Reinforcement Learning-Based Clustering for Energy Optimization in Wireless Sensor Networks

Abstract

The constrained energy resources of sensor nodes constitute a fundamental challenge in the deployment and sustainability of large-scale Wireless Sensor Networks (WSNs). Clustering, a well-established energy-efficient topology management technique, mitigates this issue by aggregating data through designated Cluster Heads (CHs). However, conventional clustering protocols often rely on static or probabilistic parameters, rendering them suboptimal in the face of dynamic network conditions such as node energy depletion and fluctuating traffic patterns. This paper investigates the application of Reinforcement Learning (RL) for dynamic clustering and energy optimization in WSNs. By formulating the cluster head selection and formation as a sequential decision-making problem, RL-enabled nodes can autonomously learn optimal policies that maximize network longevity and energy efficiency. The proposed RL-based framework adapts to the network's state, intelligently balancing energy consumption and load distribution. We provide a comprehensive review of the integration of RL algorithms, including Q-learning and Deep Q-Networks (DQN), into the clustering paradigm. The discussion synthesizes findings from contemporary literature, highlighting how RL-driven clustering significantly outperforms traditional protocols like LEACH and its variants in terms of network lifetime, data delivery, and scalability. The paper concludes by outlining persistent challenges and promising future research directions for fully realizing the potential of RL in sustainable WSNs