Content uploaded by Nadeem Javaid

Author content

All content in this area was uploaded by Nadeem Javaid on Oct 19, 2021

Content may be subject to copyright.

COMSATS University Islamabad

Enhancing robustness of scale-free IoT networks

against random and malicious attacks (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 Usman

CIIT/FA18-REE-016/ISB

Spring, 2021

ii

Enhancing robustness of scale-free IoT networks

against random and malicious attacks (MS Thesis

without Source Codes)

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

Engineering as partial fulﬁllment of the requirement for the award of Degree of

MS (Electrical Engineering).

Name Registration Number

Muhammad Usman CIIT/FA18-REE-016/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

Enhancing robustness of scale-free IoT networks against random

and malicious attacks (MS Thesis without Source Codes)

By

Muhammad Usman,

CIIT/FA18-REE-016/ISB

has been approved

For the COMSATS University Islamabad, Islamabad

External Examiner:

Dr. Ataul-Aziz Ikram

Professor, Department of Electrical Engineering,

FAST-NU, Islamabad

Supervisor:

Dr. Nadeem Javaid

Associate Professor, Department of Computer Science,

COMSATS University Islamabad, Islamabad

Co-Supervisor:

Dr. Sardar Muhammad Gulfam,

Assistant Professor, Department of Electrical and Computer Engineering,

COMSATS University Islamabad, Islamabad

HoD:

Dr. Shurjeel Wyne

Associate Professor, Department of Electrical and Computer Engineering,

COMSATS University Islamabad, Islamabad

iv

Declaration

IMuhammad Usman (Registration No. CIIT/FA18-REE-016/ISB) hereby declare

that I have produced the work presented in this thesis, during the scheduled period

of study. I also declare that I have not taken any material from any source except

referred to wherever due that amount of plagiarism is within acceptable range. If

a violation of HEC rules on research has occurred in this thesis, I shall be liable

to punishable action under the plagiarism rules of the HEC.

Date: July 09, 2021

Muhammad Usman

CIIT/FA18-REE-016/ISB

v

Certiﬁcate

It is certiﬁed that Muhammad Usman (Registration No. CIIT/FA18-REE-016/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: July 09, 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

DEDICATION

Dedicated

to my mentor loving Parents, family members and to my Supervisor

Dr. Nadeem Javaid, who motivated and guided me, when I needed.

vii

ACKNOWLEDGEMENT

I am very grateful to Almighty Allah, without His help nothing is possible.

Whenever I feel discouraged, helpless and worried, Allah helps me.

I feel great pleasure in expressing my profound and heartiest gratitude to my super-

visor Dr. Nadeem Javaid and co-supervisor Dr. Sardar Muhammad Gulfam, for

their indispensable guidance, deep consideration, aﬀection and active co-operation

that made possible this work to meet its end successfully well in time.

I would also like to thank all of the members of the ComSens family. Their

company helped me to develop the research attitude and become familiar with the

research.

In the end, I would like to thank all my family members. Their support and trust

helped me to achieve new goals.

viii

ABSTRACT

Enhancing robustness of scale-free IoT networks against

random and malicious attacks (MS Thesis without Source

Codes)

During the past few decades, the Internet of Things (IoT) has made remark-

able progress in many real-world applications including healthcare, military, trans-

portation, etc. Multiple sensor nodes are deployed in these ﬁelds to get the re-

quired data. Diﬀerent network topologies are used in IoT and scale-free is one

of them. It is mostly preferred due to its robust behavior against random node

removal, however, the network collapsed because of malicious attacks. Therefore,

in this thesis, robustness of the scale-free networks is enhanced against malicious

attacks through optimization. To achieve this, the edge’s degree and nodes’ dis-

tance based edge swap operations are used in the proposed Improved Scale-Free

Networks (ISFNs) scheme. In the edge’s degree based operation, nodes of similar

degrees are linked. Moreover, the connections of the nearest nodes are made in

distance based edge swap. These operations help to achieve a better onion-like

structure without changing the degree distribution of the network. Therefore, the

network becomes robust against malicious attacks. Moreover, no new links or

nodes are added in the optimization process, therefore, no extra cost is incurred.

Furthermore, to make the network more robust against realistic attacks, the vari-

able attacks are considered. Simulation results of the proposed scheme are com-

pared with ROSE and Simulated Annealing (SA) for diﬀerent number of nodes.

The proposed scheme outperforms the existing techniques for diﬀerent numbers

of nodes and against the low degree, high degree and random attacks. Moreover,

ISFNs has 13% and 23% better network robustness as compared to ROSE and SA,

respectively.Network Topology Evolution Scheme (NTES) is proposed to prevent

the scale-free networks from random and malicious attacks. In this scheme, the

network ﬁeld is divided into two parts with uniformly distributed nodes. After the

network’s evolution, the nodes are linked with each other through one-to-many

correspondence. The division of the network ﬁeld is made by considering that a

network is robust if its size is small. Moreover, to study the hierarchical changes

in the degree of nodes, k-core decomposition is used. In addition, nodes’ degrees

and core based attacks are performed on the network to evaluate the performance

of the proposed scheme. Furthermore, the network robustness is analyzed using

three optimization techniques: Artiﬁcial Bee Colony (ABC), Bacterial Foraging

Optimization (BFO) and Genetic Algorithm (GA). The techniques are compared

with each other and a technique that eﬃciently optimizes the network to increase

ix

the robustness is selected. In the optimization process, we make use of three

edge swap methods. Due to the edge swap, the network robustness is enhanced

without changing the degree distribution, so the addition of nodes/links is not re-

quired to increase the robustness. Furthermore, NTES is compared with Barab´asi

Albert (BA) model and Hill Climbing (HC) algorithm against random and ma-

licious attacks. The simulation results show that the proposed NTES optimized

using GA outperforms BA and HC by 46.90% and 57.08%, respectively, in terms

of robustness.In addition, the network robustness of Scale-Free Networks (SFNs)

is enhanced against the malicious attacks. For that purpose, initially, a parame-

terless optimization algorithm JAYA is used because it requires less computational

eﬀorts as compared to the heuristic techniques. Then, as the edge swap plays an

important role to enhance the robustness of SFNs, therefore, the edge swaps are

classiﬁed into three categories. For each category, eﬀects on the network’s topolog-

ical parameters such as average shortest path length, assortativity and clustering

coeﬃcient are analyzed. Next, the robustness is enhanced with the addition of

nodes in the maximum connected subgraphs and the protection of bridge edges

maintain the network connectivity. Moreover, optimized network is analyzed for

diﬀerent attack strengths. In simulations, the comparison of JAYA is made with

two existing algorithms: ROSE and Simulated Annealing (SA). The network op-

timized by JAYA has a better robustness against random and malicious attacks,

as compared to the existing algorithms. Furthermore, among the edge swap cat-

egories, the degree dependent edge swap is better to increase the robustness of

SFNs. Moreover, the addition of nodes into the maximum connected subgraphs

enhances the robustness and the protection of bridge edges ensures the network

connectivity in all the algorithms. Furthermore, the robustness against diﬀerent

attack strengths are analyzed and the results show that high attacks strength

paralyzed the network more eﬃciently.

x

Conference Proceedings

1Usman, M., Javaid, N., Abbas, S. M., Javed, M. M., Waseem, M. A., &

Owais, M. (2021, July). A novel approach to network’s topology evolution

and robustness optimization of scale free networks. In Conference on Com-

plex, Intelligent, and Software Intensive Systems (pp. 214-224). Springer,

Cham. Download

2 Abbas, S. M., Javaid, N., Usman, M., Baig, S. M., Malik, A., & Rehman,

A. U. (2021, July). An Eﬃcient Approach to Enhance the Robustness of

Scale-Free Networks. In International Conference on Innovative Mobile and

Internet Services in Ubiquitous Computing (pp. 76-86). Springer, Cham.

Download

3Usman, M., Javaid, N., Khalid, A., Nasser, N., & Imran, M. (2020, June).

