UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach
文献类型：期刊论文
| 作者 | Jiawen Kang; Junlong Chen; Minrui Xu; Zehui Xiong; Yutao Jiao; Luchao Han; Dusit Niyato; Yongju Tong; Shengli Xie |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2024 |
| 卷号 | 11期号:2页码:430-445 |
| ISSN号 | 2329-9266 |
| 关键词 | Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs |
| DOI | 10.1109/JAS.2023.123993 |
| 英文摘要 | Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation, which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units (RSU) or unmanned aerial vehicles (UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning (MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization (MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers (e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54553]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Jiawen Kang,Junlong Chen,Minrui Xu,et al. UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(2):430-445. |
| APA | Jiawen Kang.,Junlong Chen.,Minrui Xu.,Zehui Xiong.,Yutao Jiao.,...&Shengli Xie.(2024).UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach.IEEE/CAA Journal of Automatica Sinica,11(2),430-445. |
| MLA | Jiawen Kang,et al."UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach".IEEE/CAA Journal of Automatica Sinica 11.2(2024):430-445. |
入库方式： OAI收割
来源：自动化研究所
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