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Journal of Computer Networks and Communications
Volume 2017, Article ID 7348141, 10 pages
https://doi.org/10.1155/2017/7348141
Research Article

GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks

1Department of Computer Science, Pondicherry University, Puducherry, India
2Department of Computer Science, KL University, Vaddeswaram, India

Correspondence should be addressed to R. Rajakumar; moc.liamg@erahskumar

Received 16 November 2016; Revised 18 February 2017; Accepted 2 March 2017; Published 21 March 2017

Academic Editor: Arun K. Sangaiah

Copyright © 2017 R. Rajakumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO) and modified bat algorithm (MBA). The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.