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COMSATS University Islamabad, Islamabad Campus

Synopsis For the Degree of M.S/MPhil. PhD.

PART-1

Name of Student Muhammad Usman

Department Department of Electrical and Computer Engineering

Registration No.

CIIT/FA18-REE-016/ISB Date of Thesis Registration March 13, 2021

Name of

(i) Research Supervisor

(ii) Co-Supervisor

Dr. Nadeem Javaid

Dr. Sardar Muhammad Gulfam

Research Area Internet of Things (IoT)

Members of Supervisory Committee

1 Dr. Sardar Muhammad Gulfam

2 Dr. Nadeem Javaid

3 Dr. Mariam Akbar

4 Dr. Saif ur Rehman Khan

Title of Research Proposal MS Synopsis on enhancing robustness of scale-free IoT networks

against random and malicious attacks

Signature of Student:

Summary of the Research

In this synopsis, robustness of the Scale-Free Networks (SFNs) is enhanced against malicious

attacks through optimization. To achieve this, the edge’s degree and nodes’ distance 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, 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. Furthermore, to make the network robust

against realistic attacks, the variable attacks are considered. Apart from that, a Network Topology

Evolution Scheme (NTES) is proposed to prevent SFNs 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 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 required

to increase the robustness. In addition, the network robustness of SFNs is enhanced against the

malicious attacks. For that purpose, initially, a parameterless 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 topological 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.

1

Table 1 List of Abbreviations

Abbreviations Description

AI Artiﬁcial Intelligence

BA Barabási Albert

CF Cascading Failures

CPS Cyber Physical System

DALR Degree Addition Link Rewiring

DAO Degree Associativity Operation

DB Degree and Betweenness

DBA Degree Based Attack

DDO Degree Difference Operation

DN Degree of Node

ENC Empirical Necessary Condition

GA Genetic Algorithm

GDU Gene Distribution Unit

GPCR Gene Position Coverage Ratio

HC Hill Climbing

HDA High Degree Adaptive

HDO High Degree Operation

IoTs Internet of Things

MA Memetic Algorithm

MAGA Multi-Agent Genetic Algorithm

MCS Maximam Connected Subgraph

MIP Mixed Integer Programming

MOO Multi-Objective Optimization

MPGA Multi Population GA

RSF Robustness of Scale-Free network

SA Simulated Annealing

SFNs Scale Free Networks

WSNs Wireless Sensor Networks

1 Introduction

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.

For the effective communication between IoT nodes, different network topologies are con-

sidered [4]. The two widely used topologies are Small World Topology (SWT) [5] and Scale-

Free Topology (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 band-

width 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.

2

In [6], Barabási Albert (BA) model is used to form a SFT based network by following 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 Maximum Connected Subgraph

(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 resources, therefore, the Scale-Free

Networks (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 impor-

tant role in the robustness enhancement. Therefore, in literature, the community structure in

analyzed against the random attacks, malicious attacks and cascading failures [33–36, 38–56].

1.1 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 tech-

niques, the networks are becoming dense. The size of a network has an effect on network ro-

bustness and with the increase in a network size the robustness decreases. To address the afore-

mentioned problem, a Network Topology Evolution 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 algorithms, Genetic Algorithm (GA), Bacterial Foraging Opti-

mization (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 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

3

adding the nodes in the MCS after the network fragments. Moreover, the network’s performance

is analyzed against different attack strengths.

2 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 fragment these networks into multiple sub-

graphs. 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 synopsis, 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 en-

ergy 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 topol-

ogy 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 topol-

ogy 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 net-

work 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 Simulated Annealing (SA). The edges are classiﬁed with the removal of nodes

from 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.

4

However, in some problems, it is difﬁcult to optimize the network with single objective op-

timization. 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 Evolution-

ary 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 objec-

tive 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. There-

fore, a directed network with the emergence 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 movement has great importance

to complete the tasks. Therefore, the collision and obstacle avoidance control strategies are

studied in the [80, 81]. Moreover, the secure 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 proposed [86]. Moreover, the

efﬁcient routing is performed in [87–89] The routing is made efﬁcient by managing the energy

consumption of the sensors. Furthermore, 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 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

5

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 between-

ness centrality attack becomes more vulnerable to the network. For SFNs onion-like structure is

not considered because in that structure 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 increased [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 second 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

implement 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 per-

formed better than the betweenness centrality because it is based on a cheap-to-compute network

matrix combination. The proposed system enhanced the robustness of scalable networks, there-

fore, 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 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’ connectivity is studied. However, the mali-

cious 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 network 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 robustness 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 charac-

teristics information in [114–117].

