YSGPO: A Hybrid Metaheuristic Framework for Energy Conservation and Lifetime Maximization in Wireless Sensor Networks

Abstract

Wireless Sensor Networks consist of multiple sensor nodes that work together to monitor environmental and physical scenarios. These sensor nodes are typically battery-powered devices and are frequently deployed in remote locations, therefore energy efficiency becomes a major concern in  determining the network lifespan and reliability. In recent years, to address these challenges, numerous metaheuristic optimization techniques have been proposed particularly for clustering and energy-efficient routing. This research presents a structured and comprehensive analysis of several evolutionary and swarm intelligence algorithms for energy-efficient WSN operations. The algorithms examined include Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Differential Evolution, Artificial Bee Colony, Firefly Algorithm, Grey Wolf Optimizer, Whale Optimization, Salp Swarm Algorithm, Yellow Saddle Goatfish Optimization (YSGO), and Pelican Optimization Algorithm (POA). Moreover, a hybrid YSGPO algorithm is proposed to maintain a balance between global exploration and local exploitation. In the proposed model, YSGO is utilized for energy-aware cluster head selection while POA is applied for finding the optimal route for data transmission between cluster heads and the base station. Experimental findings of proposed algorithm are done in MATLAB using the First order radio energy model. Simulation findings indicates that the hybrid YSGPO algorithm achieves better energy efficiency, longer network stability and enhanced convergence performance when compared with aforementioned metaheuristic techniques.