Robustness Optimization of Scale-Free IoT Networks. In 2020 International

Wireless Communications and Mobile Computing (IWCMC) (pp. 2240-

2244). IEEE. Download

xi

TABLE OF CONTENTS

Dedication vii

Acknowledgements viii

Abstract ix

Conference Proceedings 95

List of Figures xv

List of Tables xvii

1 Introduction 1

1.1 Introduction ............................... 2

1.1.1 Details of scale-free networks .................. 2

1.1.2 Contributions .......................... 4

1.1.3 Organization of thesis ..................... 5

2 Literature review and problem statement 6

2.1 Literature review ............................ 7

2.2 Problem statement ........................... 18

2.2.1 Details of problem statement ................. 18

3 Edge swap based optimization strategy to enhance the robustness of

Scale-Free Networks (SFNs) 19

3.1 Summary of the chapter ........................ 20

3.2 Generation of scale-free networks ................... 20

3.3 Network model description ....................... 21

3.3.1 Independent edges ....................... 22

3.3.2 Edges’ degree based edge swap ................ 22

3.3.3 Nodes distance based edge swap ................ 23

3.3.4 Measuring network robustness ................. 24

3.3.5 Variable attacks ......................... 24

3.4 Details of ISFNs algorithms ...................... 25

3.5 Simulation results ............................ 29

3.5.1 Construction of scale-free network ............... 29

3.5.2 Comparison of Improved Scale-Free Networks (ISFNs) with

ROSE and Simulated Annealing (SA) ............. 29

3.5.3 Comparison of ISFNs with ROSE and SA against low degree

attacks .............................. 30

3.5.4 Comparison of ISFNs with ROSE and SA against high de-

gree attacks ........................... 31

3.5.5 Comparison of ISFNs with ROSE and SA against random

attacks .............................. 31

xii

3.5.6 Comparison of ISFNs with ROSE and SA against variable

attacks .............................. 32

3.5.7 Network robustness against diﬀerent number of nodes . . . . 33

3.6 Conclusion of the chapter ....................... 34

4 Network’s topology evolution scheme 35

4.1 Summary of the chapter ........................ 36

4.2 Scale-free model ............................ 36

4.2.1 Construction of Scale-Free Network .............. 36

4.2.2 Details of edge swap ...................... 37

4.2.2.1 Edge swap of randomly selected nodes ....... 38

4.2.2.2 Edge swap of degree based selected nodes ..... 38

4.2.2.3 Edge swap of distance based selected nodes . . . . 39

4.2.3 Metric of robustness ...................... 39

4.3 Network Topology Evolution Scheme overview ............ 40

4.3.1 Evolution of network topology ................ 40

4.3.2 k-core based nodes’ degree distribution ............ 41

4.3.3 Attacks on the proposed topology ............... 41

4.3.4 NTES’s optimization by heuristic algorithms ......... 42

4.4 Details of NTES algorithms ...................... 43

4.5 Simulation results and discussion ................... 46

4.5.1 Network topology evolution .................. 47

4.5.2 Attacks on upper and lower networks ............. 47

4.5.3 Networks connection by one-to-many correspondence . . . . 49

4.5.4 Core based attacks on network ................. 50

4.5.5 Network robustness against random and malicious attacks . 50

4.5.6 Comparison of NTES robustness against diﬀerent optimiz-

ing techniques .......................... 51

4.6 Comparison of NTES and other algorithms on Scale-Free Topoogy

(SFT) topologies ............................ 52

4.7 Conclusion of the chapter ....................... 54

5 Optimization of scale-free networks 55

5.1 Summary of the chapter ........................ 56

5.2 Overview of JAYA ........................... 56

5.3 JAYA for the scale-free networks ................... 57

5.3.1 Metric of robustness ...................... 58

5.3.2 Scale-free model ......................... 58

5.3.3 Details of the algorithms .................... 59

5.4 Classiﬁcation of edge swap ....................... 60

5.4.1 Random edge swap ....................... 62

5.4.2 Degree dependent edge swap .................. 63

5.4.3 Distance dependent edge swap ................. 63

5.4.3.1 Global communication eﬃciency .......... 64

5.4.3.2 Average clustering coeﬃcient ............ 64

5.4.3.3 Average shortest path length ............ 64

5.4.3.4 Assortative coeﬃcient ................ 65

xiii

5.5 Addition of nodes in maximum connected subgraphs ........ 65

5.6 Eﬀect of bridge edge on the network ................. 66

5.7 Eﬀect of diﬀerent attacks strength ................... 66

5.8 Simulation results ............................ 67

5.8.1 Comparison of JAYA with the existing techniques ..... 67

5.8.2 Analysis of edge swap methods ................ 69

5.8.3 Nodes’ addition in Maximam Connected Subgraph (MCS) . 74

5.8.4 Robustness enhancement by bridge edge protection ..... 75

5.8.5 Network strength against diﬀerent attacks strength ..... 76

5.9 Conclusion of the chapter ....................... 77

6 Conclusion and future work 79

6.1 Conclusion ................................ 80

6.2 Future work ............................... 81

7 References 82

Conference Proceedings 95

xiv

LIST OF FIGURES

3.1 Scale-free network’s construction ................... 21

3.2 Edge swap mechanism a: First connection method b: Second con-

nection method c: Third connection method ............. 22

3.3 Nodes distance based edge swap a: First connection method b:

Second connection method c: Third connection method ....... 24

3.4 ISFNs evaluation of robustness .................... 30

3.5 Network’s performance evaluation against low degree attacks . . . . 30

3.6 Network’s performance evaluation against high degree attacks . . . 31

3.7 Network’s performance evaluation against random attacks ..... 32

3.8 Network’s performance evaluation against variable attacks ..... 32

3.9 Network’s performance evaluation against diﬀerent number of nodes 33

4.1 Network evolution by adding edges .................. 37

4.2 Edge swap mechanism ......................... 38

4.3 Nodes’ attack based on core ...................... 42

4.4 Attacks on NMCS of the network ................... 42

4.5 (a) Malicious and random attacks on upper network. (b) Malicious

and random attacks on the lower network ............... 48

4.6 (a) Power-law distribution of the mutual nodes (b) Comparison of

core based attacks and high degree node attacks ........... 49

4.7 (a) Random attacks (b) Malicious attacks .............. 51

4.8 (a) Comparison of optimization algorithms when N = 100. (b)

Comparison between NTES and existing algorithms when N = 100 52

4.9 (a) Comparison of NTES with existing algorithms when random

attacks happen (b) Comparison of NTES with existing algorithms

when malicious attacks happen .................... 53

5.1 Chromosome is obtained from the adjacency matrix ......... 57

5.2 JAYA for the optimization of SFNs (a) Current individual (b) The

best individual (c) Finding the neighbors (d) Selecting the nearest

node (e) The updated individual .................... 58

5.3 Edge swap mechanism (a) Original topology (b) First connection

method (c) Second connection method ................ 63

5.4 Aﬀect of bridge edge on the robustness of SFNs ........... 66

5.5 Eﬀect of edge swap on the robustness of SFNs ............ 67

5.6 Aﬀect of edge swap on the robustness of SFNs ............ 68

5.7 Aﬀect of edge swap on the robustness of SFNs ............ 69

5.8 HDA attack on network ........................ 69

5.9 LDA attack on the network ...................... 70

5.10 Random attack on network ....................... 70

5.11 Assortativity coeﬃcient with diﬀerent network size and edge swap

methods ................................. 71

5.12 Average clustering coeﬃcient with diﬀerent network size and edge

swap methods .............................. 72

xv

5.13 Aﬀect of edge swap on average shortest path length ......... 72

5.14 Aﬀect of edge swap on global communication eﬃciency of network . 73

5.15 Computational complexity of diﬀerent edge swaps .......... 73

5.16 Eﬀect of edge swap on the robustness of the network ........ 74

5.17 Addition of nodes in MCS ....................... 75

5.18 Bridge edge in the network ....................... 75

5.19 Robustness of the network with attack strength 5 .......... 76

5.20 Robustness of the network with attack strength 10 .......... 77

5.21 Robustness of the network with attack strength 15 .......... 77

xvi

Chapter 1

Introduction

1

1.1. INTRODUCTION

1.1 Introduction

In this section of thesis, the background and the importance of the SFNs robust-

ness, contributions to improve robustness and organization of thesis have been

presented.

1.1.1 Details of scale-free networks

In this section, ﬁrst we will discuss the deﬁnitions of diﬀerent terminologies that

we have used secondly, we will discuss about the work in details.

The Internet of Things (IoT) has become an essential part of many real-world

applications due to the availability of a vast range of sensor nodes and the internet.

The IoT is part of healthcare [1], smart grid [2], industry [3], etc., and with the

passage of time, the number of IoT devices is increased, therefore, the networks

become dense. Moreover, due to the ease in the availability of the internet, the

concept of internet of everything has become common.

Table 1.1: List of acronyms

Notations Description

ABC Artiﬁcial Bee Colony

BA Barab´asi Albert

BFO Bacterial Foraging Optimization

DDO Degree Diﬀerence Operation

GA Genetic Algorithm

HC Hill Climbing

HDA High Degree Adaptive

HDO High Degree Operation

IoT Internet of Things

MA Memetic Algorithm

mEdge density

MCS Maximum Connected Subgraphs

MOO Multi-Objective Optimization

NNumber of Nodes

NC Natural Connectivity

NTES Network Topology Evolution

Scheme

RRobustness

SFNs Scale-Free Networks

WSNs Wireless Sensor Networks

For the eﬀective communication between IoT nodes, diﬀerent network topologies

are considered [4]. The two widely used topologies are Small World Topology

2Thesis by: Muhammad Usman

1.1. INTRODUCTION

(SWT) [5] and SFT [6,7]. These topologies are part of complex network theory. In

SWT, the heterogeneous nodes are considered that have diﬀerent communication

ranges, bandwidths and energy. Moreover, the topology has a high clustering

coeﬃcient and average shortest path length. Furthermore, the SFT is formed by

the homogeneous nodes [8] that have similar bandwidth and communication range.

Being part of several critical applications, the IoT networks are subject to cyber

attacks [9]. The attackers mostly aﬀect the networks to take its controllability.

In IoT, two types of attacks are common: random and malicious. In random

attacks, the attackers have less information about the networks, so the nodes

remove randomly. However, in malicious attacks, the attackers have complete

network information and the nodes are removed based on their properties [10]. The

eﬀect of these types of attacks on the IoT topologies is diﬀerent [11]. The SWT

is robust against malicious attacks, whereas, the SFT is robust against random

attacks. In SFT, the nodes’ degree follows the power-law degree distribution. In

this distribution, the number of low degree nodes is more as compared to the nodes

with high degrees. Therefore, the probability of attacks on low degree nodes is

high against which the SFT is robust. However, the removal of high degree nodes

makes the SFT fragile.

In [6], Barab´asi Albert (BA) model is used to form a SFT based network by follow-

ing the growth and preferential attachment processes. During the network growth

process, a new node is added asynchronously while in preferential attachment,

the local information of the already connected nodes is considered. Based on the

probability of nodes’ degree, the connections are made with nodes that are already

part of a network and have a high degree. Furthermore, the SFT is represented

by graph theory and unweighted and undirected graphs are considered [12]. The

robustness, which is the resilience of a network against attacks [13], is calculated

based on percolation theory based measure proposed by Schneider et al. The

network fragments into multiple subgraphs because of important nodes removal.

The MCS is used to calculate the robustness [14] and a large number of nodes are

required to make the network robust. The sensor nodes have limited energy re-

sources, therefore, the SFNs robustness decreases due to the nodes’ failure. Many

researchers study the methods to increase the lifetime of nodes [15–31].

In the network, some nodes are more densely connected internally nodes than the

other nodes in the network and form the community. The community in the SFNs

has an important role in the robustness enhancement. Therefore, in literature, the

3Thesis by: Muhammad Usman

1.1. INTRODUCTION

community structure in analyzed against the random attacks, malicious attacks

and cascading failures [33–36,38–56].

1.1.2 Contributions

In this section, details of the contributions to enhance the robustness of SFNs is

given. During the optimization the degree is not changed, therefore, no extra cost

is required.

A homogeneous nodes based method is represented for generating a scale-free

network that have the same communication range and energy. These nodes are

robust enough to not only random attacks but also to speciﬁc attacks. Moreover

to enhance a SFNs robustness, a new technique, Improved Scale-Free Networks

(ISFNS) is proposed. ISFNs, enhances the robustness of the scale-free network

topology without changing the nodes’ degree distribution. ISFNs consists of two

phases: the edges degree based swap and nodes distance based edges swap to

obtain onion-like structure for the network. The onion-like structure makes the

network robust against the malicious attacks

Moreover, as the size of networks are increasing due to the advancement in the

sensor techniques, the networks are becoming dense. The size of a network has an

eﬀect on network robustness and with the increase in a network size the robustness

decreases. To address the aforementioned problem, a Network Topology Evolu-

tion Scheme (NTES) is proposed. In NTES, the network evolution is started by

dividing the sensor ﬁeld into two parts. The networks are evolved in each part and

link with each other through one-to-many correspondence. The nodes of network

Aare linked with single or multiple nodes of network Bby following one-to-many

correspondence. To make the network robust, the nodes’ degree follow the power-

law degree distribution. The nodes’ degree changes hierarchically in each ring [59],

therefore, the change is calculated by k-core decomposition. Furthermore, the SFT

is optimized to form long links between the nodes because these links make the

network robust against malicious attacks [60]. In addition, three optimization al-

gorithms, Genetic Algorithm (GA), Bacterial Foraging Optimization (BFO) and

Artiﬁcial Bee Colony (ABC), are used to optimize NTES and the one that has

better performance is selected.

Furthermore, to optimize the SFNs the JAYA algorithm is used to ﬁnd the optimal

solution with less computational cost and without the control parameters. It is

the latest optimization technique used to ﬁnd the optimal results than the already

existing techniques. According to our knowledge, we are the ﬁrst that are using

4Thesis by: Muhammad Usman

1.1. INTRODUCTION

JAYA for the optimization of SFNs. Furthermore, to enhance the robustness of

the networks edge swaps are performed. To analyze the eﬀect of edge swap on the

network structure, the edges are classiﬁed into three categories: random edge swap,

degree based edge swap and distance based edge swap. The edge swap is performed

by keeping the nodes’ degree distribution constant. In addition, the robustness is

enhanced by adding the nodes in the MCS after the network fragments. Moreover,

the network’s performance is analyzed against diﬀerent attack strengths.

1.1.3 Organization of thesis

The rest of thesis is organized as: in Chapter 2related work on SFNs is reviewed.

Chapter 3, Chapter 4and Chapter 5present strategy to enhance robustness, net-

work topology evolution scheme and optimization of SFNs, respectively. In Chap-

ter 6, conclusion and future work of the study is presented.

5Thesis by: Muhammad Usman

Chapter 2

Literature review and problem statement

6

2.1. LITERATURE REVIEW

2.1 Literature review

In real-world applications many networks are scale-free. These networks are robust

against random attacks because low degree nodes are present in a large number

and they have a high probability of selection. However, malicious attacks frag-

ment these networks into multiple subgraphs. In malicious attacks, the important

nodes based on their degree, betweenness centrality, closeness centrality, etc., are

removed from the network. Due to the presence of a small number of important

nodes, SFT are prone to malicious attacks. In this thesis, the importance of a

node is calculated based on its degree.

The network topology has a key role in deﬁning the robustness of SFT. In [57],

based on the graph theory, BA model is proposed. In this model, the processes

that are required to construct a network are presented. The model forms the

network topology to an onion-like structure that is robust against the malicious

attacks. However, the limited communication range of sensor nodes in Wireless

Sensor Networks (WSNs) is not considered. Therefore, the nodes may die due to

their excessive usage. Moreover, the constraints of sensor nodes including limited

energy and communication range is considered in [62]. The network is evolved

by considering nodes’ communication range and the threshold value of the nodes’

degree. The robustness is improved through the Degree Diﬀerence Operation

(DDO) and Angle Sum Operation (ASO). However, the redundant operations

increase the computational complexity. A similar work has been performed in

the [63]. Furthermore, based on the nodes’ fault probability a network topology

evolution scheme is introduced in [65]. In the network growth process, a new

node joins the network based on the fault probability and communication range of

nodes, simultaneously. The network robustness is enhanced against the malicious

attacks due to the formation of onion-like structure.

