Hybrid Reinforcement Learning “HRL” Approach with Energy Harvesting for Energy-Efficient Routing Protocols “EERP” “HRL-EERP” in Wireless Body Area Networks “WBANs” (Preprint)
BACKGROUND Wireless Body Area Networks (WBANs) revolutionize healthcare by utilizing miniature, low-power sensors, implanted or worn externally, to provide continuous, real-time tracking of vital signs like heart rate, blood pressure, temperature, and glucose levels. However, their potential is hindered by significant routing challenges, particularly energy efficiency, due to the limited battery life of sensor nodes and the dynamic, reliability-critical nature of medical applications. OBJECTIVE This paper introduces HRL-EERP, a Hybrid Reinforcement Learning-based Energy-Efficient Routing Protocol for WBANs, designed to address these issues. METHODS HRL-EERP integrates Deep Q-Network (DQN) to optimize multi-hop paths for low latency, Q-Learning to ensure efficient local routing decisions, Restricted Data Transmission (RDT) to minimize energy use by suppressing redundant packets, and Energy Harvesting (EH) to sustain node operation. RESULTS Evaluated using NS-3 with PhysioNet MIT-BIH Arrhythmia Database data (60-80 bpm baseline, 150 bpm anomalies, 86,400 samples/day), HRL-EERP achieved a 34-hour network lifetime, 0.019 mJ/s energy efficiency, 96.8% PDR, and 88 ms latency across 56,160 packets/day (post-35% RDT suppression), meeting ECG <100 ms requirement. RDT saved 1,512 mJ/day, while EH provided 120-720 mJ/day/node (0.05-0.3 mJ/s across head, chest, wrist). CONCLUSIONS HRL-EERP delivers a robust, energy-conscious solution for time-sensitive WBAN applications. CLINICALTRIAL None applicable