A Q-learning-based QoS-aware multipath routing protocol in IoMT-based wireless body area network

arXiv cs.AI / 4/20/2026

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Key Points

  • The paper introduces QQMR, a Q-learning-based QoS-aware multipath routing protocol tailored for WBANs within the IoMT, addressing challenges like dynamic topology and limited energy.
  • QQMR classifies traffic into three priority levels and uses adaptive multi-level queuing combined with fuzzy C-means clustering to improve routing decision quality.
  • It learns using separate reinforcement-learning policies for different data types and selects primary and backup paths based on the corresponding learned behavior.
  • Experiments indicate that QQMR improves packet delivery ratio while significantly reducing delay, routing overhead, and overall energy consumption versus existing approaches.

Abstract

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.