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An Adaptive Enhanced Differential Evolution Strategies for Topology Robustness in Internet of Things

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An Adaptive Enhanced Differential Evolution Strategies for Topology Robustness in Internet of Things

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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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... 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 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. ...
... If the obtain edges are independent then the degree of each edge is calculated (Lines 7-10). Moreover, for all the possible edges the difference of edge degree is calculated (Lines [11][12][13][14] and the minimum degree difference is selected (Line 15). The edges are swapped according to the minimum difference in edge degree and after each swap, the robustness is calculated (Lines [16][17][18][19][20][21][22]. ...
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.
... 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 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. ...
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.
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... Yang et al. [45] proposed an intelligent trust cloud management method for secure and reliable communication in Internet of Medical Things (IoMT) systems. Qureshi et al. [46] proposed enhanced differential evolution (EDE) and adaptive EDE algorithms to effectively improve the topology robustness of the IoT network while keeping the node degree distribution unchanged. ...
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... Traditional systems use centralized servers as a communication medium for IoT devices. Due to the rapid increase in connected devices, The popularity of IoT is rising yearly and becoming the center of attraction among all industries [1]. It is estimated that in 2025, the number of IoT devices will increase from 8.5 to 20.4 billion [2]. ...
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... The network topologies [51,52] provide layouts of various communication activities occurring inside the networks. The resilience of the network topologies against the attacks depends upon the arrangement of the nodes present in the networks. ...
Thesis
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