REINFORCEMENT LEARNING STRATEGIES FOR ENERGY EFFICIENT CLUSTER HEAD SELECTION IN WSNS
DOI:
https://doi.org/10.63503/c.acset.2025.7Keywords:
Wireless Sensor Networks, Cluster Head Selection, Reinforcement Learning, Q-learning, Energy Efficiency, Network Lifetime, Node DensityAbstract
Wireless Sensor Networks (WSNs) are important for numerous IoT applications. The limitation of the universal applications of WSN is due to the constrained energy reservoirs of the sensor nodes. For data communication, clustering of sensor nodes is an established scheme for minimizing the energy expenditure by appointing Cluster Heads (CHs) responsible for data aggregation and relay. This paper describes an approach for developing the cluster and CH selection dynamically for WSNs, leveraging Reinforcement Learning (RL), specifically Q-learning. The framework of this paper integrates residual energy level of a node and localized node density into the RL state space, empowering individual nodes to make perceptive decisions regarding their roles. Moreover, the CH selection mechanism prioritizes nodes that not only exhibit elevated Q-values for the CH function but are also optimum distance from the Base Station (BS), thereby minimizing cumulative transmission energy. The simulations show that this RL-driven, adaptive paradigm effectively equilibrates energy consumption throughout the network, resulting in an extended network lifespan and improved energy efficiency compared to traditional methodologies.
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