HQARRF：无线可充电传感器网络中多充电器调度的分层Q学习与力感知路由
HQARRF: Hierarchical Q-learning and Force-aware Routing for Multi-Charger Scheduling in Wireless Rechargeable Sensor Networks
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中文总结 AI 辅助
HQARRF提出两级调度器，底层用ARR-F评分排序簇，顶层用门控Q学习决定重定向服务，在27个参数点上平均存活率比基线均值高20.7个百分点。
中文摘要 AI 辅助
无线可充电传感器网络中的多充电器调度必须同时权衡传感器死亡风险、充电器能量、行驶成本、返回基地的可行性以及充电器间的协调，而仅由局部紧迫性驱动的调度器会导致服务重复并使整个区域无人值守。我们提出HQARRF，一个两级调度器。在底层，一个可解释的ARR-F评分通过吸引项（针对局部紧迫性）、排斥项（针对充电器拥挤）以及来自附近关键传感器的力奖励来对候选簇进行排序。在顶层，自适应区域将区域状态压缩为基于截止时间的风险估计，一个门控Q学习控制器仅决定是否将服务重定向到高风险、服务不足的区域。在27个参数点上，HQARRF在26个点上取得了最高的平均存活率，比五个基线的平均值提高了20.7个百分点，比每个点上最强基线提高了9.2个百分点。消融实验隔离了上层：其增益与控制器触发的频率相关。
英文摘要
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.
发表机构
- National Chung Hsing University（国立中兴大学）
机构由 AI 辅助整理，请以论文原文为准。
