Attack resistance-based topology robustness of scale-free internet of things for smart cities

Abstract

In internet of things (IoTs), the increase in the number of devices is directly proportional to the number of applications. The exponential growth of devices increases both the network complexity and risk against topology robustness. Moreover, the network is also prone to targeted and malicious attacks. In this paper, enhanced angle sum operation ROSE (EASO-ROSE), enhanced ROSE, adaptive genetic algorithm (AGA) and cluster adaptive genetic algorithm (CAGA) are proposed to cater the topology robustness issue for IoT enabled smart cities. In addition, the proposed solutions keep the nodes’ initial degree distribution unchanged by maintaining the scale-free nature of the topology. Enhanced ROSE and EASO-ROSE Copyright © 2021 Inderscience Enterprises Ltd. 344 T.N. Qureshi et al. significantly improve the topology robustness by calculating the difference in nodes’ degree while rearranging the surrounding angles according to the highest degree node. CAGA and AGA also significantly improve the topology robustness by using adaptive probabilities of crossover and mutation that guide both algorithms to converge towards global optimum solution. Extensive simulations are preformed to evaluate the performance of the proposed strategies. Schneider R is used as a performance metric in the simulations. The results depict that the proposed algorithms perform 61.3%, 48.3%, 45.5% and 34.95%, better than simulating annealing algorithm.