EHPSO: An Enhanced Hybrid Particle Swarm Optimization Algorithm for Internet of Things

Abstract

Abstract-Internet of Things (IOT) has found broad applications and has drawn more and more attention from researchers. At the same time, IOT also presents many challenges, one of which is node localization, i.e. how to determine the geographical position of each sensor node. Algorithms have been proposed to solve the problem. A popular algorithm is Particle Swarm Optimization (PSO) because it is simple to implement and needs relatively less computation. However, PSO is easily trapped into local optima and gives premature results. In order to improve the PSO algorithm, this paper proposes the EHPSO algorithm based on Novel Particle Swarm Optimization (NPSO) and Hybrid Particle Swarm Optimization (HPSO). The EHPSO algorithm applies the principle of best neighbor of each particle to the HPSO algorithm. Simulation results indicate that EHPSO outperforms HPSO and NPSO in evaluating accurate node positions and improves convergence by avoiding being trapped into local optima.

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“…The evaluation results showed a dramatic reduction of 13% in network load and 8% in latency. (Li et al, 2018) ( Malik and Dimple, 2017) proposed an Ant Colony Optimization technique for IoT network that was inspired from Nature. The technique was used to find the shortest path possible between the source and the destination node.…”

Understanding the viability of integrating WSN with IoT using cloud infrastructure

“…The evaluation results showed a dramatic reduction of 13% in network load and 8% in latency. (Li et al, 2018) ( Malik and Dimple, 2017) proposed an Ant Colony Optimization technique for IoT network that was inspired from Nature. The technique was used to find the shortest path possible between the source and the destination node.…”