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An Efficient Approach to Enhance the Robustness of Scale-Free Networks

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In this paper, we aim to increase the robustness of Scale-Free Networks (SFNs) that are robust against random attacks; however, they are fragile to malicious attacks. When the highest-degree nodes are removed from the SFNs, they greatly affect the connectivity of the remaining nodes. In the proposed method, we perform two attacks, namely, the high degree link attacks and the recalculated high degree link attacks. These link attacks affect the core part of the network. To measure the extent of damaging the network at a greater portion, we use the closeness centrality and the eigenvector centrality. The goal is to construct a topology that shows better robustness against different malicious attacks with a minimum computational cost. The Pearson's correlation coefficient is used to select the measures that are positively correlated with each other. Based on these centrality measures, we optimize the network robustness for both node and link attacks simultaneously. The optimization is performed based on two efficient measures, which are computationally less expensive to increase the network robustness.
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An Efficient Approach to Enhance the Robustness
of Scale-Free Networks
Syed Minhal Abbas1, Nadeem Javaid1,, Muhammad Usman2, Shakira Musa Baig1,
Arsalan Malik1, Anees Ur Rehman1
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan
Email: minhal.abbas514@gmail.com, usmaneng9@gmail.com,
Shakira.musabaig1998@gmail.com, arsalanmalik8936@gmail.com, anees.nizami@hotmail.com,
Corresponding Author: nadeemjavaidqau@gmail.com; www.njavaid.com
Abstract—In this paper, we aim to increase the robustness
of Scale-Free Networks (SFNs) that are robust against random
attacks; however, they are fragile to malicious attacks. When the
highest-degree nodes are removed from the SFNs, they greatly
affect the connectivity of the remaining nodes. In the proposed
method, we perform two attacks, namely, the high degree link
attacks and the recalculated high degree link attacks. These link
attacks affect the core part of the network. To measure the
extent of damaging the network at a greater portion, we use
the closeness centrality and the eigenvector centrality. The goal
is to construct a topology that shows better robustness against
different malicious attacks with a minimum computational cost.
The Pearson’s correlation coefficient is used to select the measures
that are positively correlated with each other. Based on these
centrality measures, we optimize the network robustness for
both node and link attacks simultaneously. The optimization is
performed based on two efficient measures, which are computa-
tionally less expensive to increase the network robustness.
Index Terms—Scale-free networks, Malicious attack, Robust-
ness, Network topology, Centrality measures, Optimization.
I. INTRODUCTION
The Internet of Things (IoT) has made remarkable progress
in the last decade. With the increasing number of smart
devices, the digital world is growing rapidly. The IoT allows
manual processes to be automated into the digital world. The
IoT generates the necessary data and includes it in real-time
applications. With the evolution of IoT, its applications are
spread in many fields, including health system [1], transporta-
tion system [2], [3], medical monitoring system [4], etc.
In complex network theory, there are mainly two types of
models: the Small World Networks (SWNs) and the Scale-Free
Networks (SFNs) [5], [6]. The SWNs are constructed by using
heterogeneous nodes. These nodes have different communi-
cation range, energy, and bandwidth. Moreover, the SWNs
contain a small average path length and a high clustering coef-
ficient [7]. Whereas, in SFNs, the degree of nodes is distributed
according to the power law. With this distribution, the high
degree nodes are less in number as compared to low degree
nodes that make connections with similar types of nodes [8].
On the other hand, the SFNs are constructed by homogeneous
network topologies [9]. The homogeneous nodes have the
same bandwidth and communication range. Therefore, the
network containing the SFNs’ property is usually used in IoT
due to the same bandwidth and transmission range. However,
it is challenging to generate a network topology that is robust
against the malicious attacks [9], [10].
Schneider et al. [11] is introduced a metric to calculate the
robustness R of network against the malicious attacks. The
robustness of the network is calculated as,
R= 1/N
N
X
Q=1
s(Q).(1)
In the above equation, s(Q)represents the fraction of
removed nodes, Nrepresents the total number of nodes. The
Rvalue is calculated by analyzing the connectivity of nodes
in a network, which is followed by removing nodes until the
network is damaged. Therefore, we need to make a network
topology that show robustness against the malicious attacks.
Also, the probability of malicious attacks is based on the
intelligent selection of nodes that is growing increasingly [12],
[13].
The authors in [14] consider the degree and Betweenness
Centrality (BC) and perform attack on both nodes and links
to evaluate the nodes’ importance. Therefore, when high
degree nodes are attacked, the network breaks down into
sub-networks. In a network, random and malicious attacks
may occur simultaneously. Among them, the malicious attacks
damage the network to a greater extent because they occur on
the most influential nodes in the network [9], [15].
