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.
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