An Optimizing MAC Protocol for WBANs Managing Multi-class and Multi-load in IEEE 802.15.6 Superframes Using Hysteretic Q-learning

Wireless Body Area Networks (WBANs) have become a fundamental technology for continuous healthcare monitoring by enabling reliable communication among wearable and implantable biomedical sensors. Since WBAN applications generate heterogeneous traffic with different Quality of Service (QoS) requirements, efficient Medium Access Control (MAC) protocols are essential to provide reliable data delivery while maintaining low latency and energy consumption. Although the IEEE 802.15.6 standard supports multiple access phases for different traffic priorities, its predefined superframe structure cannot efficiently adapt to dynamically varying traffic loads, leading to underutilized bandwidth, increased packet collisions, and reduced network performance. To address these limitations, this paper proposes an Optimizing Multi-Class Multi-Load Handling MAC (OMMH-MAC) protocol that integrates Hysteretic Q-learning (HQL) into the IEEE 802.15.6 superframe for adaptive slot allocation and dynamic traffic scheduling. The proposed protocol first classifies network traffic and estimates network load. Based on the learned HQL policy, the Body Coordinator (BC) dynamically allocates TDMA slots within the IEEE 802.15.6 superframe. Simulation outcomes exhibit that the proposed protocol achieves faster convergence, higher throughput, lower packet collisions, and reduced energy consumption and making it suitable for dynamic healthcare monitoring applications.