A Survey of Machine Learning Approaches for Energy Consumption Optimization in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) play a vital role in numerous fields, such as environmental monitoring, industrial automation, and healthcare systems. One of the major limitations in WSNs is the finite energy capacity of sensor nodes, which directly affects the network’s lifespan. Leveraging Machine Learning (ML) methods offers promising solutions to enhance energy efficiency by enabling intelligent control over routing strategies, data handling, and fault management processes. This study explores the influence of various ML techniques on minimizing energy consumption within WSNs. We assess the performance of different ML approaches, including supervised learning, reinforcement learning, and clustering methods, to determine their effectiveness in energy optimization. Through simulation-based analysis, our findings reveal that ML-driven models significantly outperform conventional routing protocols in terms of energy savings, contributing to improved network performance and prolonged node longevity.