3D weighted centroid localization algorithm for wireless sensor network using teaching learning based optimization

Abstract

The purpose of this paper is to improve the localization accuracy of range-free algorithm in three-dimensional (3D) space for wireless sensor networks (WSNs). In this paper, weighted centroid localization algorithm using teaching learning based optimization (WCL-TLBO) is proposed to improve the positioning accuracy in 3D space of WSN. In range-free algorithms, only received signal strength (RSS) information between nodes is sufficient to determine the position of target nodes. RSS value gives the clue to find out the distance between sensor nodes but shows non-linearity between RSS and distance. To overcome this non-linearity, fuzzy logic system (FLS) is used. Edge weights of WCL are modelled using FLS. Further to reduce the errors, TLBO is applied to optimize these edge weights. Simulation results establish the superiority of the proposed algorithm in terms of localization accuracy to other existing range-free algorithms in similar scenario.