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

TL;DR

The paper addresses moral hazard in the vehicular Metaverse by introducing a reputation-based incentive mechanism that uses subjective logic to assign reputations to SIoT devices based on VMU feedback. It integrates a vanishing-like decay for past ratings and a reputation backpropagation scheme to attribute feedback to specific devices, coupled with a social-welfare driven reverse auction to procure semantic data. Through extensive simulations on real-world data, the approach demonstrates substantial suppression of poisoning attacks while minimizing unfair expulsion of trustworthy devices, thereby strengthening market integrity and data reliability. This framework offers a practical path to secure and trustworthy data sharing for digital twins in the vehicular Metaverse, with potential extensions in weighting schemes for historical data and dynamic parameter optimization.

Abstract

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.

Figures (4)

• Figure 1: System model of anti-poisoning attacks vehicular Metaverse ecosystem.
• Figure 3: Representation of the cube under interest.
• Figure 4: Acceptance rate of SIoT devices from different types with and without reputation mechanism.
• Figure 5: Average social welfare and successful attack rates in different scenarios.