基于深度强化学习的联合能源补充和数据收集计划为WRSN
Jishan Li1, Zhichao Deng1, Yong Feng1
1Yunnan Key Laboratory of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究引入了一个新的深度强化学习方案,用于无线可充电传感器网络. 它优化了使用无人机的能源补充和数据收集,提高了网络效率并降低了成本.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 无线可充电传感器网络 (WRSN) 通过移动充电车 (MCV) 实现连续运行.
- 现有的WRSN策略往往无法有效地整合能源补充和数据收集.
- 无人驾驶飞行器 (UAV) 为多功能WRSN应用提供了高机动性.
研究的目的:
- 提出一个使用深度强化学习的WRSN的联合能源补充和数据收集计划 (D-JERDG).
- 优化WRSN中数据收集和无线充电过程的集成.
- 为了利用无人机的移动性,实现高效的网络运行.
主要方法:
- 使用K-意味着集群的网络分区.
- 通过改进的动态路由协议,根据剩余的能量和位置选择集群头.
- 使用模拟火 (SA) 确定最短飞行路径.
- 使用多目标深确定性政策梯度 (MODDPG) 模型的无人机控制和悬浮点优化.
- 奖励功能重新设计,以共同优化节点死亡率,无人机吞吐量和能源消耗.
主要成果:
- D-JERDG计划有效地整合了数据收集和能源补充.
- MODDPG模型成功地优化了无人机飞行路径和悬浮点.
- 与基线方法相比,拟议方案显示了吞吐量,时间利用率和降低收费成本的显著改善.
- 实现了多个目标的联合优化,包括节点存活,数据吞吐量和能源效率.
结论:
- D-JERDG 方案为优化 WRSN 性能提供了一个强大的解决方案.
- 深度强化学习,特别是MODDPG,对于基于无人机的复杂WRSN管理是有效的.
- 拟议的方法在WRSN的效率和成本效益方面提供了实质性的优势.
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