在异质无人机辅助WSN中收集AoI-Aware数据:强势代理协调覆盖和Vicsek驱动的弱势群控制.
Lin Huang1,2, Lanhua Li3, Songhan Zhao3
1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了使用高容量和低容量无人机 (UAV) 进行高效数据收集的层次框架. 这种新的方法显著减少了无线传感器网络中的数据信息时代 (AoI).
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 无线通信无线通信
背景情况:
- 无人驾驶飞行器 (UAV) 群是有效的数据收集,但面临环境动态和资源限制的挑战.
- 现有的集中式或纯强化学习方法难以在复杂环境中平衡全球解决方案质量与本地灵活性.
研究的目的:
- 为异质无人机辅助无线传感器网络 (WSN) 提出一个层次化的数据收集框架.
- 加强高容量无人机 (H-UAV) 的协调和决策,管理许多低容量无人机 (L-UAV).
- 为了减少地面用户 (GUs) 的信息时代 (AoI).
主要方法:
- 一个使用多代理深度强化学习 (MADRL) 的层次框架,特别是多代理深度决定性政策梯度 (MADDPG),用于H-UAV协调.
- 根据GU数据速率和L-UAV密度动态更新的Power-Voronoi分区.
- L-UAV使用加权的Vicsek模型进行自我组织的数据收集和机会性中继,考虑AOI,链接质量和拥堵.
主要成果:
- 拟议的强和弱剂MADDPG (SW-MADDPG) 方案显示了数据收集效率的显著改善.
- 与No-Voronoi基线相比,AOI减少了30%.与No-Voronoi基线相比,AOI减少了30%.
- 与启发式HUAV基线相比,AOI降低了21%.
结论:
- 具有空间分解和分散的弱群控制的等级框架使大规模无人机部署中的可扩展和高效的数据收集成为可能.
- SW-MADDPG方法有效地平衡工作量并适应环境动态,在减少AOI方面表现优于现有的方法.
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