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Tree-searching based trust assessment through communities in vehicular networks

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Abstract

In vehicular networks, trustworthy information sharing between vehicles is an important security issue. We find that existing trust systems in vehicular networks have the disadvantages of high assessment latency and high maintenance cost. In this paper, by introducing mobile edge computing (MEC), we propose a tree-searching based trust assessment method through communities, named TTAC method, for vehicular networks. The proposed TTAC method includes two parts. First, based on information interactions, TTAC gives a direct trust assessment method by utilizing Dempster-Shafer (D-S) evidence theory. Second, with the assistance of MEC base stations, TTAC designs a tree-searching based indirect trust calculation method by utilizing two neural networks through vehicles’ communities. In experiments, we use a dataset of Shenzhen taxicab traffic and simulate information interactions among vehicles. The experimental results show that TTAC method can ensure fast calculation time with high assessment accuracy in a distributed manner. Especially, in terms of the accuracy of the indirect trust assessment, the mean square error (MSE) of TTAC method is lower than that of two popular trust assessment methods, compared with 3VSL by 41.4% and with MoleTrust by 71.4%.

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Notes

  1. The establishment of any social network is based on interactions. Although the interactions should be based on trust, the social network does not have any trust relationships at the initial stage. At this moment, we need to try to initiate interactions randomly or subjectively to gradually build a social network. When the trust is established, it can guide interactions. Therefore, the trust and the interactions are closely related and affect each other.

  2. Our work only provides trust opinions to users. The threshold about whether to receive a message sent by a trustee according to the trust opinion is made by users themselves.

  3. Here we select a classic distributed community detection method. Of course, other distributed community detection methods are also available.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61972080, in part by the Shanghai Rising- Star Program under Grant 19QA1400300, in part by the National Key Research and Development Project under Grant 2018YFB2100801.

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Authors and Affiliations

  1. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Zhong Li & Xueting Yang

  2. The Key Laboratory of Embedded System and Service Computing Ministry of Education, Tongji University, Shanghai, 201804, China

    Zhong Li & Changjun Jiang

  3. Department of Computer Science, Tongji University, Shanghai, 201804, China

    Changjun Jiang

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  1. Zhong Li
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  2. Xueting Yang
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  3. Changjun Jiang
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Correspondence to Changjun Jiang.

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Li, Z., Yang, X. & Jiang, C. Tree-searching based trust assessment through communities in vehicular networks. Peer-to-Peer Netw. Appl. 14, 1854–1868 (2021). https://doi.org/10.1007/s12083-021-01114-5

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