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

Area of Science:

• Computer Science
• Network Security
• Transportation Systems

Background:

• The Internet of Vehicles (IoV) facilitates data sharing for enhanced environmental perception among vehicles.
• The spread of false information in IoV networks can lead to traffic confusion and safety risks.
• A robust vehicular trust model is essential for validating message trustworthiness.

Purpose of the Study:

• To propose a novel double-layer blockchain trust management (DLBTM) mechanism for the IoV.
• To accurately evaluate the trustworthiness of vehicle messages and detect malicious nodes.
• To enhance the reliability and security of IoV communication.

Main Methods:

• Implementation of a double-layer blockchain architecture comprising a vehicle blockchain and an RSU blockchain.
• Quantification of vehicle evaluation behavior to establish historical trust values.
• Utilization of logistic regression for computing vehicle trust values and predicting future service reliability.

Main Results:

• The DLBTM mechanism effectively identifies malicious nodes within the IoV network.
• The system demonstrates the capability to recognize at least 90% of malicious nodes over time.
• Objective and accurate evaluation of vehicle message trustworthiness is achieved.

Conclusions:

• The proposed DLBTM mechanism significantly improves the detection of malicious nodes in IoV.
• Blockchain technology offers a secure and efficient solution for vehicular trust management.
• DLBTM enhances overall IoV network security and reliability, contributing to safer traffic environments.