Air-ground integrated networks can relieve communication pressure on ground
transportation networks and provide 6G-enabled vehicular Metaverses services
offloading in remote areas with sparse RoadSide Units (RSUs) coverage and
downtown areas where users have a high demand for vehicular services. Vehicle
Twins (VTs) are the digital twins of physical vehicles to enable more immersive
and realistic vehicular services, which can be offloaded and updated on RSU, to
manage and provide vehicular Metaverses services to passengers and drivers. The
high mobility of vehicles and the limited coverage of RSU signals necessitate
VT migration to ensure service continuity when vehicles leave the signal
coverage of RSUs. However, uneven VT task migration might overload some RSUs,
which might result in increased service latency, and thus impactive immersive
experiences for users. In this paper, we propose a dynamic Unmanned Aerial
Vehicle (UAV)-assisted VT migration framework in air-ground integrated
networks, where UAVs act as aerial edge servers to assist ground RSUs during VT
task offloading. In this framework, we propose a diffusion-based Reinforcement
Learning (RL) algorithm, which can efficiently make immersive VT migration
decisions in UAV-assisted vehicular networks. To balance the workload of RSUs
and improve VT migration quality, we design a novel dynamic path planning
algorithm based on a heuristic search strategy for UAVs. Simulation results
show that the diffusion-based RL algorithm with UAV-assisted performs better
than other baseline schemes.

本文提出了一种基于动态无人机辅助的车辆双生物迁移框架，使用空地一体化网络中的无人机作为辅助服务器，以平衡 RSU 的负载并提高双生物迁移质量。模拟结果表明，与其他基准方案相比，基于扩散式强化学习算法和无人机辅助的方法效果更好。

基于扩散的动态无人机辅助车辆双子迁移在车辆元宇宙中的强化学习

Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle  Twins Migration in Vehicular Metaverses

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.

基于跨元区和边缘计算技术，我们提出了一种用于车辆边缘元宇宙的双伪名管理框架，利用跨链技术提高伪名的管理效率和数据安全性，采用多智能体深度强化学习方法获得最佳伪名生成策略，数值结果表明我们的方案具有高效和经济高效的特点，在车辆边缘元宇宙中具有广泛的应用前景。