Adaptive Cluster Optimization in Wireless Sensor Networks Using Reinforcement Learning

Abstract

Wireless Sensor Networks (WSNs) are essential to modern Internet of Things (IoT) applications, ranging from environmental monitoring to smart agriculture. One of the primary challenges in managing WSNs is minimizing energy consumption while maintaining robust communication. Traditional clustering protocols like LEACH and HEED, though pioneering, exhibit limited adaptability in dynamic or large-scale deployments. This paper proposes a novel adaptive clustering technique based on Q-learning, a model-free reinforcement learning algorithm. By treating each sensor node as an autonomous agent, the proposed framework allows nodes to learn optimal decisions over time—such as becoming a cluster head, joining a neighboring cluster, or entering sleep mode—based on local environmental parameters like residual energy, distance to the base station, and mobility. The algorithm’s performance is evaluated via MATLAB simulations and benchmarked against LEACH and HEED. Results show significant improvements in energy efficiency, network lifetime, and cluster stability. Notably, the Q-learning-based method extends the network lifetime by up to 25% and improves packet delivery by 13% compared to conventional protocols. These findings underline the value of reinforcement learning in creating intelligent, scalable, and energy-efficient clustering mechanisms for future WSN applications. This work also paves the way for extensions using deep learning and federated architectures.

Keywords: Clustering, Energy efficiency, Network lifetime, Q-Learning, Reinforcement learning, Wireless sensor network.

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