To enhance the network robustness against the malicious attacks, the parameterized net-

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

robustness is increased against the random and malicious attacks without increasing the number

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]. Furthermore, the robustness structure

and the effects nodes removal are extensively studied in [126–130]

6

Table 2 Problems addressed by previous work.

Problem identiﬁed Proposed solutions Validations Limitations

SFNs survivability and ro-

bustness after cyber at-

tacks

• Topology evolution

based on fault probability

• Considered two opera-

tions i.e., HDO and DAO

• Four types of attacks

considered that make

TMSE resilient against

real life attack

• Results are validated by com-

paring with the existing algo-

rithms

• TMSE outperforms the exist-

ing techniques of different 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 probability

• DAO not considered the fault

probability that increase the ef-

fect of attack [65]

Multiple attacks on a net-

work simultaneously

Uses multi-objective opti-

mization to enhance the

robustness

• Effects of attack on topologi-

cal features validate the impor-

tance of MOEA

• Synthetic and real-life net-

works prove the importance of

MOEA

• High computation cost due to

calculation of multi-objectives

• Topology improvement for

simultaneous attacks not dis-

cussed

• Pareto set generation can be

difﬁcult [74]

Premature convergence of

classical GA

Uses multi-populations

co-evolution to solve

premature convergence

Comparing with existing algo-

rithms MPGA enhances the ro-

bustness

• Optimal population size

should be chosen

• Local operator should be

used to make diverse popula-

tion [69]

Premature convergence of

classical GA

• Self-competition among

the individuals

• Calculate population di-

versity using GPCR and

GDU

• Mutation probability cal-

culated by adaptive adjust-

ment

• Performance is validated with

different algorithms and at-

tacks

• Less time cost as compared

to ROCKS which is multi-

population algorithm

• Diversity calculation is a

complex process at every oper-

ation

• GPCR and GDU have high

computation cost

• Uses multiple operations

which make computation cost

high [71]

Link attack based on be-

tweenness centrality has

high computational com-

plexity

• Link attack based on

shell has better perfor-

mance than betweenness

centrality

• Shell based attack has

less computational com-

plexity

• Importance of shell-based

attacks validated for different

types of network

• Shell-min, shell-max and

shell-pro attacks compared

with betweenness centrality

• Less destructive when the

fraction of removed nodes is

high

• Less effective for SFN due to

not following onion-like struc-

ture

[59]

No practical approach

available to understand

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 converge

by shrinking search space

• RER helps to rectify

ENC

• Importance of RER is vali-

dated for different number of

nodes

• Network connectedness im-

proved by increasing number

of RER operations

• Exhaustive attack is not actu-

ally considered

• Not explain RER for undi-

rected network

• RER changes nodes degree

• Due to RER SFN not remains

scale-free [79]

The vulnerability of SFNs

due to malicious attack

• Constructed network

considering communica-

tion range of nodes

• DDO and ASO are used

to construct onion-like

structure

• Through ROSE topology con-

verted to onion-like structure

• Network becomes robust

against malicious attack

• Target all nodes in the net-

work may generate redundant

operations

• DDO and ASO are computa-

tionally expensive [62]

High computational com-

plexity of existing algo-

rithms is a hurdle in topol-

ogy self-optimization

For the self-optimization

AI based technique is used

• Efﬁciency and loss function

for training and testing vali-

dated

• 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]

Continued on next page

7

Table 2 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

Due to random edge swap

without considering the

network structure redun-

dant operations are per-

formed

• After attack edges are

classiﬁed into three types

• By increasing the valid

edges robustness enhances

• For HDA attack algorithm

performance is validated for

different size of networks

• The heuristic algorithm main-

tains the robustness

• Edge swap between invalid

edges enhances the computa-

tional overhead

• When nodes fail the edges

with the nodes also removed

[68]

• Network robustness de-

creases when the nodes re-

moved

• Optimization algorithms

fall into local optima

• NC is used to measure ro-

bustness

• Chaotic GA is used to op-

timized network

• To avoid local optima lo-

gistic maps based power

function carrier used

• Degree distribution re-

mains unchanged after op-

timization

• NC increases with increasing

iterations [136]

