Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 9, Pages 16504-16513Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3129458
Keywords
Peer-to-peer computing; Security; Bayes methods; Trust management; Wireless communication; Data communication; Analytical models; Vehicular ad hoc networks; malicious nodes; cooperative data communication; trust assessment and management; historical interaction records; bayesian inference
Categories
Funding
- National Key Research and Development Program of China [2020YFB2103805]
- National Natural Science Foundation of China [61972358]
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This paper focuses on trust node management in VANETs, aiming to quantify node credibility to avoid malicious nodes. It introduces an integrated trust calculation method, combining direct and recommended trust, and ensures the weight of the latest information through a time sliding window and time decay function. Experimental results demonstrate the method outperforms baseline methods in packet delivery ratio and security.
Vehicular ad hoc networks (VANETs) provide self-organized wireless multihop transmission, where nodes cooperate with each other to support data communication. However, malicious nodes may intercept or discard data packets, which might interfere with the transmission process and cause privacy leakage. We consider historical interaction data of nodes as an important factor of trust. Thus, this paper focuses on the trust node management of VANETs, which aims to quantify node credibility as an assessment method and avoid assigning malicious nodes. First, the integrated trust of each node is proposed, which consists of the direct trust and the recommended trust. The former is dynamically computed by historical interaction records and Bayesian inference considering penalty factors. The latter defines trust by third-party nodes and their reputation. Second, the process of trust calculation and data communication calls for timeliness. Therefore, we introduce a time sliding window and time decay function to ensure that the latest interaction information has a higher weight. We can sensitively identify malicious nodes and make quick responses. Finally, the experimental results demonstrate that our proposed method outperforms bassline methods, especially with respect to the packet delivery ratio and security.
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