mlsanityData to 7 Oct 2026

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

arXiv 2407.11036 ↗First posted 5 Jul 2024cs.AI cs.NIcited by 7

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 25 of its 59 references that are in the map. First posted on arXiv 5 Jul 2024.

Builds on

25 of 59 references.

References · 25

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agendacited by 67
  3. 2022MDM: Molecular Diffusion Model for 3D Molecule Generationcited by 58
  4. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  5. 2022A Survey on Metaverse: Fundamentals, Security, and Privacycited by 51
  6. 2022Efficient Diffusion Models for Vision: A Surveycited by 50
  7. 2023Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Servicescited by 33
  8. 2023AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharingcited by 21
  9. 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Studycited by 21
  10. 2023From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlookscited by 16
  11. 2023Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshnesscited by 15
  12. 2023Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Predictioncited by 10
13 more
  1. 2023Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaversescited by 10
  2. 2024Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoTcited by 9
  3. 2017Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort Systemcited by 8
  4. 2023Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approachcited by 8
  5. 2023Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaversescited by 7
  6. 2023Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Studycited by 7
  7. 2022Bi-directional Digital Twin and Edge Computing in the Metaversecited by 5
  8. 2022Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaversecited by 5
  9. 2023When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migrationcited by 4
  10. 2024Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaversecited by 4
  11. 2022Joint Compute-Caching-Communication Control for Online Data-Intensive Service Deliverycited by 2
  12. 2022Vetaverse: A Survey on the Intersection of Metaverse, Vehicles, and Transportation Systemscited by 2
  13. 2019A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safetycited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 13
  2. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
  3. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Modelscited by 4
  4. 2025Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communicationscited by 3
  5. 2025Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaversescited by 2
  6. 2026Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approachcited by 0
  7. 2026From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networkscited by 0

Similar, not cited

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  1. 2024Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks
  2. 2025A Digital Twin Framework for Metamorphic Testing of Autonomous Driving Systems Using Generative Model
  3. 2026Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
  4. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
  5. 2025Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks
  6. 2025Optimization-Guided Diffusion for Interactive Scene Generation
  7. 2026Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
  8. 2024Versatile Behavior Diffusion for Generalized Traffic Agent Simulation