A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things

Abstract

Emerging applications in the Internet of Things (IoT) will depend on the accurate location of thousands of deployed sensors. However, accurate localization of deployed sensors nodes is a classical optimization problem which falls under NP-hard class of problems. Therefore in this work, we propose a new distributed localization algorithm using social learning based particle swarm optimization (SL-PSO) for IoT. With SL-PSO algorithm, we aim to do precise localization of deployed sensor nodes and reduce the computational complexity which will further enhance the lifetime of these resource-constrained IoT sensor nodes. Extensive simulations are carried out to show the effective performance of the SL-PSO algorithm in accurate localization. Experimental results depict that SL-PSO algorithm can not only increase convergence rate but also significantly reduce average localization error compared to traditional particle swarm optimization (PSO) and its other variants.