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

2026-04-16

AI Summary

This work addresses the routing challenges in wireless body area networks for the Internet of Medical Things (IoMT), which arise from dynamic network topologies, stringent energy constraints, and diverse quality-of-service (QoS) requirements. To tackle these issues, the paper proposes QQMR, a novel routing protocol that uniquely integrates Q-learning with a multi-priority QoS mechanism. QQMR employs an adaptive multi-level queue to classify data into three priority levels, each governed by its own dedicated Q-learning policy. Furthermore, it enhances cluster head selection through fuzzy C-means clustering and dynamically coordinates primary and backup paths for robust routing decisions. Experimental results demonstrate that QQMR significantly improves packet delivery ratio while effectively reducing transmission delay, routing overhead, and energy consumption.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Web measurements

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.

Problem

• IoMT
• WBAN
• QoS
• multipath routing
• energy constraints

Innovation

• Q-learning
• QoS-aware routing
• multipath routing
• fuzzy C-means clustering
• wireless body area network