Comparative Analysis of Bio-Inspired Enhancement Techniques for Localization in 2D Wireless Sensor Networks

Abstract

Accurate node localization is crucial for optimizing the performance of Wireless Sensor Networks (WSNs), which are widely used in applications such as environmental monitoring and smart city infrastructure. This study conducts a comparative evaluation of three bio-inspired optimization algorithms for node localization: Particle Swarm Optimization (PSO), Fruit Fly Optimization Algorithm (FOA), and Drop Mongoose Optimization Algorithm (DMOA). The algorithms are assessed based on their localization accuracy and precision, which are key performance metrics for WSN applications. PSO, inspired by the collective movement of particle birds, is a well-established meta-heuristic technique, while FOA simulates the foraging behavior of fruit flies to determine optimal locations. The recently introduced DMOA, modeled after the hunting and movement strategies of mongooses, represents a novel approach in optimization. Simulation results indicate that while PSO and FOA perform well under specific conditions, DMOA outperforms them in terms of accuracy and precision, making it a promising solution for improving node localization in WSNs. This study underscores the effectiveness of bio-inspired algorithms in enhancing the reliability and efficiency of WSN deployments.