Joint energy-aware task offloading and privacy protection in healthcare monitoring systems via deep reinforcement learning.

With the growing use of in-body health sensors (IBHS) for real-time care, large-scale data processing, mobile edge computing (MEC) support, and sustainable power have become key bottlenecks. IBHS are long-term implants, so conventional recharging is infeasible; radio-frequency wireless energy transfer (RF-WET) is the primary means of continuous power. During data uploading and energy harvesting, adversaries can passively observe application-layer signals such as offloading behavior, charging frequency, and energy states to infer user activities and locations, creating serious privacy risks. We propose a deep reinforcement learning (DRL) based task offloading framework that jointly considers MEC, energy awareness, and privacy preservation, and we introduce an energy-aware privacy entropy model to quantify risk across the offloading process. By jointly sensing wireless channels, MEC resource availability, and energy status, the scheme dynamically optimizes data uploading and computation offloading, while analyzing location-privacy leakage induced by RF-WET access points (WET-APs). Simulations show clear gains over state-of-the-art baselines in latency, energy efficiency, and location-privacy protection, indicating strong potential for next-generation edge-assisted healthcare systems.