# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 2 Related Work
### Trust Computation  
The current trust computation scheme, according to the adopted decision logic, can be divided into different approaches based on multi-weight fusion [10; 13], Bayesian inference (BI) [14; 15; 16; 17], the Dempster-Shafer (D-S) theory [14; 19], fuzzy logic [20; 21], and three-valued subjective logic (3 VSL) [22; 23; 24; 25], etc. Bayesian inference describes the uncertainty of data-centered modeling and reasoning based on probabilities and statistics [26], which is more suitable for the quantitative judgement of interactive trust in the VANET scenario.  
According to how trust is calculated, it can be divided into direct trust [27] and a combination of direct and indirect trust [28]. In addition, social metrics [10; 29; 30] are also added based on driver behavior.  
In [15], [PERSON] et al. proposed a trust management model based on the trustrank algorithm, which considers both local trust and global trust. Local trust is obtained by applying the Bayesian inference model to past interactions of vehicles. In the evaluation of the system model, new-user attacks, on-off attacks, and collusive attacks are considered.  
[PERSON] et al. [16] proposed a trust management model using Bayesian inference to prevent on-off attacks. The trust calculation combines weighted direct trust and indirect trust. The attack identification window is defined according to the interaction between the trust and the trustee, and the vehicle's credibility is judged according to the number of switches before reaching the highest and lowest scores. If the vehicle exceeds the predetermined threshold, it is marked as malicious. The model is simulated in MATLAB.  
[PERSON] et al. [17] proposed a blockchain-based decentralized trust model based on Bayesian inference, taking into account the quality of direct interactions between vehicles. It is mainly targeted at new-user attacks, based on punishment strategy to prevent malicious vehicles from gaining higher trust points by frequently leaving and joining the network. Meanwhile, an incentive scheme is proposed to encourage cooperation between vehicles. The scheme performance is analyzed in MATLAB.  
In addition, the main calculation factors considered in the calculation are the weight quantization and threshold setting, that is, the determination of whether or not the vehicle is trustworthy according to the trust value calculated. The concept of weights is often applied when aggregating the trustee's final trust score, where different contribution parameters are assigned different weights according to their contribution/importance in the final trust value calculation. The determination of the exact value of these weights is essential. In [13], two weight calculation methods of three parameters, involving similarity, familiarity, and grouping delivery ratio are proposed, and the simulation verifies that the results produced by the weight calculation methods are more accurate than using the mean value directly.  
The threshold setting is designed to identify misbehaving entities in the IoV and detect malicious vehicles by adopting a preset stable threshold. In [31], the vehicle with a trust value greater than the threshold is a trusted vehicle, while a vehicle with a trust value lower than the threshold is a malicious vehicle. However, authors do not consider the dynamic characteristics of VANET. A fixed threshold cannot resist on-off attacks. In [32], [PERSON] et al. proposed the adaptive threshold technology to effectively reduce the on-off attack. However, the trust management model is quite computer intensive and has efficiency issues.