An Energy-Aware Cluster-Head Selection for Improving Network Lifetime in Wireless Sensor Networks Using Machine Learning Techniques
Received: 2 November 2025 | Revised: 23 November 2025 and 5 December 2025 | Accepted: 10 December 2025 | Online: 30 January 2026
Corresponding author: Shobha Chandra K.
Abstract
Energy efficiency and reliable communication continue to be critical challenges in Wireless Sensor Networks (WSNs), primarily due to the limited energy resources of battery-operated sensor nodes. To address this, the present study introduces a novel hybrid machine learning–driven clustering and routing framework that combines swarm intelligence with reinforcement learning for adaptive, energy-aware network management. Unlike traditional clustering schemes such as Low-Energy Adaptive Clustering Hierarchy (LEACH) and Hybrid Energy-Efficient Distributed (HEED), or single-heuristic approaches like Particle Swarm Optimization–based Cluster-Head selection (PSO-CH) and Artificial Bee Colony–based Cluster-Head selection (ABC-CH), the proposed design integrates PSO and ABC algorithms with a Firefly-based exploitation mechanism for improved CH selection. Additionally, a Q-learning-assisted adaptive decision layer enables each node to autonomously refine CH participation and routing choices, whereas a fuzzy logic and Genetic Algorithm (GA)–tuned reinforcement learning model dynamically optimizes inter-cluster routing paths. Extensive MATLAB simulations demonstrate that the proposed hybrid model achieves up to 25–30% improvement in network lifetime, with delays in First Node Death (FND), Half Node Death (HND), and Last Node Death (LND) compared to benchmark protocols. The framework also achieves a 20% reduction in energy consumption per delivered packet, a 15% higher Jain's fairness index, and near-unity Packet Delivery Ratio (PDR) across all test configurations. These gains confirm more balanced energy utilization, stable connectivity, and efficient data aggregation even in large-scale topologies. By unifying metaheuristic exploration, learning-based adaptation, and fuzzy optimization, the proposed approach closes the existing gap between static clustering and adaptive routing, offering a scalable and intelligent solution for real-world WSN and Internet of Things (IoT) applications.
Keywords:
Wireless Sensor Networks (WSNs), hybrid swarm intelligence, reinforcement learning, adaptive routing, machine learningDownloads
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