• NC and r have positive

correlation that proves enhance

robustness

• Onion-like structure is

achieved through optimization

• Considering different attacks

network is robust

N/A

• Community detection al-

gorithms reveals individ-

ual information

• Practical approach re-

quired to protect these in-

dividual’s privacy

• Network is optimized by

using heuristic technique

• CDA and HDA per-

formed to detect commu-

nity and high degree nodes

• Q-based attack based on

GA is performed

• Addition and removal of

links increase privacy on

individual

• Different P

cand P

mare used

to ﬁnd optimal parameter for

GA

• For all networks Q-based at-

tack outperformed

• Decrease in Q value conﬁrms

the privacy of individual

• Randomly addition and re-

moval of links affect SFN

• Addition of links have cost

• Onion-like structure is not

follow

• Directedness is an impor-

tance feature for a network

•f(c)and Rdifﬁcult to op-

timize simultaneously

• Correlation between f(c)

and Rproves the negative

correlation

• MOEA is used for these

objective

• r is used to set network

structure

• PD is used to calculate

f(c)

• MCS is used to calculate

R

• Pareto optimal solution

obtain for both objectives

• MOEA optimized both objec-

tives better than single objec-

tive algorithm

• Different networks are opti-

mized

•Ris assortative whereas f(x)

disassortative therefore, onion-

like structure is obtained by

shufﬂing edges

• No comparison is made

with other multi-objective al-

gorithms [78]

• Only assortativity is consid-

ered

• Other topological parameter

may prove positive correlation

of these objectives

• 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 at-

tacks

• Swap edge enhances robust-

ness

• Increase in number of nodes

decreases network robustness

• Assortativity and clustering

increase

• Random edge swap effects

network structure [57]

• Average shortest path length

increased

• HC traps into local op-

tima

• Network optimization

against malicious attacks

required

• Network is generated by

using BA mode

• Global and local edge

swap performed for explo-

ration and exploitation

•Tis used to avoid local

optima

•αis used to get fast con-

vergence

• Both synthetic and real-world

networks considered

• Global edge swap and local

edge swap are compared

• Global edge swap has better

robustness due to exploration

• Network optimized by using

αis more robust

• No threshold value for edge

swap is set [66]

• No real-world network con-

sidered

Continued on next page

8

Table 2 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

• SFNs are not robust

against malicious attack

• No proper solution is

available to mitigate cas-

cading failure

•Rcand Rare weakly

correlated

• Multi-objective opti-

mization is used

• High and low objectives

value based networks

generated by SA

• Optimal topology is

found by the MAGA by

exploration

• Both synthetic and real-world

networks are considered

• Normalized robustness is bet-

ter as compared to SA

• For different sized networks

both objective have improved

values

• Cascading failure proved to

be more damaging as com-

pared with intentional attack

• A single measure needs to be

used for these objectives

• Cascading failure does not

happens rapidly countermea-

sures reduce its effect

• A better network structure re-

quired that is robust against at-

tacks

• Robustness of interde-

pendent network needs to

enhance

• Connectivity and depen-

dence links are added to

enhance robustness

• Considering cost con-

strain optimal values of

these links need to be cal-

culated

• CPS is more vulnerable

and caused cascading fail-

ure in physical region

• One-to-many conﬁgura-

tion is used to connect

these networks

• Stubborn and smart at-

tacker are considered

• Defender add links by

calculating intra and inter

degree

• Both synthetic and real-world

networks are considered

• Intra and inter degree based

attacks are performed on net-

works

• Based on these attacks links

are added

• Different intra and inter de-

gree based networks are con-

sidered

• Links are added randomly

without considering degree dis-

tribution [132]

• Dependence links are not fol-

lowing power-law distribution

• Stubborn attack on depen-

dence links caused network to

fail

• GA has premature con-

vergence problem

• Population diversity

needs to be high

• ROCKS uses multi-

populations co-evolution

to deal premature conver-

gence

•P

mand P

care different

for populations

• Populations coordinate

using migration operator

• Migration population

contains best individuals

from all populations

• Degree distribution

remains same after opti-

mization

• Onion-like structure is ob-

tained after optimization

• After ROCKS network is

more robust against random

and malicious attacks

• For different size networks

ROCKS outperform HC and

SA

• Due to multi-population com-

putation cost is high [70]

