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

arXiv: https://arxiv.org/abs/2604.15489 · HTML: https://mlsanity.com/paper/2604.15489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 42 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things](https://mlsanity.com/paper/2202.09657/) (2022)

## Abstract (arXiv)

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.