Energy Optimization in Wireless Sensor Networks Using Machine Learning

Abstract

Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, smart agriculture, healthcare systems, and industrial automation. However, one of the major limitations of WSNs is constrained energy resources, as sensor nodes are typically battery-powered and deployed in inaccessible environments, making energy efficiency a critical design challenge. This study investigates energy optimization in wireless sensor networks using machine learning techniques to enhance network lifetime, reduce energy consumption, and improve data transmission efficiency. The methodology involves the simulation of a WSN environment with multiple sensor nodes performing periodic sensing and data transmission tasks. Key network parameters such as node energy levels, communication distance, traffic load, and routing paths are collected and used to train machine learning models. Algorithms such as Reinforcement Learning (RL), K-Nearest Neighbors (KNN), and Decision Trees are applied to optimize routing decisions and cluster head selection. Reinforcement learning is particularly utilized to enable adaptive decision-making for energy-efficient routing based on dynamic network conditions. The performance of the proposed system is evaluated using metrics such as network lifetime, energy consumption per node, packet delivery ratio, and latency. The results indicate that machine learning-based optimization significantly reduces overall energy consumption and extends the operational lifetime of the sensor network compared to traditional energy-aware routing protocols. It is also observed that adaptive routing strategies improve load balancing among nodes, preventing early node failures and network partitioning.