A Q-learning-based QoS-aware multipath routing protocol in IoMT-based wireless body area network
This work addresses QoS-aware routing in resource-constrained IoMT/WBANs, offering an incremental improvement over existing routing protocols.
The paper proposes QQMR, a Q-learning-based multipath routing protocol for IoMT-based wireless body area networks, which classifies data into three priority levels and uses adaptive queuing and fuzzy clustering to optimize routing. Results show improved packet delivery ratio and significant reductions in delay, routing overhead, and energy consumption compared to existing methods.
The Internet of Medical Things (IoMT) enables intelligent healthcare services but faces challenges such as dynamic topology, energy constraints, and diverse QoS requirements. This paper proposes QQMR, a Q-learning-based QoS-aware multipath routing method for WBANs. QQMR classifies data into three priority levels and employs adaptive multi-level queuing and fuzzy C-means clustering to optimize routing decisions. It maintains separate learning policies for each data type and selects primary and backup paths accordingly. Experimental results demonstrate improved packet delivery ratio and significant reductions in delay, routing overhead, and energy consumption compared to existing methods.