RLFS-OR: Reinforcement Learning-Based Forwarder Selection for Opportunistic Routing in Wireless Sensor Networks

This paper introduces RLFS-OR, a reinforcement learning-based opportunistic routing protocol designed for energy-constrained and duty-cycled wireless sensor networks (WSNs). Unlike traditional opportunistic routing, which either relies on static metrics or requires nodes to remain continuously active, RLFS-OR integrates a Deep Q-Network (DQN) to dynamically select the most energy-efficient forwarder based on residual energy, hop distance, wake-up timing, and link quality. A realistic Castalia-derived radio model is incorporated, accounting for transmission, reception, idle listening, and path loss-dependent energy consumption. Through coordinated learning and asynchronous duty-cycle integration, RLFS-OR minimizes overhearing and unnecessary wake-ups. Simulation results demonstrate that RLFS-OR significantly outperforms two established protocols—ORW and FCM-OR—achieving 10–30% lower energy consumption and 10–45% longer network lifetime under diverse network densities and traffic loads. RLFS-OR also provides smoother node-death dynamics and optimal performance at low duty cycles. The findings confirm RLFS-OR as an efficient and scalable solution for long-lived WSN deployments.