The optimization is performed to enhance the robustness of SFT. The optimized

topology has a better network structure and is robust against malicious attacks.

Therefore, a local rewiring based algorithm Hill Climbing (HC) is proposed [57].

In this algorithm, the rewiring is performed by considering the local information of

nodes. Although it provides a better network after the rewiring, however, it traps

into local optima due to the local rewiring process. A similar work has been done

in [58]. The authors in [66] extended the work proposed in [57] by introducing the

global edge swap method. A temperature variable is introduced to get the optimal

solution. Moreover, the authors in [67], use a deluge algorithm to get the optimal

result as compared to SA. The edges are classiﬁed with the removal of nodes from

7Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

the network [68]. The swap operation is performed to increase the number of valid

edges in the network. The increase of nodes in MCS enhance the robustness of

the network.

Diﬀerent heuristic techniques are used to enhance the robustness of SFT. GA is

mostly used to optimize SFT, however, the classical GA traps to local optima due

to premature convergence. The low population diversity causes the premature

convergence problem and for better results, a global solution is required from the

search space. To solve the less population diversity problem [69,70] introduces the

multi-population based methods. Although the methods provide a high diverse

solution, however, due to the involvement of multi-population the computational

cost is high. Moreover, the involvement of multiple operations makes the method

diﬃcult to implement on real-world networks. Qie et al. [71] solve the premature

convergence problem by introducing the self competition among the individuals

of a population. Moreover, a Memetic Algorithm (MA) is proposed [72] that uses

the local search operator to avoid premature convergence. The optimal solution

is found from the search space by MA. A multi-agents based algorithm [73] is

proposed to get the optimal solution with less computational cost.

Single objective optimization techniques are discussed so far for the optimization

of SFT. However, in some problems, it is diﬃcult to optimize the network with

single objective optimization. Therefore, a Multi-Objective Optimization (MOO)

technique is used to optimize the problems that have more then one objective to

be optimized. A Multi-Objective Evolutionary Algorithm (MOEA) is proposed

in [74] that is used to optimize robustness, when multiple attacks simultaneously

happen on the network. In MOEA, the negatively correlated objective functions

are found by Pearson correlation coeﬃcient and are optimized. Due to MOO,

the computational cost to calculate robustness is high. A similar work has been

done in the literature [75–77] to enhance the robustness of SFNs against multiple

objectives. Apart from undirected network, a directed network also deﬁne the

important network characteristics. Therefore, a directed network with the emer-

gence of cooperation and controllability of robustness as two important features are

discussed [78]. To ﬁnd the correlation between network topological features and

their robustness a practical approach is required. Therefore, in [79], an empirical

approach is proposed to better control the network.

Moreover, the IoT based applications and importance of the networks other then

the scale-free is studied in the literature review. In robotic, the controlled move-

ment has great importance to complete the tasks. Therefore, the collision and

8Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

obstacle avoidance control strategies are studied in the [80,81]. Moreover, the se-

cure networks for the agri-food and the data sharing based on the blockchain are

studied in [82,83]. Using the blockchain network’s and data security is increased.

The WSNs are extensively used in the underwater communications. The eﬀects

of sink mobility on the data gathering are analyzed in [84,85]. The sink nodes are

considered static and the sensor nodes are mobile before. However, the extensive

research prove the importance of sink position for the eﬃcient communication.

In the communication, the delay should be kept minimum for the eﬃcient sharing

of data. Therefore, a routing scheme for the delay sensitive applications pro-

posed [86]. Moreover, the eﬃcient routing is performed in [87–89] The routing

is made eﬃcient by managing the energy consumption of the sensors. Further-

more, to increase the network lifetime the protocols are enhanced [90,91]. These

protocols use the proper routing to better utilize the energy. In the same way,

energy utilization and consumption algorithms to increase the network lifetime

are discussed [92,93]. The network attack and repair strategy is analyzed in [94].

The fraction of removed nodes and the average degree of nodes are experimentally

studied.

Some other crossed domain topics are also studied in the literature review to

make use of diﬀerent concepts. The deep learning is used to detect the brain

tumor [95]. The data set of normal and eﬀected persons are analyzed and based

on the selected features the model is trained to detect the tumor. Furthermore, the

communications related to the body area network based protocol is studied [96].

The faults detection in the WSNs has a great importance. Therefore, base on

the random forest scheme the faults are detected [97]. Moreover, to increase

the lifetime of the networks the cooperation of nodes is very important, which is

analyzed in [98].

In cascading failure and robustness of the network, how they relate with each

other needs to be found [99]. By calculating these objectives comprehensively

normalized robustness used that has high computation cost. Connectivity and

dependence links enhance the robustness [132], however, these links are added

randomly. If both networks follow scale-free topology then these links may change

the network structure. The dependence links are not following the power-law

distribution, therefore, stubborn attacks on these links cost more damage to the

network. Both networks contain high and low degree nodes and attacks on high

degree nodes aﬀect more. Therefore, dependence links need to be added between

these nodes to improve robustness. After a node or edge having high load is

9Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

removed its load is distributed among other nodes or edges. However, there is a

possibility of simultaneous edge and node removed due to the environmental faults

or extra load then how the network deals with that to minimize the Cascading

Failures (CF) is not discussed [100]. Secondly, due to overloading the robustness

of a network decreases therefore, when an attack or failure happens on one part

of a network that should be removed to protect the other part of the network

to collapse. The proposed scheme better capture the CF, however, no method

is discussed to reduce the CF. The failure of nodes is not mentioned either it is

random or based on degree. In Cyber Physical System (CPS) Distributed Energy

Resources (DER) can help to maintain load that need to be discussed. A similar

work is done in [101–103]. Moreover, against the multiple nodes removal from the

network, the cascading failure is calculated in [104]. Using diﬀerent parameters

the network robustness is analyzed to make a practical approach to enhance the

robustness.

Although [59] gives new perspective of attack based on cores of network, however,

when the fraction of removed nodes is high core based attack is less destructive.

In that case the betweenness centrality attack becomes more vulnerable to the

network. For SFNs onion-like structure is not considered because in that struc-

ture same degree nodes are connected with each other. Shell-min attacks can

be considered as random attacks because they are done on low value of core for

these attacks SFNs are robust. By installing backups network performance in-

creased [105] however, installing backup increase cost. The key nodes usually are

hub nodes so, installing backup for these nodes is not an easy task. Secondly, after

the attack, a key node is removed because there is a high probability that a sec-

ond attack may happen on the backup node. In targeted attacks, the attacker has

complete information of the network [105] how the proposed scheme update the

network information is not discussed. The links are added and removed to imple-

ment the adversarial attack [106], however, the degree distribution is change that

is not considered. The addition of links have a cost that has not been discussed.

Wandelt et al. [107] propose Quick Robustness Estimation (QRE) for estimating

the network robustness. QRE performed better than the betweenness centrality

because it is based on a cheap-to-compute network matrix combination. The pro-

posed system enhanced the robustness of scalable networks, therefore, it is not

for SFNs. The methodology for managing the robustness of the Social Internet of

Things (SIoT) is proposed by the authors in [108]. SIoT is divided into multiple

Enterprise Systems (ESs). The state variable of these ESs are determined and the

interaction between them is anatomized. After that, the nonlinear dynamic model

10 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

is developed. However, the applicability of the proposed method is reduced at the

operating stage of SIoT.

The entropy based network robustness optimization technique is proposed in [109].

In this technique, the random failures of nodes are considered and the entropy of

the network degree distribution, scaling factor of power-law and nodes’ connectiv-

ity is studied. However, the malicious attacks against which the SFNs are fragile

is not considered. Apart from that, the network robustness is enhance by adding

links [110]. A similar work is performed in [120,121], that consider the critical net-

work infrastructure and modiﬁcation of edges, respectively. On the other hand,

the network robustness of SFNs against the removal of links is analyzed in [122].

The malicious attacks are happened on high degree nodes, therefore, the method

is failed to increase the robustness in that case. Moreover, the network robust-

ness in enhance by decreasing the assortativity coeﬃcient against the malicious

attacks [111]. The network robustness against the random attacks by keeping the

nodes’ average degree constant is enhanced in [112,113]. Moreover, the network

robustness is enhanced by modifying the network structural and characteristics

information in [114–117].

To enhance the network robustness against the malicious attacks, the parame-

terized networks are made that are robust against both random and malicious

attacks [118]. A similar work has been done in [119]. On the other hand, the lim-

ited energy of sensor nodes are analyzed to deal with sensor nodes’ constraints to

increase the robustness of large-scale WSNs [123]. To increase the network robust-

ness against the attacks on the edges is studied in [124]. The network robustness is

increased against the random and malicious attacks without increasing the num-

ber of driver nodes. Moreover, to study the limited energy of the sensor nodes

in the SFNs, the network is partitioned and a reduced SFN is made [125]. Fur-

thermore, the robustness structure and the eﬀects nodes removal are extensively

studied in [126–130]

11 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

Table 2.1: Problems addressed by previous work.

Problem identiﬁed Proposed solutions Validations Limitations

SFNs survivability and

robustness after cyber

attacks

•Topology evolution

based on fault proba-

bility

•Considered two op-

erations i.e., HDO and

DAO

•Four types of attacks

considered that make

TMSE resilient against

real life attack

•Results are validated by

comparing with the exist-

ing algorithms

•TMSE outperforms the

existing techniques of dif-

ferent size networks and

diﬀerent types of attacks

•Preferential attachment

may be compromised due

to fault probability

•HDO changes connection

of high degree nodes which

have high attack probabil-

ity

•DAO not considered the

fault probability that in-

crease the eﬀect of attack

[65]

Multiple attacks on

a network simultane-

ously

Uses multi-objective

optimization to en-

hance the robustness

•Eﬀects of attack on topo-

logical features validate the

importance of MOEA

•Synthetic and real-life

networks prove the impor-

tance of MOEA

•High computation cost

due to calculation of multi-

objectives

•Topology improvement

for simultaneous attacks

not discussed

•Pareto set generation can

be diﬃcult [74]

Premature conver-

gence of classical

GA

Uses multi-populations

co-evolution to solve

premature convergence

Comparing with exist-

ing algorithms MPGA

enhances the robustness

•Optimal population size

should be chosen

•Local operator should be

used to make diverse pop-

ulation [69]

Premature conver-

gence of classical

GA

•Self-competition

among the individuals

•Calculate population

diversity using GPCR

and GDU

•Mutation probability

calculated by adaptive

adjustment

•Performance is validated

with diﬀerent algorithms

and attacks

•Less time cost as com-

pared to ROCKS which

is multi-population algo-

rithm

•Diversity calculation is a

complex process at every

operation

•GPCR and GDU have

high computation cost

•Uses multiple operations

which make computation

cost high [71]

Link attack based on

betweenness centrality

has high computa-

tional complexity

•Link attack based on

shell has better perfor-

mance than between-

ness centrality

•Shell based attack

has less computational

complexity

•Importance of shell-based

attacks validated for diﬀer-

ent types of network

•Shell-min, shell-max and

shell-pro attacks compared

with betweenness central-

ity

•Less destructive when the

fraction of removed nodes

is high

•Less eﬀective for SFN due

to not following onion-like

structure

[59]

Continued on next page

12 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

Table 2.1 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

No practical approach

available to under-

stand the relationship

between network

topology features and

network robustness

•Empirical approach

to measure robustness

•Exhaustive search-

based technique on

small world and real-

life networks

•ENC helps to con-

verge by shrinking

search space

•RER helps to rectify

ENC

•Importance of RER

is validated for diﬀerent

number of nodes

•Network connectedness

improved by increasing

number of RER operations

•Exhaustive attack is not

actually considered

•Not explain RER for

undirected network

•RER changes nodes de-

gree

•Due to RER SFN not re-

mains scale-free [79]

