Performance Evaluation of Hybrid Evolutionary Algorithms in Minimizing Localization Error for Wireless Sensor Networks

Abstract

<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:="" hi"="" lang="EN-GB">Localization is considered as one of the most significant research issues in Wireless Sensor Network (WSN). The objective of localization is to determine the physical co-ordinates of sensor nodes distributed over the sensing field. Location information plays a vital role for coverage, deployment of sensor nodes, routing and target tracking applications. Initially, the localization of sensor nodes can be performed by Mobile Anchor Positioning (MAP), a range-free localization method. To further enhance the location accuracy obtained by MAP, we propose three algorithms, viz. Differential Evolution with MAP (DE-MAP), Ant Colony Optimization with MAP (ACO-MAP) and<b style="mso-bidi-font-weight:normal"> </b>Simulated Annealing<b style="mso-bidi-font-weight:normal">-</b>Differential Evolution with MAP (SA-DE-MAP). The scope of this work is to compare the performance of these three algorithms. Root Mean Square Error (RMSE) has been used as the metrics for comparing the performance. Simulation result demonstrates that out of the proposed algorithms, SA-DE-MAP algorithm achieves better performance in minimizing the localization error when compared to DE-MAP and ACO-MAP algorithms.</span>