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

TL;DR

This paper introduces QQMR, a Q-learning-based routing protocol for IoMT WBANs that improves QoS by classifying data, optimizing routing, and reducing energy use.

Contribution

It presents a novel QoS-aware multipath routing protocol using Q-learning, adaptive queuing, and clustering for IoMT-based wireless body area networks.

Findings

Enhanced packet delivery ratio achieved.

Reduced delay, routing overhead, and energy consumption.

Effective data classification and path selection.

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.