HQARRF: Hierarchical Q-learning and Force-aware Routing for Multi-Charger Scheduling in Wireless Rechargeable Sensor Networks
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...
| Authors | Liang-Ching Tao, Pi-Chung Wang |
|---|---|
| Primary category | cs.NI |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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