Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses

TL;DR

The paper tackles VT migration in the vehicular metaverse, addressing challenges from high mobility, edge-server heterogeneity, and security threats. It introduces a secure framework with a two-layer trust evaluation to quantify edge-server credibility and models VT migration as a POMDP; a novel hybrid-GDM DRL method generates hybrid (continuous and discrete) migration actions via forward and reverse diffusion processes. Key contributions include the two-layer trust mechanism, the POMDP formulation for VT migration, and the diffusion-based hybrid-action generator with actor-critic training, all validated by simulations showing latency reductions and robust performance under attacks. The work advances practical VT migration by delivering reliable, low-latency immersive experiences in heterogeneous vehicular metaverses and sets the stage for broader defense-in-depth considerations.

Abstract

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.

Figures (9)

• Figure 1: Secure and reliable vehicle twin migration framework for vehicular metaverses.
• Figure 2: Two-layer trust evaluation model.
• Figure 3: The overall architecture of the hybrid-GDM algorithm.
• Figure 4: Comparison of test reward curves of different algorithms.
• Figure 5: Rewards under different utility weight ratios.