# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 2 Related Work
### Progress of IoV Trust Management Model  
Compared with traditional identity authentication methods, the low latency and low computing power consumption of trust management solutions are more adaptable to complex real-world environments. In [24], [PERSON] et al. proposed a trust model using a Dirichlet distribution, reputation regression, and revocation penalty to objectively and accurately reflect the trust status of vehicles, improving the accuracy of malicious vehicle detection and the anti-attack capability of networked vehicles. In [25], [PERSON] et al. used a Bayesian inference model to verify the messages received from adjacent vehicles. An RSU calculated the trust value offset of related vehicles based on the ratings uploaded by the vehicle and packaged these data into blocks. Experimental results showed that the system is effective and feasible in collecting, calculating, and storing the trust values of vehicle networks. In [26], [PERSON] et al. proposed a trust management mechanism, MESMERIC, based on machine learning, which considers direct trust, indirect trust, and context, and separates trustworthy and untrustworthy vehicles through an optimal decision boundary, achieving performance superior to that of other trust management models. In [27], [PERSON] et al. designed an incentive mechanism based on the trust value management method of alliance blockchain to encourage vehicles to actively participate in blockchain maintenance. In [28], [PERSON] et al. designed a powerful trust management system by integrating blockchain with the IoV. [PERSON], [PERSON] et al. proposed a novel approach [29] that synergistically integrates an adaptive particle convergence optimization algorithm with a blockchain-based architecture to enhance data trust and alleviate congestion, thereby paving the way for efficient, secure, and reliable vehicular communication networks. A dual-layer blockchain trust management (DLBTM) mechanism was proposed by [PERSON] et al. [30], which uses logistic regression to accurately calculate the trust value of a vehicle and predicts the probability that the vehicle will provide satisfactory service to other nodes in the next stage. [PERSON] et al. [31] proposed an improved multi-source multi-weight subjective logic algorithm, which integrates the direct and indirect opinion feedback of nodes through a subjective logic trust model and considers factors, such as event validity, familiarity, timeliness, and trajectory similarity, to ensure the security of data storage and sharing. [PERSON] et al. [32] proposed a reliability and recommendation (ReTrust)-based scheme, which uses a combined trust model to detect rogue nodes in an IoV network and evaluate the credibility of vehicles. In [33], [PERSON] et al. proposed an architecture to manage the reputation of terminal devices in an Internet of Things (IoT) system and deploy devices based on their locations for data management. The credit management model has significantly advanced research in IoV due to its low resource consumption and strong dynamic adaptability. However, existing solutions primarily emphasize dynamic adaptability and incentive mechanisms, while failing to adequately address issues related to data security storage and overall system security.