Semantic-Aware Scheduling for Minimizing Age of Informative Data in WBAN-Based Health Monitoring Systems

Since outdated information can lead to critical situations in health monitoring systems, recent schedulers have adopted the Age of Information (AoI) as a key decision metric. However, traditional AoI-based schedulers penalize all data uniformly over time, even when successive packets contain redundant content, failing to prioritize informative data. Although semantic-aware AoI schedulers address this limitation, they often become overly sensitive to minor variations in content, neglecting to quantify data criticality and ultimately behaving like traditional AoI-based schedulers. To overcome this issue, in this article, a new semantic-aware AoI scheduler is proposed for wireless body area network (WBAN)-based health monitoring systems. The scheduling objective focuses on minimizing the weighted average AoI, where the weights are based on deviations from historical data, ensuring timely emergency detection. Additionally, an effective optimization algorithm is developed and applied to achieve this goal. Simulation results show that the proposed scheduler improves system performance across key metrics, including average AoI, weighted average AoI, and throughput, ensuring the freshness of informative data in WBAN-based health monitoring systems.