• After attack self-optimization

is difﬁcult

• Difﬁcult to implement on

real-life network due to com-

plexity

• After edge or node

removal 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 failure

• Node importance is

calculated based on its

degree and betweenness

• Removal of important

node causes other nodes

and edges to become

overload

• Load is distributed

among node by consider-

ing their initial capacity

• 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 SCcompared to DN and

BN

• Load is distributed among

neighboring nodes which have

already high load

• Simultaneous edge and node

removal is not discussed

• Overload edges and nodes

should be removed to protect

remaining network

• Random edge swap is

performed in optimization

• Robustness improve by

compromising community

structure

• Optimizing performed in

a way that preserve com-

munity structure

• Three step strategy is

proposed to preserve com-

munity

• Onion-like structure is

introduced in every com-

munity

• High degree nodes are

connected with nodes of

their own community

• Considering cost links

can be added to better pre-

serve community

• Community structure is com-

pared before and after opti-

mization

• These are preserved with en-

hanced robustness

• After the node removal 3-

steps strategy performed better

compared with single step

• High degree node of different

communities are not connected

with each other

• After removal of high degree

node its load can not be dis-

tributed by remaining nodes

• High degree nodes must con-

nect to share their load

Continued on next page

9

Table 2 Continued from previous page

Problems identiﬁed Proposed solutions Validations Limitations

• Removal of important

node causes decrease in

robustness

• These nodes must be pro-

tected by taking counter-

measures

• No method is available to

protect these nodes

• Networks vulnerability

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 elimination

on topological parameter

• Throughput, network delay,

and ﬂow is calculate

• End-to-end delay is maxi-

mized by node protection

• After removal of a node there

is high probability of backup

node removal

• Network information need to

be updated against smart at-

tacker

• Adversarial attack

change network informa-

tion

• Network robustness

decrease by these attacks

• Adversarial attacks are

considered in this network

• Two new attacks strate-

gies DILR and DALR in-

toduced

• RLR is less effective 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 coefﬁcient and diag-

onal distance is compared with

attacks

• Degree distribution is change

by edge swaps [106]

• Addition of links have cost

Cascading failures in inter-

dependent networks

• Novel method to capture

cascading failure intro-

duced

• CPS and PS considered

as interdependent net-

works

• One-to-One correspon-

dence between networks

• Fraction of survival

nodes is calculated after

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 ac-

cording to asynchronous fail-

ure propagation model

• Intra and inter dependencies

considered

• At even stage nodes removed

from CPS and at odd stage

nodes are removes from PS

• For load and space of PS

different distributions are fol-

lowed

• Interdependent network is

more vulnerable to node re-

moval as compared to single

network

• How to reduced cascading

failure is not discussed [100]

• DER impacts to reduce load

needs to be discussed

Importance of SFN robust-

ness

• Memetic algorithm to op-

timize network is used

• To enhance robustness

global and local searches

used

• Population is generated

by swapping edges of ini-

tial network

• Crossover is performed

by changing links of par-

ents

• Offsprings has same de-

gree 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−

RSFMA outperformed

• Proposed crossover improve

network robustness

•MA −RSFMA performed bet-

ter against random and mali-

cious attack

• Onion-like structure is pro-

duced

• Population has less diversity

[72]

• Crossover changes degree of

nodes

3 Problem statement

This section presents the problem statement of the synopsis. Moreover, the main problem is

divided into three subproblems which are presented below.

10

3.1 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 equation 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 structure 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 robust-

ness is not solved against malicious attacks and these attacks make the network vulnerable.

4 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

∑jdj

,(1)

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

The network construction is depicted in Fig. 1. There are three nodes i,jand kthat want to

k

k1

k2

k3

k6

k4

k5

i

i1

i2

j1

j3

j4

j

j2

j5

Figure 1. Scale-free network’s construction

11

become part of the network. First of all, start 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. 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 i2

directly. 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.

5 Network model description

The complete details of the proposed Improved Scale-Free Networks (ISFNs) are given in this

section. The description of the operations as part of the ISFNs is presented in detail. However,

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

5.1 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 ei j 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. 2a, the original topology with edges ei j 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. 2b and Fig. 2c, respectively.

ij

kl

ij

kl

ij

kl

ab c

Figure 2. Edge swap mechanism a: First connection method b: Second connection

method c: Third connection method

12

5.2 Edges’ degree based edge swap

For the speciﬁc edge ei j, the edge degree di j is calculated from the degrees of its respective

nodes. The edge degree is derived from the nodes degree [74] and is deﬁned as,

di j =pdi×dj,(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. 2. After that, based on the Eqs. 3, 4 and 5, the degree difference is calculated against each

edge swap. The degree difference of all the edge swaps is 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)

DIF1=|dik −djl|,(4)

DIF2=|dil −djk|.(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 synopsis, the edges are selected based on the

degrees of their respective nodes. The edge degree based swap helps to connect the similar de-

gree nodes. Moreover, the edge degree difference operation helps to achieve high robustness by

increasing the assortativity. Using the edges’ degree difference operations, the degree distribu-

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

network.

