A Reputation Assessment Method for Vehicular Networks Based on the Fusion of Spatiotemporal Features and Blockchain

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A Reputation Assessment Method for Vehicular Networks Based on the Fusion of Spatiotemporal Features and Blockchain

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[1]TIAN Zhao,ZHOU Zheng,NIU Ya Jie,et al.A Reputation Assessment Method for Vehicular Networks Based on the Fusion of Spatiotemporal Features and Blockchain[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10.13705/j.Issn.1671-6833.2026.06.008]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 48 Number of periods: 2027 XX Page number: 1-8 Column: Public date: 2027-12-10

Title:: A Reputation Assessment Method for Vehicular Networks Based on the Fusion of Spatiotemporal Features and Blockchain

Author(s):: TIAN Zhao^1,2, ZHOU Zheng^1,2, NIU Ya Jie^1,2, Lu Hao Jie^1,2, LIU Wei^1,2 ZAI Guang Jun^1,2; 1.School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China; 2.Zhengzhou Key Laboratory of Blockchain and Data IntelligenceZhengzhou 450000China;

Keywords:: intelligent transportation; blockchain; artificial intelligence; internet of vehicles; reputation assessment

CLC:: TP389.1

DOI:: 10.13705/j.Issn.1671-6833.2026.06.008

Abstract:: Aiming at the problem of untrusted interaction data caused by malicious attacks and selfish behaviors of nodes in the Internet of Vehicles (IoV), and the issue that existing methods are prone to cause reputation depreciation, a reputation assessment method fusing blockchain and spatiotemporal features was proposed. First, the Gaussian Naive Bayes algorithm was introduced to fuse temporal and spatial features, aiming to improve the accuracy of reputation assessment in dynamic environments. Second, reputation was updated based on the event confirmation degree to achieve more reliable reputation aggregation. Finally, a reward and punishment mechanism and a taxation mechanism based on signaling games were deployed in smart contracts to maintain the dynamic balance of global reputation. Simulation results showed that the identification precision and recall of the proposed method remained above 82% and 81%, respectively. Facing highly concealed malicious switching attacks, it could reduce the reputation of attacking nodes to zero within 2.5 minutes. This method effectively suppressed complex network attacks and rational selfish behaviors, mechanically avoided system reputation depreciation, and guaranteed the security of data interaction in the IoV

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Last Update: 2026-06-03