Model for enhancing wireless sensor network energy efficiency through genetic algorithm-driven optimization

Abstract

This study presents a model to enhance energy efficiency in Wireless Sensor Networks (WSNs) using a Genetic Algorithm (GA)-based optimization technique. The research addresses the problem of excessive energy consumption in WSNs, which significantly limits their performance and operational lifetime. The primary objective is to develop an optimized clustering and communication model that reduces energy usage while ensuring reliable data transmission. The methodology involved a simulation of clustered WSNs with visibility constraints and varying energy levels, integrating GA to compute optimal routing paths. The results showed that the Gaoptimized model outperformed traditional random and all-sensor selection methods in conserving energy and improving network lifetime. The study concludes by recommending the adoption of evolutionary algorithms like GA in the design of energy-aware WSN protocols