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

AI in healthcare
Published: arXiv: 2604.15489v1
Authors

Mehdi Hosseinzadeh Roohallah Alizadehsani Amin Beheshti Hamid Alinejad-Roknyd Lu Chen Mohammad Sadegh Yousefpoor Efat Yousefpoor Muneera Altayeb Thantrira Porntaveetus Sadia Din

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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.

Paper Summary

Problem
The Internet of Medical Things (IoMT) has expanded rapidly, enabling intelligent communication among medical devices, systems, and services. However, this has introduced challenges in designing efficient routing methods for IoMT-based Wireless Body Area Network (WBAN) networks. The dynamic nature of network topology, energy constraints of sensor nodes, and the need to guarantee Quality of Service (QoS) for diverse medical data pose serious challenges.
Key Innovation
This research introduces a novel Q-learning-based QoS-aware multipath routing method called QQMR. QQMR uses a QoS-aware clustering algorithm based on adaptive weighted fuzzy C-means to reduce the state space and accelerate convergence. The algorithm classifies sensed data into three categories based on QoS requirements and uses an adaptive multi-level queuing model to manage heterogeneous traffic.
Practical Impact
The QQMR method can be applied in real-world IoMT-based WBAN networks to improve the quality of healthcare services and patient well-being. By dynamically making routing decisions based on QoS requirements, QQMR can increase packet delivery rates, reduce average end-to-end delays, and lower energy consumption. This can lead to more efficient and reliable medical processes.
Analogy / Intuitive Explanation
Imagine a hospital with many interconnected medical devices and systems. Each device sends and receives sensitive medical data, such as patient vital signs and test results. QQMR is like a smart traffic manager that dynamically routes these data packets based on their priority and type. It uses a complex algorithm to optimize the flow of data, ensuring that critical information reaches its destination quickly and efficiently, while also conserving energy and reducing delays.
Paper Information
Categories:
cs.NI cs.AI
Published Date:

arXiv ID:

2604.15489v1

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