Computationally efficient topology optimization of scale-free IoT networks
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Computer Communications, 2022, 185, pp. 1-12
- Issue Date:
- 2022-03-01
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|---|---|---|---|---|---|
| 1-s2.0-S0140366421004795-main.pdf | 1.28 MB |
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The malicious attacks in the scale-free Internet of Things (IoT) networks create a serious threat for the functionality of nodes. During the malicious attacks, the removal of high degree nodes greatly affects the connectivity of the remaining nodes in the networks. Therefore, ensuring the maximum connectivity among the nodes is an important part of the topology optimization. A good scale-free network has the ability to maintain the functionality of the nodes even if some of them are removed from the network. Thus, designing a robust network to support the nodes’ functionality is the aim of topology optimization in the scale-free networks. Moreover, the computational complexity of an optimization process increases the cost of the network. Therefore, in this paper, the main objective is to reduce the computational cost of the network with the aim of constructing a robust network topology. Thus, four solutions are presented to reduce the computational cost of the network. First, a Smart Edge Swap Mechanism (SESM) is proposed to overcome the excessive randomness of the standard Random Edge Swap Mechanism (RESM). Second, a threshold based node removal method is introduced to reduce the operation of the edge swap mechanism when an objective function converges at a point. Third, multiple attacks are performed in the network to find the correlation between the measures, which are degree, betweenness and closeness centralities. Fourth, based on the third solution, a Heat Map Centrality (HMC) is used that finds the set of most important nodes from the network. The HMC damages the network by utilizing the information of two positively correlated measures. It helps to provide a good attack strategy for robust optimization. The simulation results demonstrate the efficacy of the proposed SESM mechanism. It outperforms the existing RESM mechanism by almost 4% better network robustness and 10% less number of swaps. Moreover, 64% removal of nodes helps to reduce the computational cost of the network.
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