HQARRF: Hierarchical Q-learning and Force-aware Routing for Multi-Charger Scheduling in Wireless Rechargeable Sensor Networks

Abstract:Multi-charger scheduling in wireless rechargeable sensor networks must weigh sensor death risk, charger energy, travel cost, return-to-base feasibility and inter-charger coordination at once, and schedulers driven by local urgency alone duplicate service and leave whole regions unattended. We present HQARRF, a two-level scheduler. Below, an interpretable ARR-F score ranks candidate clusters through an attraction term for local urgency, a repulsion term against charger crowding and a force bonus from nearby critical sensors. Above, adaptive zones compress regional state into a deadline-based risk estimate, and a gated Q-learning controller decides only whether to redirect service to a high-risk, under-served zone. Over 27 parameter points HQARRF attains the highest mean survival rate at 26, improving survival by 20.7 percentage points over the mean of five baselines and 9.2 over the strongest baseline at each point. An ablation isolates the upper level: its gain tracks how often the controller fires.

15 pages, 5 figures, 1 table. Under review at MAMM 2026. Also archived at this https URL

Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)

arXiv:2609.13901 [cs.NI]

(or arXiv:2609.13901v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2609.13901 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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