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

TL;DR

This paper introduces a reputation-based incentive mechanism to enhance trust and security in the vehicular Metaverse by preventing malicious SIoT devices from delivering falsified data while protecting honest devices.

Contribution

The paper proposes a novel reputation-based incentive mechanism using subjective logic to mitigate moral hazard in SIoT devices within the vehicular Metaverse.

Findings

The mechanism effectively prevents malicious devices from poisoning the system.

Trustworthy devices are not unfairly excluded due to false positive ratings.

Simulation results validate the mechanism's efficiency in maintaining system integrity.

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.