Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses
Jinbo Wen, Jiawen Kang, Zehui Xiong, Yang Zhang, Hongyang Du, Yutao, Jiao, Dusit Niyato

TL;DR
This paper proposes an incentive mechanism for vehicle twin migration in vehicular metaverses, using a new metric AoMT to ensure task freshness and optimize resource contribution among RSUs.
Contribution
It introduces a novel AoMT metric and an AoMT-based contract model to incentivize RSUs for efficient vehicle twin migration in vehicular metaverses.
Findings
The proposed incentive mechanism effectively encourages RSUs to contribute bandwidth resources.
Numerical results show improved task freshness and migration efficiency.
The contract model maximizes RSU utility in VT migration scenarios.
Abstract
Vehicular metaverse, which is treated as the future continuum between automotive industry and metaverse, is envisioned as a blended immersive domain as the digital twins of intelligent transportation systems. Vehicles access the vehicular metaverses by their own Vehicle Twins (VTs) (e.g., avatars) that resource-limited vehicles offload the tasks of building VTs to their nearby RoadSide Units (RSUs). However, due to the limited coverage of RSUs and the mobility of vehicles, VTs have to be migrated from one RSU to other RSUs to ensure uninterrupted metaverse services for users within vehicles. This process requires the next RSUs to contribute sufficient bandwidth resources for VT migrations under asymmetric information. To this end, in this paper, we design an efficient incentive mechanism framework for VT migrations. We first propose a novel metric named Age of Migration Task (AoMT) to…
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Taxonomy
TopicsTransportation and Mobility Innovations · Age of Information Optimization · IoT and Edge/Fog Computing
