Analysis of Machine Learning Methods for Optimizing WSNS

Abstract

Wireless Sensor Networks (WSNs) have become integral to modern sensing applications, ranging from environmental monitoring to industrial automation. Optimizing WSNs is essential for extending network lifespan, improving data reliability, and minimizing energy consumption. This paper presents a comparative evaluation of key optimization methodologies—including dynamic programming, artificial neural networks, energy-efficient sleep scheduling, cluster-based management, and performance evaluation techniques. Using parameters like efficiency, routing table modification time, rerouting delays, and battery utilization, we analyze the suitability of each method across different network scales. Our results offer actionable insights for selecting the most appropriate approach depending on network dynamics and application constraints.