The vulnerability of

SFNs due to malicious

attack

•Constructed network

considering communi-

cation range of nodes

•DDO and ASO

are used to construct

onion-like structure

•Through ROSE topol-

ogy converted to onion-like

structure

•Network becomes robust

against malicious attack

•Target all nodes in the

network may generate re-

dundant operations

•DDO and ASO are com-

putationally expensive [62]

High computational

complexity of exist-

ing algorithms is a

hurdle in topology

self-optimization

For the self-

optimization AI based

technique is used

•Eﬃciency and loss func-

tion for training and test-

ing validated

•99% eﬃciency is achieved

•Loss function minimized

in 35th iterations

•Not suitable for diﬀerent

size of networks and edge

densities

•After diﬀerent attack

how self-optimization

works not discussed [64]

Due to random edge

swap without consider-

ing the network struc-

ture redundant opera-

tions are performed

•After attack edges

are classiﬁed into three

types •By increasing

the valid edges robust-

ness enhances

•For HDA attack algo-

rithm performance is vali-

dated for diﬀerent size of

networks

•The heuristic algorithm

maintains the robustness

•Edge swap between in-

valid edges enhances the

computational overhead

•When nodes fail the

edges with the nodes also

removed [68]

•Network robustness

decreases when the

nodes removed

•Optimization algo-

rithms fall into local

optima

•NC is used to mea-

sure robustness

•Chaotic GA is used

to optimized network

•To avoid local optima

logistic maps based

power function carrier

used

•Degree distribution

remains unchanged

after optimization

•NC increases with in-

creasing iterations [136]

•NC and r have positive

correlation that proves en-

hance robustness

•Onion-like structure is

achieved through optimiza-

tion

•Considering diﬀerent at-

tacks network is robust

N/A

Continued on next page

13 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

Table 2.1 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

•Community detec-

tion algorithms reveals

individual information

•Practical approach

required to protect

these individual’s

privacy

•Network is optimized

by using heuristic tech-

nique

•CDA and HDA per-

formed to detect com-

munity and high degree

nodes

•Q-based attack based

on GA is performed

•Addition and removal

of links increase pri-

vacy on individual

•Diﬀerent Pcand Pmare

used to ﬁnd optimal pa-

rameter for GA

•For all networks Q-based

attack outperformed

•Decrease in Q value con-

ﬁrms the privacy of indi-

vidual

•Randomly addition and

removal of links aﬀect SFN

•Addition of links have

cost

•Onion-like structure is

not follow

•Directedness is an

importance feature for

a network

•f(c) and Rdiﬃcult

to optimize simultane-

ously

•Correlation between

f(c) and Rproves the

negative correlation

•MOEA is used for

these objective

•r is used to set net-

work structure

•PD is used to calcu-

late f(c)

•MCS is used to calcu-

late R

•Pareto optimal solu-

tion obtain for both ob-

jectives

•MOEA optimized both

objectives better than sin-

gle objective algorithm

•Diﬀerent networks are

optimized

•Ris assortative whereas

f(x) disassortative there-

fore, onion-like structure is

obtained by shuﬄing edges

•No comparison is made

with other multi-objective

algorithms [78]

•Only assortativity is con-

sidered

•Other topological param-

eter may prove positive

correlation of these objec-

tives

•SFNs are vulnerable

to malicious attacks

•Robust network

structure is require

•SFNs converter to

onion-like structure

•Random edge swap is

made

•Degree diﬀerence re-

mains same after opti-

mization

•Nodes increase in

MCS

•Diﬀerent networks

are used for validations

•Network performance im-

proved against malicious

attacks

•Swap edge enhances ro-

bustness

•Increase in number of

nodes decreases network

robustness

•Assortativity and cluster-

ing increase

•Random edge swap ef-

fects network structure [57]

•Average shortest path

length increased

•HC traps into local

optima

•Network optimiza-

tion against malicious

attacks required

•Network is generated

by using BA mode

•Global and local edge

swap performed for ex-

ploration and exploita-

tion

•Tis used to avoid lo-

cal optima

•αis used to get fast

convergence

•Both synthetic and real-

world networks considered

•Global edge swap and

local edge swap are com-

pared

•Global edge swap has

better robustness due to

exploration

•Network optimized by us-

ing αis more robust

•No threshold value for

edge swap is set [66]

•No real-world network

considered

Continued on next page

14 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

Table 2.1 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

•SFNs are not ro-

bust against malicious

attack

•No proper solution

is available to mitigate

cascading failure

•Rcand Rare weakly

correlated

•Multi-objective opti-

mization is used

•High and low objec-

tives value based net-

works generated by SA

•Optimal topology is

found by the MAGA by

exploration

•Both synthetic and real-

world networks are consid-

ered

•Normalized robustness is

better as compared to SA

•For diﬀerent sized net-

works both objective have

improved values

•Cascading failure proved

to be more damaging as

compared with intentional

attack

•A single measure needs to

be used for these objectives

•Cascading failure does

not happens rapidly coun-

termeasures reduce its ef-

fect

•A better network struc-

ture required that is robust

against attacks

•Robustness of in-

terdependent network

needs to enhance

•Connectivity and

dependence links are

added to enhance

robustness

•Considering cost

constrain optimal val-

ues of these links need

to be calculated

•CPS is more vulnera-

ble and caused cascad-

ing failure in physical

region

•One-to-many conﬁg-

uration is used to con-

nect these networks

•Stubborn and smart

attacker are considered

•Defender add links by

calculating intra and

inter degree

•Both synthetic and real-

world networks are consid-

ered

•Intra and inter de-

gree based attacks are per-

formed on networks

•Based on these attacks

links are added

•Diﬀerent intra and inter

degree based networks are

considered

•Links are added ran-

domly without considering

degree distribution [132]

•Dependence links are not

following power-law distri-

bution

•Stubborn attack on de-

pendence links caused net-

work to fail

•GA has premature

convergence problem

•Population diversity

needs to be high

•ROCKS uses

multi-populations

co-evolution to deal

premature convergence

•Pmand Pcare dif-

ferent for populations

•Populations coordi-

nate using migration

operator

•Migration popu-

lation contains best

individuals from all

populations

•Degree distribution

remains same after

optimization

•Onion-like structure is

obtained after optimiza-

tion

•After ROCKS network is

more robust against ran-

dom and malicious attacks

•For diﬀerent size net-

works ROCKS outperform

HC and SA

•Due to multi-population

computation cost is

high [70]

•After attack self-

optimization is diﬃcult

•Diﬃcult to implement

on real-life network due to

complexity

Continued on next page

15 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

Table 2.1 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

•After edge or node re-

moval their respective

edges and links also re-

moved

•Multiple nodes and

links removal caused

cascading failure

•Node and Edge based

model required to deal

the failure

•DB model proposed

to deal cascading fail-

ure

•Node importance is

calculated based on its

degree and between-

ness

•Removal of impor-

tant node causes other

nodes and edges to be-

come overload

•Load is distributed

among node by consid-

ering their initial ca-

pacity

•Five metrics used to

evaluate robustness

•Initial capacity of nodes

is compared with critical

value of tolerance

•Cascading failure of edge

and node of DB model is

less when θ¡ 0.9

•DB model has better

value of SGand SCcom-

pared to DN and BN

•Load is distributed

among neighboring nodes

which have already high

load

•Simultaneous edge and

node removal is not dis-

cussed

•Overload edges and

nodes should be removed

to protect remaining

network

•Random edge swap

is performed in opti-

mization

•Robustness improve

by compromising com-

munity structure

•Optimizing per-

formed in a way that

preserve community

structure

•Three step strategy

is proposed to preserve

community

•Onion-like structure

is introduced in every

community

•High degree nodes

are connected with

nodes of their own

community

•Considering cost

links can be added

to better preserve

community

•Community structure is

compared before and after

optimization

•These are preserved with

enhanced robustness

•After the node removal

3-steps strategy performed

better compared with sin-

gle step

•High degree node of dif-

ferent communities are not

connected with each other

•After removal of high de-

gree node its load can not

be distributed by remain-

ing nodes

•High degree nodes must

connect to share their load

•Removal of impor-

tant node causes de-

crease in robustness

•These nodes must

be protected by taking

countermeasures

•No method is avail-

able to protect these

nodes

•Networks vulnerabil-

ity against key nodes

removal

•Key nodes are found

based on MILP

•Network is optimized

by heuristic algorithm

•Countermeasures in

the form of installing

backup for key nodes

•Impact of node’s elimina-

tion on topological param-

eter

•Throughput, network de-

lay, and ﬂow is calculate

•End-to-end delay is max-

imized by node protection

•After removal of a node

there is high probability of

backup node removal

•Network information

need to be updated

against smart attacker

Continued on next page

16 Thesis by: Muhammad Usman

2.1. LITERATURE REVIEW

Table 2.1 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

•Adversarial attack

change network infor-

mation

•Network robustness

decrease by these at-

tacks

•Adversarial attacks

are considered in this

network

•Two new attacks

strategies DILR and

DALR intoduced

•RLR is less eﬀec-

tive as compared with

DILR and DALR

•SFNs are categorized

into strong to weakest

•Networks are generated

by considering Rand α

•Each network is attacked

200 times

•Average shortest path

length, clustering coeﬃ-

cient and diagonal distance

is compared with attacks

•Degree distribution is

change by edge swaps [106]

•Addition of links have

cost

Cascading failures

in interdependent

networks

•Novel method to

capture cascading

failure introduced

•CPS and PS consid-

ered as interdependent

networks

•One-to-One corre-

spondence between

networks

•Fraction of survival

nodes is calculated af-

ter each node removal

•Removal of a node in

PS caused overloading

in survived nodes

•Removal of a node

in CPS decreases MCS

size

•Networks are validated

according to asynchronous

failure propagation model

•Intra and inter dependen-

cies considered

•At even stage nodes re-

moved from CPS and at

odd stage nodes are re-

moves from PS

•For load and space of PS

diﬀerent distributions are

followed

•Interdependent network

is more vulnerable to node

removal as compared to

single network

•How to reduced cascad-

ing failure is not discussed

[100]

•DER impacts to reduce

load needs to be discussed

Importance of SFN ro-

bustness

•Memetic algorithm

to optimize network is

used

•To enhance robust-

ness global and local

searches used

•Population is gener-

ated by swapping edges

of initial network

•Crossover is per-

formed by changing

links of parents

•Oﬀsprings has same

degree distribution as

parents

•Local search operator

is used for exploitation

•Optimal solution is

found by 2-tournament

selection

•Optimal value of edge

swap

•Diﬀerent sized network

MA −RSFM A outper-

formed

•Proposed crossover im-

prove network robustness

•MA−RSFMA performed

better against random and

malicious attack

•Onion-like structure is

produced

•Population has less diver-

sity [72]

•Crossover changes degree

of nodes

17 Thesis by: Muhammad Usman

2.2. PROBLEM STATEMENT

2.2 Problem statement

This section presents the problem statement of the thesis. Moreover, the main

problem is divided into three subproblems which are presented below.

2.2.1 Details of problem statement

The SFNs are more suitable for IoT networks because they are resilient to random

attacks. In recent years, a signiﬁcant attention is paid to enhance the robustness

of these networks against malicious attacks [12,68,131]. For the topology of SFNs,

BA model is proposed in [57], that explains how the nodes are connected to form

a network. Furthermore, for the calculation of robustness, a mathematical equa-

tion based on the percolation theory is proposed in [139]. One way to increase

robustness is by adding edges however, it adds the cost, which is solved by edge

swap. Therefore, by global edge swap, the degree of nodes remains constant and

robustness enhances without increasing cost [62]. In addition, an onion-like struc-

ture is proposed that contains nodes, whose degree decreases hierarchically and

are more robust against malicious attacks. However, the solution falls into local

optima that is solved in [57] with local edge swap. Moreover, due to redundant

operations, it has less eﬃciency. In [68], the network is constructed based on the

communication range and the threshold of nodes degree. It converts the network

into an onion-like structure, however, due to redundant operations, the network’s

eﬃciency is reduced.