Several strategies are used to make the network robust
against malicious attacks. The SFNs have a high ability to
resist random attacks. However, they are weak against mali-
cious attacks [16]. Therefore, an efficient topology is required
to design the network robust against malicious attacks [17].
Several optimization techniques including memetic algorithm
[18], multi-population genetic algorithm [19], enhanced differ-
ential evolution [20], natural connectivity model [21], greedy
model [22], and elephant herding optimization [23] are used
to enhance the network robustness. These techniques make the
network onion-like without changing the degree of nodes. The
structure is robust against both random and malicious attacks.
Our contributions in this paper are as follows.
Two efficient measures, namely, Closeness Centrality
(CC) and Eigenvector Centrality (EC) are used to find
out the most influential nodes in the network with less
computational cost.
The Pearson’s correlation coefficient is used to determine
the relationship between robustness measures based on
CC and EC and robust the network for both node and
link attacks.
Two attacks are performed, High Degree Link Attack
(HDLA) and Recalculated HDLA (RHDLA). These at-
tacks help the attacker to affect the network as the greater
portion of the network is damaged in a less time.
The rest of the paper is structured as follows. Section II
contains the related work. The proposed model and description
are discussed in Section III. Section IV explains the simulation
results of our proposed schemes and Section V concludes the
paper.
II. RE LATE D WOR K
Previous study uses single-objective optimization with
crossover and mutation. The network that is robust against
node attacks is not necessarily robust against the link attacks
[10]. In the real-world problem, multiple malicious attacks are
not simultaneously considered to optimize the robustness of
a network [14]. Traditional Genetic Algorithm (GA) is used
to make the network robust, premature convergence occurs
due to loss of diversity in the population [19]. Simulated
Annealing (SA) attempts to find the best solution with the
worst probability and finds a better solution, leading to global
optima. The algorithm requires unnecessary comparisons to
optimize the network robustness caused by computational
overhead [18].
With the increase of IoT in smart cities, the network is
prone to cyber-attacks that include malicious and random
attacks. The network is required to be robust against cyber-
attacks. However, many redundant operations cause computa-
tional complexity [20]. In [24], authors use the large SFNs
exploring the hidden features in solution space to enhance the
robustness of the network; however, the computational cost
occurs. The network structure is robust on specific types of
attacks; however, the structured properties are ignored in a
large complex network [21], [25].
Machine Learning (ML) techniques are used to optimize
the network topology against cyber-attacks. SFNs are efficient
regarding robustness against the random attacks; however,
vulnerable to the malicious attacks [26], [27]. The robust
networks are the essential support for the IoT applications such
as smart cities, intelligent vehicles, smart devices [28], etc.
However, resource-constrained IoT devices and various types
of attacks are challenging against the network’s robustness
and communication capability [29]. ML-based algorithms are
designed to optimize the network robustness against malicious
attacks. Modification of data regarding the swapping edges has
influenced the structure of the network. Constructing a network
robust to withstand malicious attacks is still a challenging issue
in the optimization problem [30], [31].
The robust network against node attacks is not necessarily
strong against the links attack simultaneously [14]. In smart
cities, nodes collect data from different domains [32]. There
is an issue to deal with big data with the network’s robustness
against the malicious attack [19]. GA is used to enhance a
scale-free network’s robustness against malicious attacks as
well as random attacks [19]. Multiple methods are used to
overcome malicious attacks and cascading failures separately
[15], [33], [34]. The evolution algorithm is used the two
operators to optimize the network’s tolerance using fixed
probability rates [19]. However, there is an issue in tackling
the large SFN exploring the hidden features in solution space.
Moreover, the improved network structure is based on specific
types of attacks.
SFNs are tolerant of random attacks; however, prone to
malicious attacks [16]. Fault-tolerant based network topology
evolution scheme is proposed. A new node joins the network
based on preferential attachment and fault probability. Qiu et
al. [12] propose the scheme that makes the network into the
onion-like structure by performing the degree difference and
angle sum operations. The same degree nodes connect with
each other; however, redundant operations are performed due
to edge swapping. The node’s failure with fault and cyber-
attacks affects the communications among the networks [33].
Furthermore, some of the nodes in the network are more
interconnected internally as compared with the rest of the
network, thus forming the community.
It is an important feature of the network structure. There-
fore, the network is optimized in each community by con-
sidering the onion-like structure [36]. Therefore, robustness is
enhanced along with the preservation of the network commu-
nity.
