Energy Efficient Wireless Sensor Network with Machine Learning

Wireless Sensor Networks (WSNs) face critical challenges in energy efficiency due to the limited power resources of sensor nodes. This paper presents a comprehensive analysis of how machine learning (ML) techniques can be leveraged to significantly reduce energy consumption and extend the operational lifetime of WSNs. Key strategies include feature selection through Recursive Feature Elimination (RFE), knowledge-based optimization models, multi-level energy-saving frameworks, and adaptive clustering and routing protocols. The paper also highlights the role of reinforcement learning and predictive models in dynamically managing network behavior to minimize redundant transmissions and enhance reliability. Performance metrics such as energy savings, latency reduction, network lifetime, and communication efficiency are evaluated across various ML-integrated approaches. Comparative analysis demonstrates that predictive models achieve up to 92.68% transmission suppression, clustering methods yield a 48.85% improvement in energy efficiency, and routing optimizations extend network stability by over 31%. The findings underscore the transformative potential of ML in creating intelligent, adaptive, and energy-efficient WSNs, laying the groundwork for future smart city and IoT applications.