A Truth-Oriented Trust Evaluation Model of Shared Traffic Messages in the Internet of Vehicles.

Jiamin Zhang, Lisha Shuai, Jiuling Dong, Gaoya Dong, Xiaolong Yang, Keping Long

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Jiamin ZhangSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0002-5682-8945

Lisha ShuaiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0001-5677-7976

Jiuling DongSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Gaoya DongSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0002-9617-4499

Xiaolong YangSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0001-5102-6962

Keping LongSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Funding

National Natural Science Foundation of China 61971033

6 · The paper itself

Abstract

The Internet of Vehicles (IoV) provides an effective solution for alleviating traffic congestion and enhancing road safety. However, shared traffic messages in IoV may deviate from on-road conditions due to self-interest protection or insufficient sensor performance. Therefore, evaluating the trustworthiness of shared messages is essential for vehicles to make informed decisions. To this end, a truth-oriented trust model for shared traffic message is proposed, which is inspired by human trust establishment mechanisms (HS-TEMs). Firstly, we quantify the integrated trust value (I-VT) of the message sender by fusing self-experience-based vehicle trust (SEB-VT) and peer-recommendation-based vehicle trust (PRB-VT). In SEB-VT, a sample-size-dependent smoothing factor dynamically trades off prior information and empirical evidence, reducing instability under small-sample conditions. In PRB-VT, we employ link analysis to compute the reference degree of recommendation information, which mitigates biases arising from subjective cognitive limitations. Secondly, with the I-VT of vehicles, we calculate event trust (ET) by differentiating message attitudes and quantifying their relative influence, which effectively reduces the impact of individual bias on the final judgment. The simulation results show that HS-TEM can accurately and fairly evaluate the credibility of messages, which helps vehicles make informed decisions.

Indexed as

internet of vehicleslink analysis technologysmoothing factortrust evaluation modeltrustworthiness of the message

Identifiers

PMID41294956

DOI10.3390/e27111113

PMCPMC12651147

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.