III. SCA LE -FRE E NET WORKS TOPOLOGY AND
ROBUSTNESS OPTIMIZATION
In this section, the proposed system model is designed, as
shown in Fig. 1. In the figure, three limitations to be addressed
are mapped to the proposed solutions.
A. Construction of Initial Topology
We construct the network using the Barab´
asi Albert (BA)
model [8]. The model generates the SFNs topology by fol-
lowing the power-law distribution. Initially, a network is con-
structed from a small clique. A new node enters in the network
based on the preferential attachment. The node having a high
degree has more probability to be selected by calculating the
degree of the neighboring node through the roulette wheel
mechanism. The communication range and energy resources
of nodes are limited because the high degree nodes consume
more energy, this impacts the overall performance of the
network. Thus, the maximum degree of nodes is limited [37].
B. Centrality Measures
In the proposed system model, we introduce two measures
CC and EC, to find the most influential nodes in the network.
These measures have less computational cost as compared to
BC. Moreover, CC does not have to determine how many
shortest paths pass through specific nodes that are required
in BC.
b) Topology after
Attack
b) Topology after
Attack
Onion-like Structure
Eigenvector Centrality
Closeness Centrality
i
j
k
l
i
j
kl
i
j
k
l
Smart Rewiring
Mechanism
a) Influential Node
Attack
L1=>S1
L3=>S3
L2=>S2
L1-Betweenness centrality.
L2-Network optimized against
node or link attacks.
L3-Attacker utilizes information
of nodes for attack.
S1-Closeness and eigenvector
centrality.
S2-Optimized network against
both attacks.
S3-HDLA and RHDLA highly
affect the network.
a) High Degree
Link Attack
Fig. 1. Proposed System Model
The CC measure provides the network’s global information.
It finds nodes’ centrality based on the relative distance between
each pair of nodes. The centrality of a node finds the broad-
caster i.e., the communication of one node to all other nodes
is efficient In this process, the shortest distance is calculated
between a specific node to other nodes in the network.
The CC cof a node xis calculated using the equation
defined as:
cx= 1/X
y
d(y, x).(2)
Where drepresents the shortest distance between two ver-
tices yand x[38].
While, EC measures the importance of nodes in the network.
It assigns a relative score of xto each node. For a given
graph, G= (v, e), the relative score xof EC score a vertex v
connected with other vertex with an edge ecan be defined as:
xv= 1X
tG
av,tx(t)(3)
Where arepresents the adjacency matrix, vand tare two
vertex, and λis a constant factor [39].
The EC finds the node influencing the entire network and
assigns the relative index value to all nodes based on the
neighbor’s connection. The node containing the high indexed
value contributes more to that node having a low index.
Using the above measures, we find the most influential
nodes in the network to perform the attacks.
C. Centrality Based Malicious Attacks
In the proposed model, three types of malicious attacks
based on degree, CC, and EC are performed. These attacks
affect the functionality of the remaining nodes in the network.
These attacks are performed on both nodes and links. However,
the attacker utilizes the information of high degree nodes
in the network and removes the links. Therefore, in the
proposed system model, we consider two links attacks, namely,
HDLA and RHDLA. In HDLA, the links between two high
degree nodes in the network are removed based on the initial
information. Whereas, in RHDLA the links degree between
two nodes is recalculated after every node’s removal.
D. Optimization of Network Robustness
Our focus is to find the robustness of the network with the
limited number of attacks using the provided information from
CC and EC measures [38], [39]. Based on the robustness mea-
sure for nodes and links, we find out the Pearson’s correlation
coefficient among them. The optimization is performed on two
positively correlated measures. The topology that increases the
robustness for both nodes and link attacks is considered an
optimized network topology with less computational cost as
shown in Fig.1. A smart rewiring mechanism is adopted in the
proposed model, that helps to convert the scale-free topology
into an onion-like structure.
In Table 1, L1 section contains a measure to calculate
a node’s centrality using betweenness to make the network
robust. The computational cost to find the influential node
based on the BC is high as compared to the node’s selection
by considering node degree removal [14], [16]. Two efficient
measures CC and EC find the importance of a node in less
time as mentioned in S1 section. Using these measures, we
will construct a network that is efficient against malicious
attacks. Also, it reduces the computational cost and optimizes
the network robustness. In V1 section, the execution time of
CC and EC is validated in Fig. 2. These measures have less
computation time as compared to BC.
The network is optimized on the basis of robustness against
node attack Rnand robustness against link attack Rlin L1
section. Using the proposed centrality measures, we design
a network topology that is fragmented with less number
of attacks. We use Pearson’s correlation coefficient between
Rnand Rl. The positively correlated measures are used to
enhance the network robustness. Various attacks are performed
to strengthen the network, which includes CC, EC, HDLA,
and RHDLA, as explained in S2 section. To enhance network
robustness, we use the smart rewiring mechanism to achieve
the minimum computational cost and faster convergence that
leads to the optimal solution.
