A Fuzzy Control System for Performance Optimization in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) play a vital role in numerous domains such as environmental monitoring, healthcare, industrial automation, and smart city infrastructures. Despite their growing relevance, WSNs face persistent challenges including limited energy resources, high data loss, network instability, and latency issues. To address these concerns, this study explores the integration of fuzzy logic as a means of optimizing WSN performance under uncertain and dynamic conditions. A fuzzy logic-based control system was designed to adaptively regulate key parameters such as node energy, packet loss, and connectivity. Simulations were conducted with varying node densities (100, 200, and 300 nodes) to assess the effectiveness of this approach. Results revealed notable improvements: energy consumption was reduced by up to 0.65%, network lifetime extended by up to 0.28%, packet delivery ratio increased by up to 3.10%, and average latency decreased by up to 43.8%. These outcomes underscore the potential of fuzzy logic to enhance the adaptability, efficiency, and reliability of WSNs, offering a practical and scalable solution for performance optimization in real-world deployments