Critical networks, including healthcare, military, and Internet, etc., have scale-free

nature. These networks should be robust against attacks however, the existing

algorithms [68,70,138] have high cost, therefore, self-optimization is used in [14].

Still, the problem of network robustness is not solved against malicious attacks

and these attacks make the network vulnerable.

18 Thesis by: Muhammad Usman

Chapter 3

Edge swap based optimization strategy to

enhance the robustness of SFNs

19

3.1. SUMMARY OF THE CHAPTER

3.1 Summary of the chapter

During the past few decades, the Internet of Things (IoT) has made remark-

able progress in many real-world applications including healthcare, military, trans-

portation, etc. Multiple sensor nodes are deployed in these ﬁelds to get the re-

quired data. Diﬀerent network topologies are used in IoT and scale-free is one

of them. It is mostly preferred due to its robust behavior against random node

removal, however, the network collapsed because of malicious attacks. Therefore,

in this chapter, the robustness of scale-free networks is improved against malicious

attacks through optimization. To achieve this, the edge’s degree and nodes’ dis-

tances based edge swap operations are used in the proposed Improved Scale-Free

Networks (ISFNs) scheme. In the edge’s degree based operation, nodes of similar

degrees are attached and links between the near neighboring nodes are made in

nodes distance based operation. These operations help to achieve a better onion-

like structure without changing the degree distribution of the network. Therefore,

the network becomes robust against malicious attacks. Moreover, no new links

or nodes are added in the optimization process, therefore, no extra cost is in-

curred. Furthermore, to make the network more robust against realistic attacks,

the variable attacks are considered.

3.2 Generation of scale-free networks

The construction of SFNs is based on the BA model. After the deployment of

nodes in a sensor ﬁeld, the model considers the growth and preferential attachment

steps. In the ﬁrst step, at each time interval, a single node is added to the network.

However, in the second step, new nodes prefer to join the network by considering

the degree of the existing neighboring nodes. The new nodes prefer to join the

nodes that initially have high degree in the network. Therefore, due to the limited

resources of the sensor nodes, the chance of network failure is increased.

To deal with that issue, the connection probability based solution is used. The

probability Πdi of a new node iis calculated as follows.

Πdi=di

Pjdj

,(3.1)

where, diand djare the degree of node iand sum of the neighboring nodes degrees,

respectively. The network construction is depicted in Fig. 3.1. There are three

nodes i, j and kthat want to become part of the network. First of all, start

20 Thesis by: Muhammad Usman

3.3. NETWORK MODEL DESCRIPTION

k

k1

k2

k3

k6

k4

k5

i

i1

i2

j1

j3

j4

j

j2

j5

Figure 3.1: Scale-free network’s construction

with the example of node jthat wants to join the network. There are ﬁve nodes

in its neighborhood with degrees 1,1,3,1 and 2. Using Eq. 3.1, the connection

probabilities of the neighboring nodes are 0.125, 0.125, 0.375, 0.125 and 0.25. All

the nodes’ connection probabilities are placed into the roulette wheel. On average,

the nodes with a high degree have more area into the roulette wheel as compared

to the low degree nodes. So, there is more probability of a high degree node’s

selection. In this study, the neighbor nodes that have the highest connection

probability are directly selected, however, for the remaining nodes, the roulette

wheel selection is followed. Furthermore, node idetermines the connection with

neighbor nodes according to the edge density m. In the neighbor of node i, their

are two nodes i1and i2with degrees 2. If m= 2 then node imakes connection with

nodes i1and i2directly. Otherwise, depending on the value of m, the connections

are made. For node k, there are six nodes and all these nodes are not connected

with the network. Therefore, when the node kbroadcasts a request message to

make the connections with the nodes in the local community, all the nodes will

receive that message. After that, the nodes reply to node k. At that point,

the node kfollows the First Come First Serve (FCFS) [140] approach because it

provides a simple, eﬃcient and less computational expensive solution. Therefore,

the node that responses early makes the connection with node k. These steps

mentioned above are followed until the network is completely evolved.

3.3 Network model description

The complete details of the proposed ISFNs are given in this section. The de-

scription of the operations as part of the ISFNs is presented in detail. However,

21 Thesis by: Muhammad Usman

3.3. NETWORK MODEL DESCRIPTION

before being familiar with these operations, the knowledge of independent edges

is essential.

3.3.1 Independent edges

The topology of the SFNs is represented as a graph G={N, E }. The set of

nodes and edges are given as N={N1, N2, ..., NN}and E={E1, E2, ..., EN},

respectively. The following conditions should be met to conﬁrm that the edges eij

and ekl are independent.

1. Nodes as part of edge pairs should be in the same communication range.

2. There should be no extra edge except eil and ejk .

In Fig. 3.2a, the original topology with edges eij and ekl are shown. By following

the above mentioned conditions, the selected edges are independent. Moreover,

the ﬁrst edge swap (eik,ejl) and the second edge swap (eil,ejk ) are represented in

Fig. 3.2b and Fig. 3.2c, respectively.

ij

kl

ij

kl

ij

kl

ab c

Figure 3.2: Edge swap mechanism a: First connection method b: Second

connection method c: Third connection method

3.3.2 Edges’ degree based edge swap

For the speciﬁc edge eij , the edge degree dij is calculated from the degrees of its

respective nodes. The edge degree is derived from the nodes degree [74] and is

deﬁned as,

dij =pdi×dj,(3.2)

where, degrees diand djare for nodes iand j, respectively. Higher the value of

edge degree, more important is it in the network.

After ﬁnding the independent edges from the network, the edge degree is calculated

with Eq. 3.2. After that, based on the Eqs. 3.3,3.4 and 3.5, the degree diﬀerence

is calculated against each edge swap. The degree diﬀerence of all the edge swaps is

22 Thesis by: Muhammad Usman

3.3. NETWORK MODEL DESCRIPTION

compared and the pair of edges having minimum diﬀerence is selected. If a new pair

of edges increases the robustness and the network connectivity is not destroyed,

then the adjacency matrix is updated with the acceptance of edge swap.

DIF0=|dij −dkl |,(3.3)

DIF1=|dik −dj l|,(3.4)

DIF2=|dil −dj k|.(3.5)

The edge swap based on the diﬀerence of degree is motivated from [62], where

random pairs are selected from the network. However, in this thesis, the edges are

selected based on the degrees of their respective nodes. The edge degree based swap

helps to connect the similar degree nodes. Moreover, the edge degree diﬀerence

operation helps to achieve high robustness by increasing the assortativity. Using

the edges’ degree diﬀerence operations, the degree distribution of the original

topology is not changed. Therefore, no extra cost is required to optimize the

network.

3.3.3 Nodes distance based edge swap

It is the second operation of the ISFNs scheme. This edge swap helps to link the

nodes that are near to each other. In the network, the robustness is improved

against malicious attacks when these links exist. As the preferential attachment is

followed by the SFNs in the growth process, the new nodes make the connection

with the existing nodes based on a high degree. Therefore, the low degree nodes

have a chance to connect with the high degree nodes, through this edge swap.

To perform the nodes distance based edge swap, nodes i, j, k and lare selected

from the graph G. There are edges eij and ekl between the nodes (i, j ) and

(k, l), respectively. The average distance is calculated between the nodes of the

independent edges using the Euclidean distance formula. The average distance of

two edges is required for each connection method because there is a possibility of

a distance mismatched between the edges. Therefore, the pair of edges is selected

that has a small distance. Fig. 3.3 shows the nodes distance based edge swap.

D1and D2represent the nodes’ Euclidean distances for the edge eij and ekl,

respectively. In Fig. 3.3a, the original topology is given and the robustness is

calculated against the malicious attacks. Moreover, in Fig. 3.3b, the ﬁrst edge

swap of the independent edges eik,ejl is performed and D1and D2are calculated.

The same approach is followed in the second edge swap as shown in Fig. 3.3c.

23 Thesis by: Muhammad Usman

3.3. NETWORK MODEL DESCRIPTION

D1

D2D1D2D1D2

ij

kl

ij

kl

ij

kl

ab c

Figure 3.3: Nodes distance based edge swap a: First connection method b:

Second connection method c: Third connection method

The robustness is calculated against these edge swaps and the pair of edges that

provides the highest value of robustness is selected to update the adjacency matrix.

3.3.4 Measuring network robustness

After the malicious attacks on the SFNs, the network is divided into multiple

subgraphs, resulting in the reduction of the network performance. To study the

relationship between robustness and the number of removed nodes, Schneider et

al. [62] and [131] proposed a mathematical equation. According to the Schneider

observation, the robustness Rof a network having Nnodes can be represented

using Eq. 3.6.

R=1

N+ 1

N−1

X

n=0

MCSn

N,(3.6)

where, Nis the number of nodes and 1

N+ 1 is the normalization factor and MCS

are formed after the nodes with high degree are removed. Moreover, the robustness

lies in the range of [0, 0.5]. The maximum value of the robustness is less than 0.5,

which means that the maximum number of subgraphs are connected while the

minimum value of the network robustness is approximately zero, which means

that a single high degree node present in the network is aﬀected by the malicious

attacks.

3.3.5 Variable attacks

To study the eﬀect of variable nodes removal from the network in an instant

of time, the variable attacks are performed. In these attacks, the number of

nodes is removed from the network and its connectivity is analyzed. By knowing

these attacks, the defender can easily optimize the network to increase its lifetime.

Variable attacks in this study are performed by randomly selecting the number

of removed nodes in the range of 1 to 10. The number of nodes in the MCS

24 Thesis by: Muhammad Usman

3.4. DETAILS OF ISFNS ALGORITHMS

is calculated after each attack. Due to multiple nodes are randomly removed,

therefore, the eﬀect on network connectivity with multiple nodes removal in a

single instant is analyzed.

3.4 Details of ISFNs algorithms

In this section, details about the algorithms used in the proposed ISFNs scheme

to enhance the robustness of the SFNs are given. The algorithm 7is motivated by

the network generation model of [62]. In this algorithm, the priority is given to the

nodes that reply ﬁrst to the connection request message. Moreover, a node that

is near to the requesting node has more possibility to make a connection than the

one that is far. Due to this less energy is consumed and the chances of node failure

are reduced. In algorithm 7, the process of SFNs topology modeling is discussed.

The following variables are used in this algorithm.

•N: number of nodes present in the network.

•r: communication range of nodes.

•m: number of edges a new node makes with its neighboring nodes.

•thr : threshold value of max degree.

•A: adjacency matrix.

The details of the Algorithm 7are as follows.

Initially, the nodes are uniformly random deployed in a sensor ﬁeld (Line 1). The

network’s evolution starts from the center node Nc. To form a clique, the center

node with its neighboring nodes are found (Lines 3-4). The nodes that are in the

same communication range, form the edge with Ncand the adjacency matrix is

updated (Line 5). After the completion of the clique, the network growth starts

and the node which wants to connect with the network is randomly selected (Line

8). The degree of its neighboring nodes is calculated (Line 9). Depending on the

degree of neighboring nodes the edges are formed. If all the neighboring nodes

are not connected with the network then depending on the value of mthe edges

are formed and the network adjacency matrix is updated (Lines 10-11). However,

if the neighboring nodes are already connected and their degree threshold is not

achieved then the connection probability is calculated (Lines 12-15). Moreover,

for the preferential attachment, the neighboring nodes are selected, based on the

25 Thesis by: Muhammad Usman

3.4. DETAILS OF ISFNS ALGORITHMS

Algorithm 1 SFNs generation

Input: N, r, thr, m

Output: A

1: Randomly deployed N nodes

2: for i = 1 : m do

3: Find Ncand its neighbors

4: Select nodes to form clique

5: Update A

6: end for

7: endfor

8: for j = m : N do

9: Randomly select Nj

10: Calculate djof the neighbors

11: if dj== 0 then

12: Randomly select neighbor

13: else if dj>0 && dj6= thr then

14: Calculate Probability

15: Πdi=di

Pjdj

16: end if

17: endif

18: Use roulette wheel method

19: Select mnodes

20: Update A

21: end for

22: endfor

roulette wheel method and the adjacency matrix is updated (Lines 16-18). The

process is repeated until all the nodes become connected.

