Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse

• URL: http://arxiv.org/abs/2403.15285v1
• Date: Fri, 22 Mar 2024 15:31:37 GMT
• Title: Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
• Authors: Jiawen Kang, Xiaofeng Luo, Jiangtian Nie, Tianhao Wu, Haibo Zhou, Yonghua Wang, Dusit Niyato, Shiwen Mao, Shengli Xie,
• Abstract summary: Vehicle Twins (VTs) provide valuable metaverse services to improve driving safety and on-board satisfaction for VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. Existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses.
• Score: 73.79237826420925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

Related papers

• Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses [58.264499654343226]
  Vehicle Twins (VTs) are digital twins that provide immersive virtual services for Vehicular Metaverse Users (VMUs) High mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. We propose a secure and reliable VT migration framework in vehicular metaverses.
  arXiv  Detail & Related papers  (2024-07-05T11:11:33Z)
• Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach [57.15309977293297]
  The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses. We propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
  arXiv  Detail & Related papers  (2024-01-18T02:14:13Z)
• Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses [40.75866071306496]
  Vehicular Twins (VTs) are digital replicas of Vehicular Metaverse Users (VMUs) VTs are established and maintained in RoadSide Units (RSUs) with sufficient computing and storage resources. During VT migrations, physical-virtual synchronization and massive communications among VTs may cause identity and location privacy disclosures. We propose a VMU-VT dual pseudonym scheme and a synchronous pseudonym change framework to defend against these attacks.
  arXiv  Detail & Related papers  (2023-09-01T14:14:33Z)
• Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction [70.9337170201739]
  We propose a model to predict the future trajectories of intelligent vehicles based on their historical data. We show that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction.
  arXiv  Detail & Related papers  (2023-06-26T13:27:11Z)
• Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning [8.513938423514636]
  Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs to develop a central Metaverse Map. Information from the Metaverse Map can then be downloaded into individual IoVs on demand and be delivered as AR scenes to the driver.
  arXiv  Detail & Related papers  (2023-03-18T00:03:50Z)
• Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse [129.8037449161817]
  We propose a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In virtual-to-physical synchronization, MARs customize diverse and personal AR recommendations via generative AI models based on user preferences.
  arXiv  Detail & Related papers  (2023-01-18T16:25:42Z)
• Mobile Edge Computing for the Metaverse [15.738852406775289]
  The Metaverse has emerged as the next generation of the Internet. It aims to provide an immersive, persistent virtual space where people can live, learn, work and interact with each other. Existing technology is inadequate to guarantee high visual quality and ultra-low latency service for the Metaverse players. Mobile Edge Computing (MEC) is a paradigm where proximal edge servers are utilized to perform computation-intensive and latency-sensitive tasks like image processing and video analysis.
  arXiv  Detail & Related papers  (2022-12-19T03:37:32Z)
• A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges [93.06849621984684]
  "The successor to the mobile Internet", the Metaverse has grown in popularity. lite versions of the Metaverse exist today, but they are far from realizing the full vision of an immersive, embodied, and interoperable Metaverse. Without addressing the issues of implementation from the communication and networking, the Metaverse is difficult to succeed the Internet. We discuss the computation challenges and cloud-edge-end computation framework-driven solutions to realize the Metaverse on resource-constrained edge devices.
  arXiv  Detail & Related papers  (2022-03-10T16:48:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.