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

Wireless body area networks (WBANs) for healthcare monitoring require age-of-information (AoI)-aware resource allocation under heterogeneous periodic and aperiodic traffic. Existing AoI-aware resource allocation methods can be broadly divided into centralized, decentralized, and hybrid approaches, but each has a structural limitation: centralized scheduling may allocate time slots to sources without newly generated samples, decentralized access may suffer from collision-induced delay under heavy contention, and fixed hybrid access may fail to adapt the scheduled and random access regions to the current traffic composition. To jointly address these limitations, this paper formulates a sample-wise weighted AoI minimization problem that accounts for source-specific sampling periods, transmission lengths, and priority weights, and proposes an online collaborative hybrid scheduler. The proposed method extracts traffic features at runtime, classifies sources as periodic or aperiodic, schedules periodic samples through contention-free access close to their sampling start times, and supports aperiodic samples through random access without pre-reserving slots. It further adapts the contention-free and random access regions according to the detected traffic composition. Simulation results show that the proposed scheduler reduces sample-wise weighted AoI compared with centralized and decentralized AoI schedulers by mitigating incorrect scheduling, reducing collision-induced delay, and improving superframe utilization.