Developing an Adaptive Energy-Efficient Routing Protocol Using Reinforcement Learning for Wireless Sensor Networks

Abstract

One of the core themes in wireless sensor networks is the creation of methods to enhance network lifetime. Their operation indeed relies on energy-efficient routing protocols that use minimal energy. Energy-efficient routing is a problem of utmost importance in WSNs to ensure sustainable data communication between nodes, cluster heads, and base stations without data loss. Because they are battery-powered and resource-constrained resource availability and additional battery supply is called for. Thus, Wireless Sensor Networks (WSNs) are confronted with limited energy availability due to the finite power capacity of sensor nodes, and the necessity of employing adaptive routing protocols for effective energy consumption while maintaining network operability. The present research work suggests an Adaptive Energy-Efficient Routing Protocol based on Reinforcement Learning (AEERPRL) to mitigate such challenges through ongoing optimization of routing choices for simulated and controlled network conditions. Evolution of (RL), AEERPRL streamlines nodes to learn ideal data forwarding routes, modulating energy consumption and network reliability. The protocol is compared against the RL-based RLBEEP protocol on major parameters: Alive Nodes (network lifetime), Packet Delivery Ratio (PDR), and Throughput. The experiment results demonstrate that AEERPRL outperforms RLBEEP with 10% additional surviving nodes over time, achieving a 0.8 PDR (compared to 0.6 RLBEEP) and 25% better throughput, indicating increased management and network robustness. The enhancements indicate AEERPRL's capacity for network lifetime prolongation with assured, reliable data delivery even in changing environments. The findings demonstrate the efficacy of reinforcement learning to develop and deploy adaptive routing protocols in wireless sensor networks with practical applications in precision agriculture, environmental monitoring, and Internet of Things networks where energy efficiency and scalability are essential. The research recommends the use of machine learning in wireless sensor network routing protocols and proposes a scalable model for future research in energy-limited networks.