Age-Aware Collaborative Scheduling for Ensuring Data Freshness in WBAN-Based Health Monitoring Systems

To ensure timely information dissemination, researchers are exploring the integration of a novel performance metric known as the age of information (AoI) into scheduler designs for real-time monitoring systems. However, existing AoI schedulers can make incorrect scheduling decisions in real-world communication systems because they penalize the AoI of each source over time and allocate time slots when the AoI of a source increases or reaches a threshold, even if no new updates from that source exist. This results in serious resource waste, thereby increasing the average AoI, particularly in health monitoring systems with heterogeneous traffic conditions. In this study, we investigate this problem for the first time with the goal of minimizing the weighted average AoI in a wireless body area network (WBAN)-based health monitoring system. Our key idea in solving the incorrect scheduling problem is to arrange time slots with exact or nearly exact sampling start times for each source within the scheduling space. To this end, we propose a collaborative method that combines centralized and decentralized scheduling schemes. Through comprehensive simulations, we demonstrate that collaborative scheduling can incorporate heterogeneous data streams and effectively minimize the AoI in a WBAN-based health monitoring system.