Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses
<|reference_start|>Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses: The vehicular metaverse is envisioned as a blended immersive domain that promises to bring revolutionary changes to the automotive industry. As a core component of vehicular metaverses, Vehicle Twins (VTs) are digital twins that cover the entire life cycle of vehicles, providing immersive virtual services for Vehicular Metaverse Users (VMUs). Vehicles with limited resources offload the computationally intensive tasks of constructing and updating VTs to edge servers and migrate VTs between these servers, ensuring seamless and immersive experiences for VMUs. However, the high mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. To address these issues, we propose a secure and reliable VT migration framework in vehicular metaverses. Specifically, we design a two-layer trust evaluation model to comprehensively evaluate the reputation value of edge servers in the network communication and interaction layers. Then, we model the VT migration problem as a partially observable Markov decision process and design a hybrid-Generative Diffusion Model (GDM) algorithm based on deep reinforcement learning to generate optimal migration decisions by taking hybrid actions (i.e., continuous actions and discrete actions). Numerical results demonstrate that the hybrid-GDM algorithm outperforms the baseline algorithms, showing strong adaptability in various settings and highlighting the potential of the hybrid-GDM algorithm for addressing various optimization issues in vehicular metaverses.<|reference_end|>
@article{kang2024hybrid-generative,
    title={Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in
  Vehicular Metaverses},
    author={Yingkai Kang, Jinbo Wen, Jiawen Kang, Tao Zhang, Hongyang Du, Dusit
  Niyato, Rong Yu, and Shengli Xie},
    journal={arXiv preprint arXiv:2407.11036},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11036},
    primaryClass={cs.AI cs.NI}
}