A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles

Wenbo Ruan; Jia Liu; Yuanfang Chen; Sardar M N Islam; Muhammad Alam

doi:10.3390/s23104699

A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles

Sensors (Basel). 2023 May 12;23(10):4699. doi: 10.3390/s23104699.

Authors

Wenbo Ruan^{1

2}, Jia Liu^{1

2}, Yuanfang Chen^{1

2}, Sardar M N Islam³, Muhammad Alam⁴

Affiliations

¹ School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China.
² Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou 310018, China.
³ Institute for Sustainable Industries and Liveable Cities (ISILC), Victoria University, Melbourne, VIC 3030, Australia.
⁴ School of Engineering, London South Bank University, London SE1 0AA, UK.

Abstract

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle's historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.

Keywords: Internet of Vehicles; blockchain; logistic regression; trust management.

Grants and funding

This research was funded by the Key Research and Development Program of Zhejiang Province No. 2023C01141. The APC was funded by Key Research and Development Program of Zhejiang Province No. 2023C01141.