[100] has termed betweenness centrality as another metric to measure the most

important nodes from the network. Therefore, in [100], the authors have combined

both measures to ﬁnd the attack probability of nodes. Still, they have failed to

provide enough evidence about the importance of both these measures in terms

47 Thesis by: Muhammad Awais Khan

Chapter 4 Computationally Eﬃcient Topology Optimization

of computational cost. The work proposed in [101] has discussed both these mea-

sures in terms of computational cost. The authors have considered the degree of

nodes as an excellent parameter to ﬁnd the local information of nodes. However,

choosing betweenness centrality is not the best option because it increases the

computational cost of the network [101].

To determine the relationship between any two measures, the Pearson correlation

coeﬃcient is used, which is a well-known correlated measure. In our scenario,

initially, three attacks named as degree, betweenness and closeness are induced.

Then, based on these attacks, three robustness measures are calculated. After-

wards, the Pearson correlation coeﬃcient between the robustness of any two cen-

trality measures is evaluated using the following formula.

r=N(PRC1RC2)−(PRC1)(PRC2)

p[N(PR2

C1)−(PRC1)2][N(PR2

C2)−(PRC2)2],(4.2)

From Equation (4.2), RC1and RC2are the evaluated robustness for any two cen-

trality measures. Figure 4.3 shows the correlation comparison of the three central-

ity measures. From Figure 4.3, the strong positive correlation between degree and

closeness attacks shows that both these measures can be considered simultane-

ously in an attack to improve the robustness. The idea of ﬁnding the correlation

between the measures is adopted from [130], where the authors use two strong

negatively correlated measures for multi-objective optimization. The optimiza-

tion of both the measures are necessary to increase the robustness. Contrary to

the aforementioned case, the strong positively correlated measures are combined

together to improve the robustness. <