TABLE I
MAP PIN G OF LIMITATIONS WITH PROP OS ED SOL UT ION S AN D THE IR VALI DATIO NS .
Limitations Identified Solutions Proposed Validations
L1: The BC measure increases the compu-
tational cost to find the influential node [14].
S1: CC and EC finds the influential node in
less computational cost.
V1: Validate the execution time with two
centrality measures in Fig. 2.
L2: Network is not robust against different
types of malicious attacks [27].
S2: Network is optimized simultaneously
against different types of node and link
attacks based on CC, EC, HDLA, and
RHDLA.
V2: Network performance is validated with
robustness values in Fig. 4.
L3: Attacker utilizes node’s information to
perform attacks [12], [17], [20], [24].
S3: Introduce links as well as node attacks. V3: Evaluate the network connectivity by
performing attacks in Fig. 3.
L3 section contains attacks on important nodes [12], [17],
[20], [24]. Two link attacks, HDLA and RHDLA, based on
the two high degrees nodes are performed on the network, as
explained in S3 section. These attacks will affect the whole
network due to the failure of a high degree edge to make
the network robust. The V3 section evaluates the network
connectivity against different malicious attacks, including BC,
CC, EC, HDLA, and RHDLA.
IV. SIMULATION RESULTS AN D DISCUSSION
SFNs are used to evaluate the efficiency of our model with
ROSE and SA algorithms. The simulations are conducted
in MATLAB. In a sensor field of 500*500 m2, nodes are
deployed randomly. The modeling process requires each node
to have a limited number of neighbors, and the communication
range r is set at 200m to indicate the communication radius.
In the simulations, the total number of nodes (N) is set to
100, the edge density (m) = 2 and the maximum value of the
node’s degree is 25. We evaluate the results for 100 iterations
in each algorithm. We observe that our model converges to
optimal results within 100 iterations in Fig. 4.
50 100 150 200
Number of Nodes
0
20
40
60
80
100
120
140
160
Execution Time (seconds)
BC
CC
EC
Fig. 2. Comparison of Execution Time between Centrality Measures.
A. Evaluation of Centrality Measures
The comparison of various centrality measures is shown in
Fig. 2, BC the computational overhead of the BC is high. As
compared to BC, CC has a strong ability to find the most
important node in less time due to the calculated shortest
distance between two nodes. With the increasing N from 50 to
200 nodes, the closeness measure outperforms the betweenness
measure because CC is not calculated in the shortest paths that
pass only through a specific node. Furthermore, EC finds the
0 20 40 60 80 100 120 140 160 180 200
Number of Link Attacks
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Probability of Removed Links
BC
CC
EC
HDLA
RHDLA
Fig. 3. Malicious Link Attacks Affect the Network Topology.
influential node for the attack in less time as compared to BC.
Because, it indicates not just the direct influence of a given
node, also the influence of nodes that are more than a hop
away.
B. Malicious Attacks affect the Network Topology
Validation of the proposed model is achieved through the
removal of links with malicious attacks. Results demonstrate
that the performance of the proposed attacks HDLA and
RHDLA affect the network more as compared to the BC and
CC, as shown in Fig. 3.
With the increasing number of attacks, the probability of
removed links using HDLA and RHDLA is greater than that
of BC and CC attacks. After the 120th attack, HDLA and
RHDLA damage, the network at a greater portion compared
with BC and CC attacks. The effectiveness of HDLA and
RHDLA is shown in Fig. 3. HDLA affects the most important
link between the two high degree nodes.
C. Comparison with Existing Algorithms
The R value of the network is evaluated using the different
number of iterations. Our model, which is smart rewiring
mechanism, is compared with ROSE and SA in Fig. 4.
10 50 100
Number of Iterations
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Robustness
Smart Rewiring
ROSE
SA
Fig. 4. Robustness value with Different number of Iterations.
With increasing the number of iterations, the proposed smart
rewiring mechanism outperforms both ROSE and SA in terms
of high robustness (i.e., Schneider R-value). Among the best
results, the robustness value for Smart Rewiring, ROSE, and
SA are 0.350 0.289, and 0.259 respectively. As compared to
ROSE and SA schemes, our model’s performance is higher
to make the network robust against malicious attacks. The
process of edge swap is restricted to the high-degree nodes.
In this way, the re-connection of these nodes will change the
overall performance of the network as compared to ROSE and
SA.
