Reputation Value-Based Converged Dual-Channel Digital Forensics for Blockchain-Enabled Smart Vehicles
Jingying Liang, Chen Miao, Jing Chen, Rui Zhu, Kaixian Lu, Liyang Jiao · IEEE Transactions on Intelligent Vehicles · 2024
Due to the mobility of intelligent vehicles, limited performance of communication devices, and distributed deployment, the integrity and authenticity of accessible information cannot be ensured during traditional digital vehicle forensics in the event of a vehicle collision. For this reason, we propose a new attribute-based access control model. Meanwhile, to reduce insurance disputes after a vehicle collision and complicate the investigation of criminal activities by law enforcement, we classify different user behaviors according to the definition of accident rights and responsibilities in real situations based on traditional insurance methods, and provide different renewal discounts for different categories of users respectively based on the incentive mechanism of reputational value creation, to encourage users' participation and reduce insurance disputes. Considering the resource constraints of real communication devices, we implemented a dual channel for converged data and deployed the corresponding smart contracts separately. We use Raspberry Pi simulations to implement resource-constrained light nodes and implement the proposed framework as multiple machines built on multiple devices in the Hyperledger Fabric blockchain platform. In addition, we conduct a series of experiments to test the incentives and the performance of the blockchain and verify the feasibility of the proposed scheme. Comprehensive evaluation and analysis results show that the framework promotes user participation while ensuring the integrity and authenticity of evidence, bridging the gap of user participation in embedded digital forensics.