Joint optimization of data sensing and computing in the air-ground collaborative inference framework: A multi-agent hybrid-action DRL approach
文献类型：期刊论文
| 作者 | Fan, Xiaokun1,3; Chen, Yali3; Liu, Min2,3,4; Zhu, Yuchen3; Li, Zhongcheng3,4 |
| 刊名 | COMPUTER NETWORKS
 |
| 出版日期 | 2025-10-01 |
| 卷号 | 270页码:13 |
| 关键词 | Edge computing Joint sensing and computing Multi-agent deep reinforcement learning Unmanned aerial vehicle Video surveillance |
| ISSN号 | 1389-1286 |
| DOI | 10.1016/j.comnet.2025.111540 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001536317400003 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41768]  |
| 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.Shanghai Satellite Network Res Inst Co Ltd, Shanghai Key Lab Satellite Network, State Key Lab Satellite Network, Shanghai, Peoples R China 2.Zhongguancun Lab, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, 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. |
入库方式： OAI收割
来源：计算技术研究所
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