# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 2 Related Work
### Reputation Evaluation and Management  
Due to the characteristics of blockchain, such as decentralization, irrevocability, traceability, transparency, autonomy, and anonymity, a group of scholars have proposed a reputation management model based on the blockchain to meet the implementation requirements of reputation security management in VANET. In [23], [PERSON] et al. proposed an improved three value subjective logic (3 VSL) algorithm, which combines the historical reputation value, historical interaction information, and interaction frequency of vehicle nodes to evaluate the reputation value of vehicles, and uses reputation threshold to identify malicious vehicles. However, this algorithm lacks relevant content and simulation for the integration of blockchain technology, and does not provide consensus on the use of the blockchain, which may have problems in implementation. [PERSON] et al. [33] designed a blockchain-based reputation evaluation model to solve security vulnerabilities and privacy issues on the autonomous vehicle social network, as well as to incentivize legal behaviors and content delivery of vehicles. In [34] the author proposed a blockchain-based vehicle network data reliability evaluation system. The reputation value is rated based on historical reputation. Both schemes adopt the PoW (proof of work) consensus, which is inefficient and there are obstacles in its implementation. In [35], [PERSON] and [PERSON] proposed a contract-based incentive mechanism for secure block verification that could encourage more miners to participate in block verification. A weighted subjective logic model is adopted to build a safe and efficient reputation management scheme and use an improved DPoS (delegated proof of stake) consensus to reduce collusion between stakeholders and mining candidates. The scheme prevents less than \(\frac{1}{3}\) of malicious attackers. The disadvantage is that the accuracy of the weight allocation is not considered. The development of a secure reputation management scheme helps to prevent the exchange of forged data and to eliminate sources that disperse such data, thus ensuring safe, reliable, and efficient traffic. Current studies can generally be divided into three categories [36]: (1) data-centric, emphasizes the authenticity of the exchanged information; (2) entity-centric, emphasizes the reliability of vehicle entity; and (3) hybrid model, considers both entity and data legitimacy. For reputation evaluation, considerations include interaction-based attributes, indirect trust of neighbor nodes, and the social metrics of the driver (as the decision-making authority). For the data-centric model, [PERSON] et al. [14] evaluated the trust level of the data by correlating the weight of the reports shared by the neighbor vehicle nodes that depends on the proximity of the vehicle to the time and location of the reported event. In the trust computation scheme, weight-based voting, Bayesian inference, and the D-S theory are compared. In [37], [PERSON] et al. evaluated the trust level of messages by considering content conflict and similarity as well as similarity of routing paths, and then assigned trust values to each message. The problem with data-centric reputation evaluation is the potential for delays and data loss in heavy traffic scenarios and poor performance when data are scarce. For the entity-centric model, [PERSON] et al. [38] classified vehicles as trusted or malicious nodes based on a predefined threshold during a trust assessment. In [39], a reputation score was obtained based on the fusion of the vehicle's current trust, its level of cooperation, and the last-hop recommendation. The problem with entity-centric reputation evaluation is that it does not take into account the highly mobile characteristic of IoV, and the authenticity of information cannot be guaranteed. For the hybrid model, in [PERSON] et al. (2018), [PERSON] et al. evaluated the node and data trust under several attack scenarios.