mlsanityData to 7 Oct 2026

Enhancing Trust and Security in the Vehicular Metaverse: A Reputation-Based Mechanism for Participants with Moral Hazard

arXiv 2405.19355 ↗First posted 23 May 2024cs.CR cs.AIcited by 2

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Abstract (arXiv)

In this paper, we tackle the issue of moral hazard within the realm of the vehicular Metaverse. A pivotal facilitator of the vehicular Metaverse is the effective orchestration of its market elements, primarily comprised of sensing internet of things (SIoT) devices. These SIoT devices play a critical role by furnishing the virtual service provider (VSP) with real-time sensing data, allowing for the faithful replication of the physical environment within the virtual realm. However, SIoT devices with intentional misbehavior can identify a loophole in the system post-payment and proceeds to deliver falsified content, which cause the whole vehicular Metaverse to collapse. To combat this significant problem, we propose an incentive mechanism centered around a reputation-based strategy. Specifically, the concept involves maintaining reputation scores for participants based on their interactions with the VSP. These scores are derived from feedback received by the VSP from Metaverse users regarding the content delivered by the VSP and are managed using a subjective logic model. Nevertheless, to prevent ``good" SIoT devices with false positive ratings to leave the Metaverse market, we build a vanishing-like system of previous ratings so that the VSP can make informed decisions based on the most recent and accurate data available. Finally, we validate our proposed model through extensive simulations. Our primary results show that our mechanism can efficiently prevent malicious devices from starting their poisoning attacks. At the same time, trustworthy SIoT devices that had a previous miss-classification are not banned from the market.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

6 of 12 references.

References · 6

  1. 2020CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotationscited by 60
  2. 2022A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challengescited by 39
  3. 2022Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approachcited by 13
  4. 2022Semantic Information Market For The Metaverse: An Auction Based Approachcited by 5
  5. 2023Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Frameworkcited by 3
  6. 2021Mitigating Adversarial Attacks by Distributing Different Copies to Different Userscited by 1

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2 papers.