UAV Frequency-based Crowdsensing Using Grouping Multi-agent Deep Reinforcement Learning

• Published in: Ji suan ji ke xue Vol. 50; no. 2; pp. 57 - 68
• Main Authors: Zhang, Cui, Wang, En, Yang, Funing, Yang, Yongjian, Jiang, Nan
• Format: Journal Article
• Language: Chinese; English
• Published: Chongqing Guojia Kexue Jishu Bu 01.01.2023; College of Computer Science and Technology,Jilin University,Changchun 130012,China%College of Information Engineering,East China Jiao tong University,Nanchang 330013,China
• Subjects: Algorithms; Clustering; Deep learning; Machine learning; Multiagent systems; Sensors; Task scheduling; Teams; Training; Unmanned aerial vehicles; UAV Crowdsensing; Grouping multi-agent deep reinforcement learning; Frequency coverage
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.221100114

Mobile CrowdSensing（MCS） is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently, unmanned aerial vehicles（UAVs） as the powerful sensing devices are used to replace user participation and carry out some special tasks, such as epidemic monitoring and earthquakes rescue.In this paper, we focus on scheduling UAVs to sense the task Point-of-Interests（PoIs） with different frequency coverage requirements.To accomplish the sensing task, the scheduling strategy needs to consider the coverage requirement, geographic fairness and energy charging simultaneously.We consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach（G-MADDPG） to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm（DCA）,then it regards each team as an agent.In this way, G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large, and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of teams.Extensive simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy.