| 摘要: |
| 高效的移动充电调度是构建长生命期、可持续运行的无线可充电传感器网络(WRSN)的关键之一.现有基于强化学习的充电策略只考虑了移动充电调度问题的一个维度,即移动充电器(MC)的路径规划,而忽略了充电调度问题中的另一维度,即充电时长调整,因而仍然存在性能限制.提出一种基于深度强化学习的WRSN动态时空充电调度方法(SCSD),建立充电序列调度和充电时长动态调整的深度强化学习模型.针对移动充电调度中离散的充电序列规划和连续的充电时长调整问题,使用DQN为待充电节点优化充电序列,并基于DDPG计算并动态调整序列中待充电节点的充电时长.通过分别从空间和时间两个维度的优化,在避免节点缺电失效的同时,所提出的SCSD可实现充电性能的有效提高.大量仿真实验结果表明,SCSD与现有的几种有代表性的充电方案相比,其充电性能具有明显的优势. |
| 关键词: 无线可充电传感器网络 深度强化学习 时空充电策略 充电序列 充电时长 充电性能 |
| DOI：10.13328/j.cnki.jos.006814 |
| 分类号:TP393 |
| 基金项目:国家自然科学基金(62062047,61662042,61962030) |
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| Dynamic Spatiotemporal Charging Scheduling Based on Deep Reinforcement Learning for WRSN |
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WANG Yi-Jun1, FENG Yong1, LIU Ming2, LIU Nian-Bo2
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1.Yunnan Key Laboratory of Computer Technology Applications(Kunming University of Science and Technology), Kunming 650500, China;2.School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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| Abstract: |
| Efficient mobile charging scheduling is a key technology to build wireless rechargeable sensor networks (WRSN) which have long life cycle and sustainable operation ability. The existing charging methods based on reinforcement learning only consider the spatial dimension of mobile charging scheduling, i.e., the path planning of mobile chargers (MCs), while leaving out the temporal dimension of the problem, i.e., the adjustment of the charging duration, and thus these methods have suffered some performance limitations. This study proposes a dynamic spatiotemporal charging scheduling scheme based on deep reinforcement learning (SCSD) and establishes a deep reinforcement learning model for dynamic adjustment of charging sequence scheduling and charging duration. In view of the discrete charging sequence planning and continuous charging duration adjustment in mobile charging scheduling, the study uses DQN to optimize the charging sequence for nodes to be charged and calculates and dynamically adjusts the charging duration of the nodes. By optimizing the two dimensions of space and time respectively, the SCSD proposed in this study can effectively improve the charging performance while avoiding the power failure of nodes. Simulation experiments show that SCSD has significant performance advantages over several well-known typical charging schemes. |
| Key words: wireless rechargeable sensor network (WRSN) deep reinforcement learning spatiotemporal charging scheme charging sequence charging duration charging performance |
