Machine Learning-Based Energy Optimization in Wireless Sensor Networks for Smart Cities

Abstract

Wireless Sensor Networks (WSNs) form the foundational infrastructure for smart city applications, enabling real-time data collection and monitoring across urban environments. However, the energy-constrained nature of sensor nodes presents significant challenges to network longevity and operational sustainability. This paper presents a comprehensive analysis of machine learning (ML) approaches for energy optimization in WSNs tailored for smart city applications. Through systematic review of contemporary literature and comparative analysis of ML techniques including Reinforcement Learning (RL), Supervised Learning, and Swarm Intelligence, we identify optimal strategies for energy-efficient routing, cluster head selection, and data aggregation. Our findings demonstrate that hybrid ML models achieve 30-45% improvement in network lifetime compared to traditional protocols, with Reinforcement Learning-based approaches showing particular promise for dynamic smart city environments. The paper contributes a taxonomy of ML-enabled energy optimization techniques, quantitative performance comparisons, and practical implementation guidelines for smart city WSN deployments.