5.3 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 ei j 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 shows the nodes distance based edge swap. D1

and D2represent the nodes’ Euclidean distances for the edge ei j and ekl , respectively. In Fig.

3a, the original topology is given and the robustness is calculated against the malicious attacks.

Moreover, in Fig. 3b, the ﬁrst edge swap of the independent edges eik ,ejl is performed and D1

and D2are calculated. The same approach is followed in the second edge swap as shown in Fig.

3c. 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.

5.4 Measuring network robustness

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

ing 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

13

D1

D2D1D2D1D2

ij

kl

ij

kl

ij

kl

ab c

Figure 3. Nodes distance based edge swap a: First connection method b: Second

connection method c: Third connection method

equation. According to the Schneider observation, the robustness Rof a network having N

nodes can be represented using Eq. 6.

R=1

N+1

N−1

∑

n=0

MCSn

N,(6)

where, Nis the number of nodes and 1

N+1is 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 ap-

proximately zero, which means that a single high degree node present in the network is affected

by the malicious attacks.

5.5 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 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.

6 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.

6.1 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,

14

the network ﬁeld is divided into two equal parts 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. The dotted line represents the partition of the network, and Ncu and Ncl are the center nodes

NA

NB

NM

CL

ML

C1

C2

C3

C4

Ncu

Ncl

Figure 4. Network evolution by adding edges

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.

6.2 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=ei j|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. 5, the original topology with the possible ﬁrst and second edge swaps are given. As

seen in Fig. 5(a), the initial topology’s nodes i,j,kand lhave independent edges ei j and ekl . In

Fig. 5(b) and Fig. 5(c), the ﬁrst and second possible edge swaps are represented, respectively.

Against all the possible edges, 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

6.2.1 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 ei j and ekl should

15

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 5. Edge swap mechanism

6.2.2 Edge swap of degree based selected nodes

The degree of the nodes is being used to perform a degree-based edge swap. Initially, 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. 5. This edge swap connects similar degree nodes.

Against all possible edge swaps, the robustness is calculated and the edge swap that enhances

the network 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.

6.2.3 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.

7 Network Topology Evolution Scheme overview

In this section, the NTES is proposed to enhance the robustness of SFT. NTES provides so-

lutions 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 net-

work topology is constructed. The NTES consists of the following operations: network topol-

ogy evolution, networks connection by one-to-many correspondence, SFT attacks, core based

attacks and a comparison of heuristic algorithms to optimize the NTES’s robustness is made.

7.1 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 dis-

tributed in it. During the evolution of both parts, the power-law is followed. The connection of

16

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 fol-

lowing the power-law distribution are shown in Fig. 4. 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.

7.2 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.

In k-core decomposition, the cores are created by removing the nodes from the network.

In Fig. 4, 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.

7.3 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. 6, 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. 7. 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 NMCS 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 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.

7.4 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

17

NA

NB

NM

NR

CL

ML

Figure 6. Nodes’ attack based on core

NA

NB

NM

NR

NMCS

CL

ML

S1

S2

S3

Figure 7. Attacks on NMCS of the network

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, when the

solution traps into the local optima the exploration by random edge swap is performed.

Table 3 presents the complete details of the limitations that are identiﬁed through the litera-

ture review. Then their proposed solutions and how they are validated is given.

8 Details of the proposed model

In this section, the details of solutions that are used for the enhancement of network robustness

are given. Initially, the overview of JAYA algorithm is discussed. Then how the JAYA algorithm

is used to enhance the robustness of SFNs is studied. Afterwards, the metric of robustness and

the process of optimizing the SFNs are given. Then the complete details of edge swaps and

their effects on the topological parameters are analyzed. Moreover, addition of nodes in MCS,

network connectivity protection by bridge edges and the effects of different attack strengths are

studied.

18

Table 3 Mapping the identiﬁed limitations, their proposed solutions with validations

Identiﬁed limitations Solutions proposed Validations done

L1: The effects of mali-

cious attacks are severe on

large-sized networks [57,

66]

S1: NTES is proposed

in which small-sized net-

works evolve

V1: The small-sized net-

works will be evolved to

validate that they are ro-

bust to random and mali-

cious attacks.

L2: The links degree that

connect the networks do

not follow the power-law

[132]

S2: Using the concept of

the interdependent links,

the networks are con-

nected

V2: The power-law de-

gree distribution will be

validated for the mutual

nodes.

L3: The change of node’s

degree in each ring con-

sidering onion-like struc-

ture is not known [57]

S3: The same degree

nodes are found using k-

core decomposition

V3: The nodes will be re-

moved based on their de-

grees, and degree based

cores will be created.

