Learning-based Incentive Mechanism for Task Freshness-aware Vehicular Twin Migration

TL;DR

This paper introduces a learning-based incentive mechanism for VT migration in vehicular metaverses, optimizing resource trading and ensuring task freshness amid mobility and privacy constraints.

Contribution

It proposes a novel AoTM metric and a deep reinforcement learning-based Stackelberg model to improve VT migration efficiency and incentive design in vehicular metaverse environments.

Findings

The proposed mechanism effectively balances resource allocation and migration freshness.

Deep reinforcement learning successfully learns equilibrium strategies under incomplete information.

Numerical results validate the mechanism's effectiveness in vehicular metaverse scenarios.

Abstract

Vehicular metaverses are an emerging paradigm that integrates extended reality technologies and real-time sensing data to bridge the physical space and digital spaces for intelligent transportation, providing immersive experiences for Vehicular Metaverse Users (VMUs). VMUs access the vehicular metaverse by continuously updating Vehicular Twins (VTs) deployed on nearby RoadSide Units (RSUs). Due to the limited RSU coverage, VTs need to be continuously online migrated between RSUs to ensure seamless immersion and interactions for VMUs with the nature of mobility. However, the VT migration process requires sufficient bandwidth resources from RSUs to enable online and fast migration, leading to a resource trading problem between RSUs and VMUs. To this end, we propose a learning-based incentive mechanism for migration task freshness-aware VT migration in vehicular metaverses. To quantify the freshness of the VT migration task, we first propose a new metric named Age of Twin Migration (AoTM), which measures the time elapsed of completing the VT migration task. Then, we propose an AoTM-based Stackelberg model, where RSUs act as the leader and VMUs act as followers. Due to incomplete information between RSUs and VMUs caused by privacy and security concerns, we utilize deep reinforcement learning to learn the equilibrium of the Stackelberg game. Numerical results demonstrate the effectiveness of our proposed learning-based incentive mechanism for vehicular metaverses.