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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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J. Kang, J. Chen, M. Xu, Z. Xiong, Y. Jiao, L. Han, D. Niyato, Y. Tong, and  S. Xie,  “UAV-assisted dynamic avatar task migration for vehicular metaverse services: A multi-agent deep reinforcement learning approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 430–445, Feb. 2024. doi: 10.1109/JAS.2023.123993
Citation: J. Kang, J. Chen, M. Xu, Z. Xiong, Y. Jiao, L. Han, D. Niyato, Y. Tong, and  S. Xie,  “UAV-assisted dynamic avatar task migration for vehicular metaverse services: A multi-agent deep reinforcement learning approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 430–445, Feb. 2024. doi: 10.1109/JAS.2023.123993

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

doi: 10.1109/JAS.2023.123993
Funds:  This work was supported in part by NSFC (62102099, U22A2054, 62101594); in part by the Pearl River Talent Recruitment Program (2021QN02S643), and Guangzhou Basic Research Program (2023A04J1699); in part by the National Research Foundation, Singapore, and Infocomm Media Development Authority under its Future Communications Research Development Programme, DSO National Laboratories under the AI Singapore Programme under AISG Award No AISG2-RP-2020-019, Energy Research Test-Bed and Industry Partnership Funding Initiative, Energy Grid (EG) 2.0 programme, DesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programme, and MOE Tier 1 under Grant RG87/22; in part by the Singapore University of Technology and Design (SUTD) (SRG-ISTD-2021- 165); in part by the SUTD-ZJU IDEA Grant SUTD-ZJU (VP) 202102; in part by the Ministry of Education, Singapore, through its SUTD Kickstarter Initiative (SKI 20210204)
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  • 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.


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    • We introduce a novel avatar task migration framework aimed at achieving continuous user-avatar interaction. Within this framework, vehicles choose appropriate edge servers (e.g., RSUs or UAVs) for the migration and pre-migration of tasks, enabling real-time avatar task execution in UAV-assisted vehicular Metaverses
    • In order to efficiently solve the service provisioning problem, we model the avatar task migration process as a Partially Observable Markov Decision Process. The proposed framework considers the avatar task migration problem as binary integer programming and proves that this problem is NP-hard. The challenges are then tackled using MADRL algorithms.
    • We propose a transformer-based decision-making model based on MAPPO that processes in a sequential manner. The proposed model leverages the self-attentive mechanism to perceive the relationship between agents' interactions for obtaining the optimal policy for each agent. Numerical results show that the proposed approach outperforms the existing MAPPO approach by approximately 2% and effectively reduces the latency of avatar task execution by around 20%
    • To incentivize edge servers (e.g., RSUs or UAVs) to contribute adequate resources to vehicles, we maintain transaction records of communication, computing, and storage resources exchanged between edge servers and vehicles in the blockchain. Utilizing smart contracts ensures the security and traceability of these transactions


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