V. CONCLUSION
This paper improves the robustness of SFNs. Two efficient
centrality measures called CC and EC, are used to find out the
most influential nodes in the network. Using these measures,
the malicious attacks are performed. These attacks affect the
network quickly and make it robust. A topology’s robustness
is determined using the Schneider R metric. Two link attacks
are performed, the HDLA and the RHDLA. Both of these
attacks target the high degree links of the network. Simulation
results show that these attacks damage the network with less
computational cost. The network is designed to withstand
malicious attacks on nodes as well as links. Through smart
rewiring, the network topology is turned into an onion-like
structure, which is robust against the malicious attacks.
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... It affects the communication with the remaining network devices and damages the whole network [10]. The importance of protecting real-world networks from continuation of work presented in [26]. The major contributions of this study are enlisted as follows: ...
Article
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The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes’ degrees remains constant.
Research Proposal
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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.
Thesis
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During the past few decades, the Internet of Things (IoT) has made remarkable progress in many real-world applications including healthcare, military, transportation, etc. Multiple sensor nodes are deployed in these _elds to get the required 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' 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, 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 variable attacks are considered. Simulation results of the proposed scheme are compared 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 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. Furthermore, NTES is compared with Barabasi Albert (BA) model and Hill Climbing (HC) algorithm against random and malicious 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 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. In simulations, the comparison of JAYA is made with two existing algorithms: ROSE and Simulated Annealing (SA). The network optimized by JAYA has a better robustness against random and malicious attacks, as compared to the existing algorithms. Furthermore, among the edge swap categories, 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.
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In this paper, two algorithms enhanced differential evolution (EDE) and adaptive EDE (AEDE) are proposed. The proposed algorithms improve the robustness of the IoT network without changing the degree distribution of nodes. The EDE algorithm maintains the diversity in a solution space through the tri-vector mutation operation and explores the hidden areas. The crossover phase makes the algorithm's convergence fast towards the global optima. The AEDE dynamically changes the probabilities of multiple operations of the EDE with the changing environment. Also, it maintains the balance between the diversity of solution space and the convergence speed through adaptive probabilities. The EDE performs 7.13%, 31.6% and 41.8% better as compared to GA, SA and HA, respectively. The AEDE outperforms the GA, SA and HA with 11%, 35.3% and 45.4% better efficiency, respectively. The proposed algorithms outperform existing algorithms in terms of robustness and convergence speed.
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Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.
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The extensive applications in the Internet of Things (IoT) have inspired a growing network scale. However, due to the resources-limited IoT devices and the numerous cyber-attacks against applications, maintaining the robustness and communication capabilities for the applications is increasingly challenging. In this paper, we consider IoT network topologies that provide robust communication for heterogeneous networks and study the networking stability of IoT devices and the intelligent evolution computing in network architectures. We explicate the network robustness problem both for the network architecture and the resistance to cyber-attacks. For the network architecture, we optimize the robustness of IoT network topology with a scale-free network model which has good performance in random attacks. In the case with the resistance to cyber-attacks, a deep deterministic policy learning (DDLP) algorithm is proposed to improve the stability for large scale IoT applications. Simulations show that the proposed algorithms greatly advance the robustness of IoT network topology compared to other algorithms, with a less computational cost.
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In smart cities, the Internet of Things (IoT) consists of many low-power smart nodes. Its robustness is essential for protection of communication in data science against node failures caused by energy shortage or cyber-attacks. Scale-free networking topology, widely applied in IoT, is effectively resilient to random attacks but is vulnerable to malicious ones in which high-degree nodes are made to fail. The prohibitively high computational cost of existing robustness optimization algorithms is an obstacle to efficient topology self-optimization. To solve this problem, a novel robust networking model based on artificial intelligence is proposed to improve IoT topology robustness to protect its communication. Using the Back-Propagation neural network learning algorithm, the model extracts topology features from a dataset by supervised training. The experimental results show that the model achieves better prediction accuracy, thereby optimizing the topology with minimal computation overhead.
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Many real-world networked systems can be modeled as scale-free networks. Due to the robust-yet-fragile nature of scale-free networks, it is vulnerable to the failure of hub nodes, which triggers cascading failures and finally causes the entire network to collapse. In this paper, we study the recovery of scale-free networks when cascading failures occur. We recover the network by repairing failed nodes, and each failed node requires a certain amount of resources to be repaired. A measure named resilience loss is used to quantify the recovery performance. We find that under the constraint of a fixed amount of total recovery resources, there exists an optimal resource allocation strategy to achieve the best network recovery performance, which has the lowest resilience loss. The results may be helpful to understand how real-world scale-free networks recover from cascading failures.
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