基于强化学习的多路径路由，提升医疗物联网网络性能
A Q-learning-based QoS-aware multipath routing protocol in IoMT-based wireless body area network
- 用Q-learning动态选择主备路径，分优先级处理医疗数据
- 相比现有方法，丢包率更低，延迟和能耗减少超20%
- 适合对实时性与稳定性要求高的可穿戴医疗设备
物联网医疗（IoMT）推动智能医疗服务发展，但面临拓扑动态变化、能量受限及多样服务质量（QoS）需求等挑战。本文提出一种基于Q-learning的面向QoS的多路径路由协议（QQMR），用于体域网（WBAN）。QQMR将数据分为三类优先级，采用自适应多级队列和模糊C均值聚类优化路由决策，为每类数据维护独立学习策略，并据此选择主路径与备用路径。实验结果表明，相较于现有方法，该方案显著提升了包送达率，同时大幅降低延迟、路由开销和能量消耗。
原文摘要 · Abstract (English)
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.
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