English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Vehicular Metaverses represent emerging paradigms arising from theconvergence of vehicle road cooperation, Metaverse, and augmented intelligenceof things. Users engaging with Vehicular Metaverses (VMUs) gain entry byconsistently updating their Vehicular Twins (VTs), which are deployed onRoadSide Units (RSUs) in proximity. The constrained RSU coverage and theconsistently moving vehicles necessitate the continuous migration of VTsbetween RSUs through vehicle road cooperation, ensuring uninterrupted immersionservices for VMUs. Nevertheless, the VT migration process faces challenges inobtaining adequate bandwidth resources from RSUs for timely migration, posing aresource trading problem among RSUs. In this paper, we tackle this challenge byformulating a game-theoretic incentive mechanism with multi-leadermulti-follower, incorporating insights from social-awareness and queueingtheory to optimize VT migration. To validate the existence and uniqueness ofthe Stackelberg Equilibrium, we apply the backward induction method.Theoretical solutions for this equilibrium are then obtained through theAlternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owingto incomplete information caused by the requirements for privacy protection, weproposed a multi-agent deep reinforcement learning algorithm named MALPPO.MALPPO facilitates learning the Stackelberg Equilibrium without requiringprivate information from others, relying solely on past experiences.Comprehensive experimental results demonstrate that our MALPPO-based incentivemechanism outperforms baseline approaches significantly, showcasing rapidconvergence and achieving the highest reward.

When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration