DEERL-WSN: A Distributed Energy-Efficient Reinforcement Learning Approach for Wireless Sensor Networks in IoT Applications

Abstract

Wireless Sensor Networks (WSNs) play a crucial role in the expanding landscape of the Internet of Things (IoT), yet they continue to face persistent challenges related to energy consumption, computational efficiency, and scalability. Although protocols like the Energy-Efficient Routing Protocol through Hybrid Algorithms (EERHA) have made progress in extending network lifespan using machine learning, they still encounter limitations particularly in managing processing overhead, adapting to changing conditions, and scaling to larger deployments. Unlike existing approaches that merely combine machine learning modules, DEERL-WSN (Distributed Energy-Efficient Reinforcement Learning for Wireless Sensor Networks), introduces a unified hierarchical learning framework where distributed reinforcement learning, lightweight graph neural networks, transfer learning, and federated optimization operate cooperatively. The novelty lies in (i) adaptive reward-driven routing using meta-learned objective weights, (ii) topology-aware clustering through lightweight GNN embeddings with significantly reduced computational complexity, (iii) transfer learning-assisted cluster-head prediction to eliminate repetitive optimization overhead, and (iv) federated deep reinforcement learning enabling scalable learning without centralized processing bottlenecks. These integrated innovations collectively address energy efficiency, scalability, and computational constraints simultaneously, which remain largely unresolved in existing WSN routing frameworks. DEERL-WSN addresses several bottlenecks found in earlier protocols and the simulation results show that DEERL-WSN significantly outperforms EERHA and other state-of-the-art methods.