A Q-learning-based QoS-aware multipath routing protocol in IoMT-based wireless body area network
Authors: Mehdi Hosseinzadeh, Roohallah Alizadehsani, Amin Beheshti, Hamid Alinejad-Roknyd, Lu Chen, Mohammad Sadegh Yousefpoor, Efat Yousefpoor, Muneera Altayeb, +2 more
Organizations: School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam · Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Victoria, Australia · School of Computing, Macquarie University, Sydney, Australia · UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia · Department of Computer Science, Zhejiang University, Hangzhou, Zhejiang 310027, China · Center of Research and Strategic Studies, Lebanese French University, Kurdistan Region, Iraq · Faculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan · Center of Excellence in Precision Medicine and Digital Health, Chulalongkorn University, Bangkok, Thailand · Department of Computer Engineering, Gachon University, Seongnam-si, South Korea
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.