Joint optimization of data sensing and computing in the air-ground collaborative inference framework: A multi-agent hybrid-action DRL approach

doi:10.1016/j.comnet.2025.111540

	Joint optimization of data sensing and computing in the air-ground collaborative inference framework: A multi-agent hybrid-action DRL approach
	Fan, Xiaokun 1,2; Chen, Yali 1; Liu, Min 1,3,4; Zhu, Yuchen 1; Li, Zhongcheng 1,3
	2025-10-01
发表期刊	COMPUTER NETWORKS
ISSN	1389-1286
卷号	270 页码:13
摘要	Unmanned aerial vehicles (UAVs) are increasingly used for surveillance applications to take videos for Points of Interests (PoIs). Then, the sampled video data is fed into deep neural networks (DNNs) for inference. Due to the high computational complexity of DNNs, directly running DNN inference tasks on resource-constrained UAVs is intractable. To alleviate this issue, edge computing provides a promising solution by offloading tasks to the ground edge servers (ESs). However, how to flexibly schedule and tradeoff various resources for high-accuracy and low-delay inference is a challenge, especially in the complex scenario where video data sensing and DNN task processing are tightly coupled. Thus, this paper studies joint optimization for data sensing and computing in the air-ground collaborative inference framework. Specifically, the models for multi-UAV collaborative data sensing and collaborative inference between multiple UAVs and multiple ESs are designed. Then, we formulate an inference delay minimization problem by jointly optimizing UAVs' 3D trajectories, number of sampled video frames and computation offloading, while satisfying accuracy, UAV energy budget and sensing mission requirements. Considering mixed continuous-discrete optimization variables, we propose a multi-agent proximal policy optimization (MAPPO) algorithm with a hybrid action space, called "MAPPO-HA", to learn the optimal policies. Finally, simulation results demonstrate that our algorithm can achieve better performance compared with other optimization approaches.
关键词	Edge computing Joint sensing and computing Multi-agent deep reinforcement learning Unmanned aerial vehicle Video surveillance
DOI	10.1016/j.comnet.2025.111540
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62202449] ; National Natural Science Foundation of China[62472410] ; National Key Research and Development Program of China[2021YFB2900102]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001536317400003
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41769
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Min
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Shanghai Satellite Network Res Inst Co Ltd, Shanghai Key Lab Satellite Network, State Key Lab Satellite Network, Shanghai, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Zhongguancun Lab, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Fan, Xiaokun,Chen, Yali,Liu, Min,et al. Joint optimization of data sensing and computing in the air-ground collaborative inference framework: A multi-agent hybrid-action DRL approach[J]. COMPUTER NETWORKS,2025,270:13.
APA	Fan, Xiaokun,Chen, Yali,Liu, Min,Zhu, Yuchen,&Li, Zhongcheng.(2025).Joint optimization of data sensing and computing in the air-ground collaborative inference framework: A multi-agent hybrid-action DRL approach.COMPUTER NETWORKS,270,13.
MLA	Fan, Xiaokun,et al."Joint optimization of data sensing and computing in the air-ground collaborative inference framework: A multi-agent hybrid-action DRL approach".COMPUTER NETWORKS 270(2025):13.