New Q-learning Protocol Enhances Medical IoT Network Performance

A novel routing protocol called QQMR has been developed for Wireless Body Area Networks (WBANs) within the Internet of Medical Things (IoMT) framework. This method addresses critical challenges in intelligent healthcare services, including dynamic network topologies, limited energy resources, and varying Quality of Service (QoS) demands. QQMR implements a Q-learning-based approach to optimize multipath routing decisions. Data transmitted across the network is categorized into three distinct priority levels. To manage this traffic efficiently, the protocol utilizes adaptive multi-level queuing mechanisms alongside fuzzy C-means clustering techniques. Separate learning policies are maintained for each data classification type, enabling the system to select both primary and backup communication pathways appropriately. Experimental evaluations of QQMR demonstrate measurable performance improvements over existing routing methods. Specifically, the protocol achieves a higher packet delivery ratio while simultaneously reducing network delay, routing overhead, and overall energy consumption. These advancements are significant for the reliability and efficiency of medical monitoring and data transmission systems.