HYBRID SWARM INTELLIGENCE-BASED LOCALIZATION OPTIMIZATION FOR ENERGY-CONSTRAINED WIRELESS SENSOR NETWORKS

Abstract

Wireless sensor networks (WSNs) rely on an accurate localization of their nodes as the data that are sensed can only have a meaning when its location is indicated. The major aim of the paper is to develop an efficient robust localization optimization model, which would enhance the precision of the position with low computational and communication energy expenditures under constrained node energy resources. To that end, a Hybrid Swarm Intelligence-Based Localization Optimization (HSILO) is offered, a combination of the global search ability of Particle Swarm Optimization (PSO) and the high local exploitation and convergence stability of Grey Wolf Optimization (GWO). In the hybrid approach suggested, PSO is utilized in order to the possible search space swiftly and produces promising position estimates whereas GWO fines the position estimates by means of hierarchical leadership and adaptive encircling processes, effectively decreasing localization error and convergence time. The fitness function is a combination of the localization error, residual energy balance, and communication cost, which is then considered as energy awareness. Significant simulation findings indicate that the proposed HSILO approach has substantially reduced average localization error, rapid convergence and enhanced network lifetime over the individual PSO, GWO and traditional localization approaches.