Improving Energy Efficiency in Wireless Sensor Networks using Fuzzy Logic Technique

Abstract

Modern communication approaches have been transformed by the growth of sensor networks, with Wireless Sensor Networks (WSNs) being essential in gathering and relaying critical environmental data to Base Stations (BS). However, network efficiency is hampered by problems including complicated node placement, poor connection, and low battery life, especially in data-intensive applications like data monitoring and analysis. Tailored sensor node topologies and routing protocols suited for data analysis activities are necessary to address these problems. In-depth study of WSN architecture, design constraints for video traffic data processing, pertinent applications, and important research issues are presented in this work. To enhance traffic data transfer from Cluster Heads (CH) to BS, we present an intelligent route selection strategy using a Fuzzy Technique based on Artificial Intelligence (AI). The proposed approach uses fuzzy triangular functions for input range representation to incorporate three important factors: cluster traffic status, distance to the sink, and CH residual energy. By ensuring effective and efficient data transfer, this method improves WSN efficiency for applications involving the data collection and monitoring task. The research work provides several benefits, including increased network performance, longer network operating lifespans, and more network durability.