L4: Random edge swap

increases the number of

redundant operations [66]

S4: Long links are cre-

ated through distance

based edge swap

V4: Against the long

links network robustness

is calculated.

8.1 Overview of JAYA

JAYA algorithm proposed by R. Rao [133] is a Sanskrit word that means victory. The name

is given to the algorithm because in each iteration, it improves the best individual along with

the worst one more efﬁciently than the other optimization algorithms. It is a parameterless

algorithm, therefore, no algorithm-speciﬁc control parameters are required that make it easy to

implement. Moreover, it is a population based algorithm and the individuals are selected based

on the ﬁtness values and are updated as in Equ. 7.

A(i+1,j,k) = A(i,j,k)+r1(i,j,1)(A(i,j,best)−|A(i,j,k)|)−r2(i,j,2)(A(i,j,worst)−|A(i,j,k)|),

(7)

where, A(i,j,k)is the candidate solution, i,jand krepresent that in an ith iteration the value

of jth variable of the kth individual, respectively. The A(i,j,best)and A(i,j,worst)are the best

and worst individuals in the search space, respectively. The random numbers r1and r2are used

to generate the diversity in the population and avoid the solution to stuck into the local optima.

The value of these numbers lie between 0 and 1. In JAYA, at each iteration, each individual in

the population is updated. After that, the individuals are compared with their previous values

and according to the required optimization, they are updated.

8.2 JAYA for the scale-free networks

The SFNs is optimized by JAYA algorithm. It is a population based algorithm, therefore, for

each topology, an adjacency matrix (A) is constructed and converted to a binary coded chro-

mosome as shown in Fig. 8. After each iterations, individual is updated based on the best and

the worst characteristics of the individuals present in the population. It is achieved by making

the exclusive edges of one individual into the other individuals. Moreover, at each iteration the

results are compared and based on the highest robustness of the individual, the population is

updated.

In Fig. 8, the topology of four nodes i,j,kand lforming a complete network is shown. The

adjacency matrix of the topology consists of binary numbers i.e., ai j =1, if node iis connected

with node jand vice versa. In the same way, if ai j =0 then no link is present between the

nodes iand j. Moreover, the usage of whole adjacency matrix in the formation of chromosome,

19

i

j

l

kAdjacency matrix

Topology

Chromosome

i j k l

i

j

k

l

0 1 1 1

1 0 0 1

1 0 0 1

1 1 1 0

0 1 1 1 0 0 1 0 1 0

Figure 8. Chromosome is obtained from the adjacency matrix

required extra storage space. The adjacency matrix is symmetric that means its upper and lower

triangles represent the same connections of nodes. Therefore, only the upper triangle is consid-

ered to form the chromosome because it has the complete network information and requires less

memory.

In the process of optimization, the degree distribution of the network remains same. There-

fore, the network is optimized by keeping the initial degree of the network same. The Equ. 8 is

obtained by modifying the Equ. 7, to get the best results.

Ai+1=Ai+r(Abest − |Ai|)−r(Aworst − |Ai|),(8)

where, Aiis the adjacency matrix of the ith topology, whereas Abest and Aworst are the best and

worst topologies, respectively, present in the population based on ﬁtness value. ris the number

of exclusive edges required to update the it h topology and ris selected randomly. When the

operations are performed on the current topology, the updated form is presented as Ai+1.

In Fig. 9, the complete process of implementing the JAYA algorithm is shown. The adja-

1

23

4

5

6

1

23

4

5

6

1

23

4

5

6

1

23

4

5

6

1

23

4

5

6

(a) (b)

(c) (d) (e)

Figure 9. 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

20

cency matrix of current topology Aiis presented in Fig. 9(a) while the best topology Abest in the

population is given in Fig. 9(b). The difference between the topologies is calculated by consid-

ering the exclusive edges. As the exclusive edges are deﬁned as the edges which are presented

only in one topology. Therefore, in Fig. 9(b), the edge between nodes 4 and 5 is not present

in Fig. 9(a), therefore, it is an exclusive edge. To enhance the robustness, the exclusive edges

present in the Abest needs to be made in Ai. In Fig. 9(c), the neighbor nodes are found to make

an edge of the Abest into the Ai. Here, the dotted circle represents the neighbors of node 4. The

green dotted lines show the distance of node 4 with two of its neighboring nodes. The nodes

that are in the same communication range and have independent edges as shown in Fig. 9(d)

are selected to make the exclusive edge in the Ai. Nodes 3, 4, 5 and 6 are in the same commu-

nication range and have independent edges, so in the edge swap, these nodes are considered.