Algorithm 8deﬁnes the edges’ degree based swap operation. In place of random

selection [62], the edges are made by considering the degree. The degree based

edge swap ensures that the nodes with a similar degree are connected. Hence,

the network structure becomes onion-like. This operation executes after the SFNs

topology generations in IoT. The algorithm uses the following variables.

•A: adjacency matrix of initial SFNs.

•Aup : adjacency matrix obtained after the swap.

Two nodes Niand Nkare randomly selected from the graph (Line 2). After

that, the neighboring nodes Njand Nlthat have high degrees and are in the

communication range of Niand Nkare selected (Lines 3-6). If the obtain edges

are independent then the degree of each edge is calculated (Lines 7-10). More-

over, for all the possible edges the diﬀerence of edge degree is calculated (Lines

26 Thesis by: Muhammad Usman

3.4. DETAILS OF ISFNS ALGORITHMS

Algorithm 2 Edges’ degree based swap

Input: A, N

Output: Aup

1: for all N∈Gdo

2: Find Niand Nk

3: if Ni6=Njthen randomly select

4: Nineighbor Nj

5: Nkneighbor Nl

6: end if

7: end if

8: if eij && ekl are independent then

9: Calculate dij =pdi×djand

10: Calculate dkl =√dk×dl

11: end if

12: end if

13: Calculate diﬀerence of edge degrees

14: DIF0=|dij −dkl |

15: DIF1=|dik −dj l|

16: DIF2=|dil −dj k|

17: EDD = min(DIF0,DIF1,DI F2)

18: if EDD == DI F1then Swap edges

19: eil &eij

20: Calculate robustness Rup

21: else if EDD == DI F2then Swap edges

22: eik &ejl

23: Calculate robustness Rup

24: end if

25: Endif

26: if Rup > R && Network is connected then

27: Update Aup ←A

28: end if

29: end if

30: end for

31: end for

11-14) and the minimum degree diﬀerence is selected (Line 15). The edges are

swapped according to the minimum diﬀerence in edge degree and after each swap,

the robustness is calculated (Lines 16-22). The updated robustness value is com-

pared with the initial network robustness for all the edge swaps and for which the

robustness is high the adjacency matrix is updated (Lines 23-24).

The algorithm 3explains the nodes’ distance based edge swap method. This swap

is performed after the edge’ degree based swap. The following variable is used in

this algorithm.

27 Thesis by: Muhammad Usman

3.4. DETAILS OF ISFNS ALGORITHMS

Algorithm 3 Nodes distance based edge swap

Input: A, N

Output: Aup

1: for all N∈Gdo

2: Randomly select Niand Nk

3: if Ni6=Nkthen

4: Select neighbors Njand Nl

5: else

6: Randomly select Nk

7: Select neighbors Njand Nl

8: end if

9: endif

10: if eij are independent of ekl then calculate

11: D1for Ni, Nj

12: D2for Nk, Nl

13: Avg0= Avg(D1, D2)

14: Repeat Steps 10-12 for edge swap

15: (eik, eij ) and (eil , ej k)

16: end if

17: endif

18: ND = min(Avg0,Avg1,Avg2)

19: if ND == Avg1&& R(Aup)≥R(A)then

20: Update Aup ←A

21: else if ND == Avg2&& R(Aup)≥R(A)then

22: Update Aup ←A

23: end if

24: endif

25: end for

26: endfor

Aup : Updated adjacency matrix.

In the graph Gof nodes N, randomly select nodes Niand Nk(Line 2). Then

the neighbor nodes of Niand Nkare found (Lines 3-8). If the edges (eij,ekl) are

independent then the distances D1and D2are calculated between the nodes that

form the independence edges (Lines 10-11). The average of D1and D2is calculated

(Line 12). For the other possible edges’ swaps, repeat steps 10-12 (Lines 13-14).

The edge swap is selected against which the average distance is minimum (Line

16). If the original edge pair has the minimum average distance then the original

topology is kept. On the other way, the edges that have the minimum average

distance are selected. If the robustness is increased and the distance between

the nodes is minimum then the adjacency matrix is updated (Lines 17-20). The

process is repeated until all the edges in the graph Gare examined.

28 Thesis by: Muhammad Usman

3.5. SIMULATION RESULTS

3.5 Simulation results

The performance of the proposed ISFNs scheme is evaluated in this section. After

the network evolution, the edges’ degree and nodes’ distance based swaps oper-

ations are used to optimized the network. Furthermore, the network robustness

is assessed against random and malicious attacks. Moreover, the network con-

nectivity against the variable attacks is studied to make the network robust. In

addition, the comparison of ISFNs is made with two existing schemes, ROSE and

SA against diﬀerent sized networks. Moreover, a system of core i7 having 7th

generation with 16GB RAM and 256GB SSD is used to perform simulations in

MATLAB.

3.5.1 Construction of scale-free network

To validate the performance of the proposed scheme, the synthetic network is

constructed. The sensor ﬁeld is set to 500×500m2in which the nodes are randomly

deployed. To make sure that each node is connected, the communication range

of nodes is set to 50% of the sensor ﬁeld. The high communication range enables

the nodes to form a dense network [?]. The maximum and minimum values of

each node’s degree is set to 25 and 2, respectively. Throughout the simulation, the

edge density m= 2. During the network evolution, the BA model is followed and

a single node is added at each interval and the new nodes follow the preferential

attachment.

3.5.2 Comparison of ISFNs with ROSE and SA

The performance of the proposed scheme is evaluated against the existing tech-

niques, ROSE and SA when N= 100. The comparison is shown in Fig. 3.4.

All the techniques improve the robustness of the network as compared to the ini-

tial network. However, the proposed scheme outperforms all the existing and the

initial network. The high robustness of ISFNs is due to the formation of better

onion-like structure. Moreover, the operations as part of the ISFNs reduce the

redundant operations to optimize the network against malicious attacks. From

the results, it can be concluded that the networks are initially robust and the

optimizations enhanced the robustness. The minimum value of SA is due to the

local optima problem. Moreover, due to the possible wrong selection of the center

node and the redundant operations caused by the degree diﬀerence and angle sum

operations reduced the performance of ROSE.

29 Thesis by: Muhammad Usman

3.5. SIMULATION RESULTS

Figure 3.4: ISFNs evaluation of robustness

3.5.3 Comparison of ISFNs with ROSE and SA against

low degree attacks

The performance of all techniques is analyzed against the high degree attacks. In

these attacks, the nodes are randomly removed from the network and the eﬀect

of nodes removal on robustness is assessed. The performance of all the optimized

networks against the low degree attack is shown in Fig. 3.5. All the techniques

Figure 3.5: Network’s performance evaluation against low degree attacks

have downward trends due to the low degree nodes removal. As the SFNs are

robust against the low degree nodes attacks. Therefore, the network robustness

decreases gradually. The high robustness against the low degree attacks conﬁrm

that the network remains scale-free after optimization.

30 Thesis by: Muhammad Usman

3.5. SIMULATION RESULTS

3.5.4 Comparison of ISFNs with ROSE and SA against

high degree attacks

SFNs collapse due to the malicious attacks on the high degree nodes. SFNs have

a very small number of high degree nodes that is due to the power-law followed

by these networks. The eﬀect of high degree attacks on the networks that are

optimized by diﬀerent techniques is shown in Fig. 3.6. The robustness of the

Figure 3.6: Network’s performance evaluation against high degree attacks

network decreases sharply after the high degree nodes are removed. All the tech-

niques have the downward trends, however, due to the change in robustness value,

the schemes could not be compared directly. Therefore, against the number of

removed nodes, the slope is analyzed. As in Fig. 3.6, the existing techniques and

the initial network collapsed after the removal of almost 20 nodes. However, the

proposed ISFNs scheme performs much better and the network fragments after

the removal of 40 nodes. The high robustness value ensures that the network is

optimized with the better formation of an onion-like structure. The two opera-

tions introduced in ISFNs proved their usefulness by providing a robust network

structure against malicious attacks.

3.5.5 Comparison of ISFNs with ROSE and SA against

random attacks

SFNs are robust against the random removal of nodes. The high robustness is

due to the presence of a large number of nodes with a low degree. Therefore,

the networks are analyzed against the random nodes removal. The network’s

performance when the random attacks happen is analyzed, as shown in Fig. 3.7.

After each attack, the techniques have downward trends because the nodes are

31 Thesis by: Muhammad Usman

3.5. SIMULATION RESULTS

Figure 3.7: Network’s performance evaluation against random attacks

removed from the networks. The robustness of ISFNs outperform the existing

schemes and initial network, due to better network structure. Almost 80% of the

nodes are required to be removed from the network to make it collapse. However,

the existing schemes and the initial network resist only against the removal of 60%

of the nodes. The small rise in the ROSE during the 20% and 35% of the nodes

removal is due to the random selection of high degree edges during the degree

diﬀerence operation. SA has less robustness as compared to ROSE and ISFNs

because in the network evolution process the constraint of sensor nodes such as

communication range and the threshold value of nodes degrees are not considered,

therefore, the removal of high degree nodes fragment the network.

Figure 3.8: Network’s performance evaluation against variable attacks

3.5.6 Comparison of ISFNs with ROSE and SA against

variable attacks

The network’s connectivity against the removal of diﬀerent number of nodes is

analyzed in the variable attacks. Moreover, against the multiple nodes’ removal

32 Thesis by: Muhammad Usman

3.5. SIMULATION RESULTS

from the network, robustness is calculated and the number of removed nodes

is randomly selected. The network performance against the variable attacks is

shown in Fig. 3.8. When the number of removed nodes is small, the maximum

number of nodes is present in the MCS. However, as the number of removed

nodes is increased, the nodes in MCS decrease. SA and ROSE performed worst,

however, ISFNs outperform the existing techniques. During the simulations, 3

diﬀerent numbers of nodes are randomly generated and against all these, ISFNs

has better results. So, these results prove the importance of edges’ degrees and

node’s distance based swaps operations to form the onion-like structure.

Figure 3.9: Network’s performance evaluation against diﬀerent number of

nodes

3.5.7 Network robustness against diﬀerent number of nodes

The comparison between ISFNs and the existing techniques for diﬀerent sized

networks is presented in Fig. 3.9. The increase in the network size causes the ro-

bustness of the network to decrease. It is due to the availability of a large number

of high degree nodes within the same sensor ﬁeld. Therefore, the removal of high

degree nodes has a severe eﬀect on the network connectivity. All the techniques

have the same downward trends with the increase in the number of nodes. How-

ever, ISFNs outperforms the existing techniques for diﬀerent number of nodes. It

is due to the better onion-like structure formed by the ISFNs operations. From

the results, it is shown that the proposed scheme is better, when it is implemented

on the dense and complex networks.

33 Thesis by: Muhammad Usman

3.6. CONCLUSION OF THE CHAPTER

3.6 Conclusion of the chapter

Many critical applications have a scale-free nature. These networks have high

robustness against the removal of nodes with a low degree, however, the high degree

nodes removal collapses the network. Therefore, in this chapter, the robustness

of the SFNs is increased by the proposed ISFNs scheme. In this scheme, two

operations, i.e., edges’ degree and nodes distance based edge swaps are used. In

the edges’ degree based operation, the edges are swapped to make the links with

similar degree nodes. Whereas, the nodes’ distance based operation is used to form

the links between the neighboring nodes that are closed to each other. Moreover,

the network is analyzed against the variable attacks. In this attack, diﬀerent

numbers of nodes are randomly removed from the network and their eﬀect on

the network connectivity is calculated. The degree distribution is not changed by

the ISFNs’s operations, therefore, no extra cost is required. After optimizing the

network, it remains scale-free and is compared with the existing techniques: ROSE

and SA. It is proved that against diﬀerent network sizes the ISFNs outperforms

the existing techniques.

