An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication

Abstract

Recently, wireless sensor network (WSN) enabled indoor communication provides an effective and flexible method for local area networks mainly in large buildings or in a group of several buildings. Node localization can be considered as a major process which helps to calculate the coordinate points of the unknown nodes with the assistance of known (anchor) nodes. Earlier studies have considered node localization problem as an NP hard problem. Several metaheuristics techniques are employed for resolving the localization problem in WSN that extremely decreases the localization error. This paper designs an effective metaheuristic-based Group Teaching Optimization Algorithm for Node Localization (GTOA-NL) technique for WSN. The goal of the GTOA-NL technique is to determine the position of the unknown nodes by the use of anchor nodes in the WSN with minimum localization error and maximum localization accuracy. The presented GTOA is stimulated from the group teaching strategy and it can be used for optimization process with no loss of generality. In order to guarantee the effective node localization performance of the presented GTOA-NL model, an extensive set of simulations were performed to highlight the supremacy of the GTOA-NL model. The obtained results have ensured the superior performance of the GTOA-NL model over the other compared methods under varying number of anchor nodes, ranging error, and transmission range.