The exclusive edge that is part of the Abest is made in the Aiand termed as Xexclusive1. The same

method is repeated for the worst and current individuals and represented as Xexcl usive2. After

making these exclusive edges the Equ. 8 become,

Ai+1=Ai+Xexclusive1−Xexclusive2(9)

The process of exclusive edge is repeated for the Xexclusive1and Xexclusive2and the Equ. 9 is

updated to the following form.

Ai+1=Ai+XexclusiveT(10)

here, XexclusiveTis the adjacency metric obtained by exclusive edges of the best and worst topolo-

gies with the current topology. At that point, the two matrixes are adding so, the exclusive edges

are made to obtain the Ai+1. The process is repeated for all the individuals of the population. Af-

ter the completion of one iteration, the ﬁtness value of Ai+1is compared with the Ai. If the Ai+1

has more ﬁtness than the Ai, the population is updated with the individual having the highest

robustness.

8.3 Effects of edge swap on topological parameters

Against the edge swap methods discussed above, the topological parameters are studied and the

results are evaluated to conﬁrm the usefulness of the speciﬁc type of edge swap. The following

topological parameters are studied to prove the efﬁciency of these edge swaps.

• Global communication efﬁciency

• Average clustering coefﬁcient

• Average shortest path length

• Assortative coefﬁcient

8.3.1 Global communication efﬁciency

The Global Communication Efﬁciency (GCE) is a network measure that describes the efﬁcient

exchange of information in the network [135]. It is deﬁned as follows.

C(G) = 1

N(N−1)∑

i6=j

1

di j

,(11)

where, 1

N(N−1)is the normalization factor and di j is the shortest path between node iand j.

Higher the value of GCE makes the network more efﬁcient.

21

8.3.2 Average clustering coefﬁcient

In a network, the clustering coefﬁcient Ckdeﬁnes the node’s characteristics to form a cluster

[136]. It is deﬁned as follows.

Ck=2Ek

k(k−1)Nk

,(12)

where, Ekand Nkare the edges between the nodes and the total number of nodes, respectively,

that have degree k. The value of Ck lies in the range of [0, 1]. Where, 0 means a node has no

connection with its neighbors, when the nodes has connections with its neighboring nodes has

value 1. The overall clustering coefﬁcient ¯

Cof a network is calculated as,

¯

C=1

N

kmax

∑

k=1

Ck,(13)

where, kmax is the maximum value of the node degree.

8.3.3 Average shortest path length

The shortest path is deﬁned as the minimum number of edges between two nodes [74]. More-

over, the average shortest path length is the length between all pairs of nodes. To calculate the

average shortest path length D(G), the following equation is used.

D(G) = 1

N−1∑

i6=j

di j.(14)

8.3.4 Assortative coefﬁcient

Assortativity is deﬁned as the links between nodes based on properties like degree, betweenness

centrality, etc. Newman [137] ﬁrst gives its concept based on the Pearson correlation coefﬁcient

to deﬁne the characteristics of nodes that are linked with each other. It has a value in the range

of [-1, 1]. Where, 1 means that the network is highly assortative i.e., similar degree nodes are

connected. Whereas, -1 proves that the network is disassortative and 0 means nodes have no

relation i.e., they have no connection. The assortative coefﬁcient γis calculated as,

γ=M−1∑N

i=2∑i

j=1ai jkikj−ζ2

M−1∑N

i=2∑i

j=1

1

2ai j(k2

i+k2

j)−ζ2

(15)

ζ=M−1

N

∑

i=2

i

∑

j=1

1

2ai j(ki+kj)(16)

where, Nis the total number of nodes, kiand kjare the degree of node iand j, respectively. M

is the total number of edges.

8.4 Addition of nodes in maximum connected subgraphs

In this section, nodes are added in the MCS to achieve high robustness. The malicious attacks

fragment the network into multiple subgraphs. The number of nodes in each subgraph is not the

same, some have a high number of nodes and some have less. To get a high number of nodes

in the MCS, the nodes present in the small-sized subgraphs broadcast a request message to the

nodes present in its neighboring large-sized subgraphs. If the degree threshold of the nodes

is not achieved the nodes present in the larger subgraphs receive the request message and reply

with a conﬁrmation. After the links are formed between the nodes of small-sized and large-sized

subgraphs, the robustness is calculated. Due to an increase in the number of nodes in the MCS,

the robustness of the network is increased. The communication between the nodes is performed

through a wireless medium, therefore, no extra cost is added in the form of adding the edges.

