Hybrid Machine Learning Techniques for Lifetime Enhancement in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a vital role in applications such as environmental monitoring, healthcare, and smart infrastructure. However, their performance is critically constrained by limited node energy and inefficient routing mechanisms, which significantly shorten network lifetime. Recent advancements in machine learning (ML) and deep learning (DL) have introduced intelligent optimization techniques capable of addressing these challenges through adaptive decision-making and predictive analytics. This paper presents a hybrid ML-based framework that integrates clustering and reinforcement learning (RL) for energy-efficient network management. The proposed architecture leverages unsupervised clustering to balance energy consumption across nodes and employs RL agents to optimize routing and node activity dynamically. The framework's adaptive learning capabilities promote scalability, energy conservation, and longer operational lifespan of WSNs in dynamic environments. Furthermore, a comparative analysis of existing ML-based approaches highlights the advantages of combining clustering with reinforcement learning. The proposed model establishes a foundation for future research toward developing lightweight, distributed, and secure hybrid ML solutions for large-scale, real-time WSN applications.