34 Thesis by: Muhammad Usman

Chapter 4

Network’s topology evolution scheme

35

4.1. SUMMARY OF THE CHAPTER

4.1 Summary of the chapter

In this chapter, against the random and malicious attacks on scale-free networks

a Network Topology Evolution Scheme (NTES) is proposed. In this scheme, the

network ﬁeld is divided into two parts with uniformly distributed nodes. After the

network’s evolution, the nodes are linked with each other through one-to-many

correspondence. The division of a network ﬁeld is made by considering that a

network is robust if its size is small. Moreover, to study the hierarchical changes

in the degree of the nodes the k-core decomposition is used. In addition, nodes’

degree and their core based attacks are performed and against these attacks the

proposed scheme is evaluated. Furthermore, the network robustness is analyzed

using three optimization techniques: Artiﬁcial Bee Colony (ABC), Bacterial For-

aging Optimization (BFO) and Genetic Algorithm (GA). The results are compared

and a technique that eﬃciently optimizes the network to increase the robustness

is selected. In the optimization process, we make use of three edge swap methods.

Due to the edge swap, the degree distribution is not changed so, no extra cost for

adding nodes or links is required.

4.2 Scale-free model

For the SFT, the details about the network evolution and the scale-free property

of the network is veriﬁed in this section. Initially, the SFT is constructed using

the BA model. Then, to enhance the network robustness, three types of edge

swapping methods are discussed. Moreover, to calculate the robustness of the

SFT, the metric of robustness is studied.

4.2.1 Construction of Scale-Free Network

The operation of dense networks are diﬃcult as they are highly vulnerable to the

attacks that occur on the network links or nodes. The real-world examples of

dense networks are hospitals, military, transportation, etc. The drawback of these

networks is that their operational eﬃciency deteriorates once the attacks happen.

Therefore, the networks are divided into smaller networks by the graph partition

concept. The small networks’ operations are easy, processing is fast, eﬃciency is

high and the failures or removal of nodes have less eﬀect on the overall networks.

In this study, we have made a synthetic network by assuming that small-sized

networks are more robust and easy to maintain than large-sized networks. To

make a small-sized network, the network ﬁeld is divided into two equal parts

36 Thesis by: Muhammad Usman

4.2. SCALE-FREE MODEL

and nodes are randomly deployed. In both parts, the network evolves with equal

number of nodes. The node at the center of the network broadcasts a request

message to its neighboring nodes. Based on the response time, the initial nodes

connect to the center node. After that, the remaining nodes join the network

based on the preferential attachment, as in [57]. The complete process of network

evolution is shown in Fig. 4.1. The dotted line represents the partition of the

NA

NB

NM

CL

ML

C1

C2

C3

C4

Ncu

Ncl

Figure 4.1: Network evolution by adding edges

network, and Ncu and Ncl are the center nodes of the upper and lower networks,

respectively. The network growth starts from the center nodes. After the network

evolves, its both parts are linked by one-to-many correspondence. Moreover, to

increase the network robustness edge swap is performed.

4.2.2 Details of edge swap

The graph theory is used to represent the SFT. With the help of graph theory,

the network is converted to a graph (G) in which the nodes are represented as a

set of vertices V={1,2, ..., N}, whereas, the links between the nodes are shown

as edges E={eij |i, j ∈Vand i6=j}. So, the graph is G= (V, E) and it is

undirected and unweighted graph used to evaluate robustness of SFT. The edge

swap of independent edges is performed to enhance the robustness. Two edges are

independent if all the nodes of these edges are in the same communication range

and they have no extra edge.

In Fig. 4.2, the original topology with the possible ﬁrst and second edge swaps

are given. As seen in Fig. 4.2(a), the initial topology’s nodes i, j, k and lhave

independent edges eij and ekl. In Fig. 4.2(b) and Fig. 4.2(c), the ﬁrst and second

possible edge swaps are represented, respectively. Against all the possible edges,

37 Thesis by: Muhammad Usman

4.2. SCALE-FREE MODEL

the robustness is calculated and a pair of edges is selected that gives the highest

robustness.

To enhance the robustness, the edge swap is important because no extra cost is

required to add new node or edges. Therefore, the following types of edge swap

methods are implemented.

1. Edge swap of randomly selected nodes

2. Edge swap of degree based selected nodes

3. Edge swap of distance based selected nodes

4.2.2.1 Edge swap of randomly selected nodes

Nodes iand jare chosen at random from the network to perform a random

edge swap. Then the nodes kand lare selected in the neighborhood of iand

j, respectively. Edges eij and ekl should be independent to make the edge swap.

The robustness is calculated for each edge swap. If the robustness of the network

is increased, then the network is updated. However, new independent edges are

found in case of robustness is not improved. Since, there are many nodes in the

network with a low degree, therefore, in this edge swap mechanism, the probability

of these nodes selection is high.

i

l

j

k

i

l

j

k

i

l

j

k

(a) (b) (c)

Figure 4.2: Edge swap mechanism

4.2.2.2 Edge swap of degree based selected nodes

The degree of the nodes is being used to perform a degree-based edge swap. Ini-

tially, from the network, a high degree node is selected, then a low degree node

from its neighboring region is chosen. The same method is followed for the other

pair of nodes. If the edges are independent, then the edge swap is made, as in

Fig. 4.2. This edge swap connects similar degree nodes. Against all possible

38 Thesis by: Muhammad Usman

4.2. SCALE-FREE MODEL

edge swaps, the robustness is calculated and the edge swap that enhances the net-

work robustness is selected. To reduce the possibility of similar edge selection, the

edges are marked. So, in the next edge swap, these edges are not selected, hence,

computational cost is reduced.

4.2.2.3 Edge swap of distance based selected nodes

In this edge swap method, independent edges in the network are marked and

Euclidean distance is calculated against all the nodes. The edge swap is made

in such a way that longer links are formed between network nodes. Against the

attacks, the existence of long links make the network robust. After making the

long links, the network robustness is calculated. If the network is fully connected

and the robustness is increased the edge swap is accepted and vice versa.

4.2.3 Metric of robustness

When the network is fully connected, it has maximum ability to perform its ser-

vices. The nodes’ failure or removal greatly reduces the network’s performance.

Usually, the importance of a node is calculated based on its degree [14]. In the

network, a node having a high degree is more important than the rest of the nodes.

In this thesis, malicious attacks happen on the nodes based on their degree. To

perform the malicious attacks, the nodes’ degree is calculated and the highest

degree node is removed from the network. For the remaining nodes, the degree

is recalculated and a node having the highest degree is removed. The removal of

nodes is carried out until the network becomes fragmented. These attacks are more

vulnerable for the SFT, therefore, a metric is required to calculate the robustness.

In recent papers, percolation theory based metric is proposed by Schneider et al.

[131], which is extensively used to calculate the robustness of SFT. In this metric,

when the high degree node is removed, the network collapses and is partitioned

into multiple parts known as subgraphs. The network’s robustness is determined

by the number of nodes present in MCS. To calculate robustness R, we use the

following formula.

R=1

N+ 1

N+1

X

n=0

MCSn

N,(4.1)

where, N,1

N+1 and MCSnare network size, the normalization factor and the

number of nodes in MCS when nth high degree node is removed, respectively.

39 Thesis by: Muhammad Usman

4.3. NETWORK TOPOLOGY EVOLUTION SCHEME OVERVIEW

The robustness value lies in the range of (0, 0.5). The minimum value of robustness

is 0, which means the network is fully collapsed. Whereas, the maximum robust-

ness value is 0.5. Due to the limited resources of sensor nodes, the maximum value

is always less than 0.5.

4.3 Network Topology Evolution Scheme overview

In this section, the NTES is proposed to enhance the robustness of SFT. NTES

provides solutions for the decentralized system. The scheme is designed to be

robust against malicious attacks by forming onion-like structure. In this structure,

the center nodes of the network have a high degree. The nodes’ degree decreases

hierarchically when we move away from the center.

Considering the importance of the onion-like structure for the robustness of SFT,

the network topology is constructed. The NTES consists of the following opera-

tions: network topology evolution, networks connection by one-to-many correspon-

dence, SFT attacks, core based attacks and a comparison of heuristic algorithms

to optimize the NTES’s robustness is made.

4.3.1 Evolution of network topology

For malicious attacks, a small sized network is robust. The results can be observed

from [62], [70], [72]. Therefore, the network ﬁeld is divided into two parts and

nodes are uniformly distributed in it. During the evolution of both parts, the

power-law is followed. The connection of nodes have a major role in network

robustness. The one-to-many correspondence is better as compared to one-to-one

correspondence [132]. Therefore, the connection of both parts is made by one-to-

many correspondence. The high degree nodes of one part connect with low degree

nodes of the other part of the network. Thus, the degree of the edges becomes

smaller, therefore, the eﬀect of the malicious attacks on the links decreases. Two

networks’ topology evolution following the power-law distribution are shown in

Fig. 4.1. The network ﬁeld division is indicated by the dotted line and the

two portions have the same number of nodes. The blue nodes (NA) and black

nodes (NB) represent the network A and B, respectively. Whereas NMdenotes

the mutual nodes of the network that exists in both parts. The black solid lines

and double dashed lines denote connectivity links CLand the mutual links ML,

respectively. During the network evolution, the nodes are added asynchronously

in both parts.

40 Thesis by: Muhammad Usman

4.3. NETWORK TOPOLOGY EVOLUTION SCHEME OVERVIEW

4.3.2 k-core based nodes’ degree distribution

Diﬀerent rings based on the degree of the nodes are presented in the onion-like

structure. In each ring, the nodes with the same degree are connected [62]. The

computational cost is high to collapse the network with malicious attacks based

on degree. Therefore, due to the availability of information about a speciﬁc node

in the core, less computational cost is incurred. As a result, in each ring, k-

core decomposition determines the nodes’ degree and a node is removed from it

respective ring based on its importance. For these nodes, the core based onion-like

structure is shown in Fig. 4.1.

In k-core decomposition, the cores are created by removing the nodes from the

network. In Fig. 4.1, core C4 contains the IDs of nodes having a low degree,

which are initially removed from the network. Then the other low degree nodes

are removed after recalculating the degrees and their information is stored in the

next core C3. The node removal process is repeated until all of the high degree

nodes have been removed and placed in the internal core C1. Due to the power-

law, a long tail of nodes with low degree presents in SFT. Hence, the removal

of a high degree node from the network causes a speciﬁc part of the network to

collapse. Therefore, in that case, less computational cost is required to damage

the network.

4.3.3 Attacks on the proposed topology

It is assumed that the attackers carry complete network topology information and

can execute any attack to collapse the network. Therefore, having the knowledge

about the speciﬁc type of attack make the defender capable to manage it. To

increase the eﬀectiveness of the proposed NTES, nodes’s core and degree based

attacks are considered. In core based attack, the nodes are removed from their

respective core. The node of the inner core are removed ﬁrst then the nodes of the

outer cores are removed. The core based attack is shown in the Fig. 4.3, where

the nodes NRare removed from the inner core. The network is not disturbed by

removing these nodes. However, as the number of removed nodes are increased

the network is fragmented into multiple subgraphs as shown in Fig. 4.4. Three

subgraphs S1, S2 and S3 are made after the core based attacks are happened on

the network. Furthermore, in each subgraph, the high degree nodes NM CS are

present. These nodes are removed to fully collapse the network.

Moreover, the High Degree Adaptive (HDA) attack is considered to remove the

nodes based on the degree. In this attack, the degree of nodes’ present in the

41 Thesis by: Muhammad Usman

4.3. NETWORK TOPOLOGY EVOLUTION SCHEME OVERVIEW

NA

NB

NM

NR

CL

ML

Figure 4.3: Nodes’ attack based on core

NA

NB

NM

NR

NMCS

CL

ML

S1

S2

S3

Figure 4.4: Attacks on NMC S of the network

network is calculated and the highest degree node is removed. Again, the highest

degree node is removed by recalculating the degree. This process is repeated until

all the nodes are removed from the network.