22

8.5 Effects of bridge edge on the network

In the networks, some edges are important for the network connectivity because the removal

of these edges effects the network structure. These edges are termed as the bridge edges and

they are protected to maintain the network connectivity. These speciﬁc edges are initially found

in the network by continuously removing the edges from the network. An edge is marked as

a bridge edge if its removal fragments the network into multiple subgraphs. The nodes in the

neighbors of the bridge edge are selected and the edge swap is performed against the nodes that

form the bridge edge. The procedure to enhance the network robustness with the protection

of bridge edge is shown in Fig. 10. The edge eki is the bridge edge and is removed from the

ij

l

kij

l

k

(a) (b)

Figure 10. Effects of bridge edge on the robustness of SFNs

network. After the removal, the neighbors of node kand iare selected. The edges ekl and ei j are

independent edges, therefore, the edge swap is performed. The edge swap increases the network

robustness by increasing the number of edges in the MCS.

8.6 Effects of different attacks strength on the network

In this section, to evaluate the network robustness, the nodes are removed based on the attacks

strength. In the particular attacks strength, speciﬁc nodes are removed in an instance. To evalu-

ate the network robustness, the attacks of the following strengths are implemented.

• Attack strength 1

• Attack strength 2

• Attack strength 3

In the attack strength 1, 5% nodes are removed from the network. These nodes are selected

randomly and after each removal of nodes, the robustness is calculated. After multiple attacks,

the network is analyzed. Moreover, 10% and 15% nodes are removed in the attack strength of 2

and 3, respectively. The higher the attack strength paralyzes the network more efﬁciently.

Table 4 presents the details of the limitations that are identiﬁed through the literature review.

Then their proposed solutions and how they are validated is given.

23

Table 4 Mapping table

Identiﬁed limitations Proposed solutions Validations

L1: Heuristic algorithms

are used to optimize SFNs

L1.1: Require number of

algorithm speciﬁc control

parameter [65, 69, 70]

L1.2: They have high

computational time

S1: JAYA is implemented

that is a parameter less al-

gorithm

V1: The results will be

compared with existing

schemes to prove JAYA

effectiveness.

L2: The changes in

the topological parame-

ters against the edge swap

are not known [62,66]

S2: Edges are classiﬁed

and analyzed for the topo-

logical parameters

V2: Different topological

parameters are validated

against the edge swaps

L3: The removal of nodes

makes the network vul-

nerable [65, 69, 70]

S3: After attacks, nodes

can make connections

with the neighboring

nodes

V3: Robustness is calcu-

lated after adding nodes in

MCS

L4: To maintain the

network connectivity no

bridge edge is considered

[74]

S4: The edges are

swapped to protect bridge

edge

V4: Edges swap will

be performed, after the

bridge edge removal, to

reduce the effects

L5: Attack on a single

node is considered [62,66,

70]

S5: Network robustness

is calculated against dif-

ferent attacks strength

V5: The results will be

validated against different

attacks strength

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33

Tentative Time Table

Serial

No.

Activity Date

1 Background study and detailed literature review —

2 Formulation of problem and proposing solution —

3 Analysis and dissemination of results —

4 Thesis Writing —

34

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 Complex, 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 Com-

puting (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 Communica-

tions and Mobile Computing (IWCMC) (pp. 2240-2244). IEEE. Download

35

PART II

Recommendation by the Research Supervisor

Name: Dr. Nadeem Javaid Signature:_____________________ Date: March 19, 2021

Recommendation by the Research Co-Supervisor

Name: Dr. S. M. Gulfam Signature:_____________________ Date: March 19, 2021

Signed by Supervisory Committee

S.# Name of Committee member Designation Signature & Date

1 Dr. Nadeem Javaid Associate Professor

2 Dr. Mariam Akbar Assistent Professor

3 Dr. Saif ur Rehman Khan Lecturer

Approved by Departmental Advisory Committee

Certiﬁed that the synopsis has been seen by members of DAC and considered it suitable for

putting up to BASAR.

Secretary

Departmental Advisory Committee

Name: _____________________________

Signature: _____________________________

Date: _____________________________

Chairman/HoD: ____________________________

Signature: _____________________________

Date: _____________________________

36

PART III

Dean, Faculty of Information Sciences & Technology

_____________________Approved for placement before BASAR.

_____________________Not Approved on the basis of following reasons

Signature_____________________Date________

Secretary BASAR

_____________________Approved for placement before BASAR.

_____________________Not Approved on the basis of following reasons

Signature_____________________Date________

Dean, Faculty of Information Sciences & Technology

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

Signature_____________________Date________

37