Joint Optimization of Data Urgency and Freshness in Wireless Body Area Networks for Enhanced eHealth Monitoring

Abstract

Wireless Body Area Networks (WBANs), as an effective technology for electronic health monitoring, have transformed traditional consumer electronics (CE) into the next generation of devices with enhanced connectivity and intelligence. The improved interconnectivity between sensor nodes, coordinators, and other consumer devices has increased data availability and enabled autonomous monitoring within CE networks. However, due to the time-sensitive nature of physiological data transmission in WBANs and the urgency of sensor node data, addressing real-time data transmission under dynamic link conditions remains a significant challenge. To tackle this issue, we propose a joint optimization scheduling strategy that considers both data urgency and freshness. Our proposed strategy consists of two key components: a Sink Channel Allocation (SCA) strategy and a Node Scheduling Selection (NSS) strategy. By integrating deep reinforcement learning (DRL), we overcome the challenges posed by the large action space in channel allocation and timeslot selection, thereby improving scheduling efficiency. Both theoretical analysis and simulation results demonstrate that our method significantly outperforms traditional approaches in terms of real-time data transmission and scheduling optimization.