4.3.4 NTES’s optimization by heuristic algorithms

The NTES is optimized by three heuristic algorithms including GA, ABC and

BFO. In GA, the edge swap is performed by considering the exclusive edges [69].

However, in both ABC and BFO, for the better exploration and exploitation

a random position change is required. In the proposed scheme, the nodes are

stationary, therefore, it is not possible to change positions at random. To deal with

this problem, random and degree based edge swaps are used for the exploration

and exploitation, respectively. The exploitation is performed by exploiting local

information of nodes that is the degree of nodes to perform edge swap. However,

42 Thesis by: Muhammad Usman

4.4. DETAILS OF NTES ALGORITHMS

when the solution traps into the local optima the exploration by random edge

swap is performed.

Table 4.1: Mapping the identiﬁed limitations, their proposed solutions with

validations

Identiﬁed limita-

tions

Solutions proposed Validations done

L1: The eﬀects of

malicious attacks are

severe on large-sized

networks [57,66]

S1: NTES is proposed

in which small-sized

networks evolve

V1: The small-sized

networks are robust to

random and malicious

attacks, as shown in

Fig. 4.8(b)

L2: The links degree

that connect the net-

works do not follow

the power-law [132]

S2: Using the concept

of the interdependent

links, the networks are

connected

V2: The power-law

degree distribution is

validated for the mu-

tual nodes, as shown

in Fig. 4.6(a)

L3: The change

of node’s degree in

each ring considering

onion-like structure is

not known [57]

S3: The same degree

nodes are found using

k-core decomposition

V3: The nodes are re-

moved based on their

degrees, and degree

based cores are cre-

ated in Algorithm 5

L4: Random edge

swap increases the

number of redundant

operations [66]

S4: Long links are

created through dis-

tance based edge swap

V4: Existence of long

links enhances the

network robustness,

as shown in Fig.

4.8(a)

Table 4.1 presents the complete details of the limitations that are identiﬁed through

the literature review. Then their proposed solutions and how they are validated

is given.

4.4 Details of NTES algorithms

In this section, the complete details of the algorithms to increase the robustness of

SFT are presented. Algorithm 7describes the network topology evolution process.

The details of the variables used in the algorithm are as follows. N,r,E,m,thr,

N.nei and Arepresent the total number of nodes, communication range, number

of edges, edge density, threshold value of maximum degree, number of neighbors

and adjacency metric, respectively.

This algorithm works as follows. Initially, based on the coordinates, the network

ﬁeld is divided into two parts and nodes are uniformly random deploy (Lines 2 and

43 Thesis by: Muhammad Usman

4.4. DETAILS OF NTES ALGORITHMS

Algorithm 4 Network topology evolution

Input: N, r, E, m, thr

Output: N.N ei, A

1: procedure Network generation

2: Divide the network ﬁeld based on coordinates

3: Random deployment of nodes

4: Find Ncu

5: for all ni∈Ndo

6: Center node broadcasts request message

7: CReq →Neighboring nodes

8: Ni←CReq

9: if DNi== 0 then

10: Make an edge with the node that replies ﬁrst

11: else

12: Calculate Pk

13: Pki=ki

Pjkj

14: end if

15: end if

16: Sort Pk

17: if DNH6=thr then

18: Select NH

19: else

20: Select Second NH

21: end if

22: end if

23: if m≥2then

24: Roulette wheel based node selection

25: end if

26: end if

27: Update: N1.N ei

28: Repeat the process for lower part of the network

29: Update: N2.N ei

30: Connect both parts using Nm

31: Update A

32: end for

33: end for

34: end procedure

35: end procedure

3). After the network ﬁeld partition and nodes’ deployment, a center node Ncu is

found (Line 4). The Ncu node broadcasts the connection request message to all the

nodes in its neighborhood (Lines 6 and 7). The replies are sent by the neighboring

nodes (Line 8). If all the neighboring nodes have zero degrees, then an edge is

made with the node that replies ﬁrst (Line 10). Moreover, if the degree of the

neighboring nodes is diﬀerent, then the connection probability is calculated (Lines

44 Thesis by: Muhammad Usman

4.4. DETAILS OF NTES ALGORITHMS

12 and 13). The addition of nodes in the network is based on the edge density m.

Due to the limited communication range of the nodes, the threshold value of nodes’

degree is ﬁxed. Hence, a new node only connects with a node that has not reached

the maximum degree limit (Lines 15-20). Moreover, if the neighboring nodes have

diﬀerent degrees, then mnodes are selected by roulette wheel selection (Lines 21

and 22). After that, the list of neighboring nodes is updated (Line 24). The

complete process is repeated for the lower part of the network and the neighbor

list is updated (Lines 25 and 26). Both parts of the network are connected by

the mutual nodes Nmand the links follow the power-law distribution (Line 27).

Finally, the adjacency metric is updated (Line 28).

To improve the robustness of SFT, Algorithm 5performs edge swap. The edge

swap increases the number of nodes in the MCS, which makes the network more

robust. The variables used in this algorithm are N, N.N ei and A. From the

graph G, two diﬀerent nodes Niand Nkare randomly selected (Lines 3 and 4).

Then the neighboring nodes are found randomly (Lines 5-7). Afterwards, the

initial robustness Riis calculated (Line 8). The interdependency of the edges is

checked and the robustness is calculated after each edge swap (Lines 10-14). The

edge swap that results in the maximum value of robustness is selected and the

adjacency metric is updated (Lines 16 and 17). Furthermore, in an edge swap

based on node degree, two nodes with higher degrees are chosen. Then, the low

degree neighboring nodes are found and the initial robustness is calculated (Lines

18-23). The edges should be independent and the edge swap is made in all the

possible ways and network robustness is calculated (Lines 24-28). The edge swap

resulting in the maximum value of robustness is selected (Line 30). The adjacency

metric is updated by considering the Nnum.

The attacks’ procedure for the robustness of SFT is explained in Algorithm 8.

After the network evolution, low degree nodes are found in the network (Line 3).

These nodes are removed and their indices are stored in the variable C(i) (Line

4). When all nodes are removed, then C(i) is converted into Ncthat contains

the number of nodes based on the cores (Line 5). The nodes are removed based

on core and the robustness is calculated (Line 7). For the degree based attacks,

the degree of nodes in the network is calculated. The robustness calculation is

performed, after the removal of high degree node (Lines 8 and 9). There is a

new calculation of a degree and the nodes with a high degree are removed (Lines

10-14). The process is repeated, until all the nodes are removed from the network.

The robustness is calculated after each high degree node is removed.

45 Thesis by: Muhammad Usman

4.5. SIMULATION RESULTS AND DISCUSSION

Algorithm 5 Edge swap strategies

Input: A, E, N

Output: A

1: procedure Edgeswap()

2: for all N∈Gdo

3: Select Random Node Ni

4: Select Random Node Nk

5: if Ni6=Nkthen

6: Nj=Neighbor of Ni

7: Nl=Neighbor of Nk

8: Calculate Ri

9: end if

10: end if

11: if eij && ekl are independent then swap

12: eik and ejl

13: R1

14: eil and ekj

15: R2

16: end if

17: end if

18: Nnum =max(Ri, R1, R2)

19: Update A according to Nnum

20: For degree based swap

21: Select Ni=NHD1

22: Select Nk=NHD2

23: Nj=Neighbor of Ni

24: Nl=Neighbor of Nk

25: Calculate Ri

26: if eij && ekl are independent then swap

27: eik and ejl

28: R1

29: eil and ekj

30: R2

31: end if

32: end if

33: Nnum =max(Ri, R1, R2)

34: Update A according to Nnum

35: end for

36: end for

37: end procedure

4.5 Simulation results and discussion

In this section, we ﬁrst make a synthetic network using the proposed NTES. The

networks are evolved by dividing the network ﬁeld and are connected by one-to-

many correspondence. The robustness is calculated after removing the high degree

46 Thesis by: Muhammad Usman

4.5. SIMULATION RESULTS AND DISCUSSION

Algorithm 6 Nodes’ degree and k-core based attacks strategy

Input: A, E, N , r

Output: RC, RD

1: procedure Attackonnetwork()

2: for all N∈Gdo

3: Find low degree node

4: C(i)←DN

5: Nc←C

6: Remove nodes based on Nc

7: Calculate RC

8: Find DN

9: Sort DN

10: Remove Dmax node

11: if Di6= 0 then

12: Recalculate DN

13: Sort DN

14: Remove Dmax node

15: end if

16: end if

17: end for

18: end for

19: end procedure

nodes from the network. For the optimization of the network, GA, ABC and BFO,

are used and their performance is compared based on robustness. Moreover, the

NTES is compared with BA model and HC algorithm to validate its performance.

The simulations are performed in MATLAB on a core i5, 6th generation system

having 8GB RAM and 512GB HDD.

4.5.1 Network topology evolution

The nodes are randomly deployed in the network ﬁeld of 500 ∗500m2and the

total number of nodes is 100. The nodes’ communication range is 50% of the

total size of the network ﬁeld. Since our work is to make small-sized networks,

therefore, based on the coordinates, the network ﬁeld is divided into two parts.

The nodes are uniformly distributed in both parts. Due to the nodes’ resource

constraints, the minimum and maximum values of the nodes’ degree are set to 2

and 25, respectively.

4.5.2 Attacks on upper and lower networks

After the completion of network topology evolution, on both parts of the net-

work the attacks are performed. In random attacks, nodes are removed randomly,

47 Thesis by: Muhammad Usman

4.5. SIMULATION RESULTS AND DISCUSSION

however, in malicious attacks based on degree the nodes are removed. The eﬀect

of these attacks is shown in Fig. 4.5. To eﬃciently analyze the eﬀect of nodes’

Figure 4.5: (a) Malicious and random attacks on upper network. (b) Malicious

and random attacks on the lower network

removal on the robustness of the network, two nodes are simultaneously removed

during each attack. Initially, the robustness remains the same, as shown in Fig.

4.5(a). However, as the number of removed nodes is increased, the malicious at-

tacks become severe as compared to random attacks. When 15 pairs of nodes are

removed, the malicious attacks damage the network more. Moreover, against the

random and malicious attacks, the diﬀerence in robustness is small that proves the

eﬀectiveness of the proposed technique. Moreover, the value of robustness is not

zero against random and malicious attacks. It is because the attacks are performed

on one part of the network, however, the second part is still connected. The lower

part of the network followed the same approach. The malicious and random at-

tacks eﬀect is diﬀerent for the lower part of the network, as shown in Fig. 4.5(b)

because of the separate network evolution. Initially, when the small number of

nodes are removed, the eﬀect of random and malicious attacks is the same. How-

ever, as the number of removed nodes is increased, the malicious attacks become

48 Thesis by: Muhammad Usman

4.5. SIMULATION RESULTS AND DISCUSSION

more vulnerable than the random attacks. The robustness value becomes constant

when 88% of the nodes are removed and the network is completely fragmented.

4.5.3 Networks connection by one-to-many correspondence

When the nodes’ degree follow the power-law distribution, the networks become

robust against the random attacks. Therefore, the mutual nodes are connected

by considering the power-law. In Fig. 4.6(a), for the mutual nodes the degree

distribution is shown. dis the degree of node and P(d) represents the probability

of nodes having the degree d. As per the deﬁnition of the power-law, the number

of nodes having high degree should be less as compared to the number of low

degree nodes. The results show that the low degree nodes outnumber the high

Figure 4.6: (a) Power-law distribution of the mutual nodes (b) Comparison

of core based attacks and high degree node attacks

degree nodes. So, the power-law is followed by the mutual nodes. Moreover, due

to the predeﬁned limit of the nodes to connect with the other part of the network,

only a few nodes are present in the mutual part of the network.

49 Thesis by: Muhammad Usman

4.5. SIMULATION RESULTS AND DISCUSSION

4.5.4 Core based attacks on network

The core based attacks are performed in Fig. 4.6(b) to study its eﬀect on the

robustness of the SFT. The removal of nodes starts from the inner core that

contains the important nodes based on degree. The robustness is calculated with

the removal of nodes from the outer core. The outer