# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 6 Conclusions  
Aimed at the current security challenges of secure data interaction and reputation evaluation in VANET, we propose a secure reputation evaluation and management model  
Figure 8: Performance of three schemes under on-off attack. (**a**) TPR and (**b**) TNR.  
Figure 9: Performance of two schemes under collusive attack. (**a**) TPR and (**b**) TNR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vehicles**} & \multicolumn{2}{c}{**Trust Computation**} & \multirow{2}{*}{**Reputation Update**} & \multirow{2}{*}{**Consensus \({}^{**}\)**} \\  & **and Fusion \({}^{*}\)** & & \\ \hline
50 & 54.67 & 6.03 & 55.70 \\
100 & 102.88 & 7.54 & 104.39 \\
150 & 147.10 & 8.12 & 176.35 \\
200 & 237.45 & 8.09 & 245.18 \\ \hline \hline \end{tabular} \({}^{*}\)Results in the table are measured in ms. [\({}^{**}\)] The proportion of nodes selected to participate in the consensus is 33\(\%\).  
\end{table}
Table 3: Latency of the DBREMM.  
DBREMM, that is based on a double-layer blockchain, which can be applied to various scenarios in VANET. The DBREMM is designed to enable secure data interaction by quantifying reputation. Firstly, the blockchain is divided into two cooperative chains, namely the event chain and reputation chain. Event chain is used for safety verification, real-time emergency response, and after-action accountability of road events. Reputation chain is used for establishing a recessive vehicle reputation network to realize the sharing, storage, and query of vehicle historical reputation values and social measurement factors. Meanwhile, in order to resist malicious attacks and realize secure data interaction, a complete set of reputation update schemes is designed, including direct trust calculation based on the multi-factor metric Bayesian inference, indirect trust calculation based on historical reputation value, and secure trust fusion and reputation updates based on the number threshold and fluctuation factor. Finally, through an experimental analysis, we prove the feasibility, effectiveness, and anti-attack capability of our scheme.  
Meanwhile, due to the limitations of the author's ideas and experimental conditions, there are still many shortcomings in this paper. For example, (1) In the experiment, the sparsity of interactions of vehicles was not considered. In actual VANET scenarios, the architecture and distribution of nodes vary greatly in different geographical regions and times. Therefore, demonstration of the applicability of the DBREMM in different scenarios is needed in the future. (2) In our model, which is similar to those in most existing articles, the assumptions about RSU security have been diluted. In practical scenarios, RSUs may also be attacked. Designing a solution for RSU security and conducting experimental verification can further improve the security of VANET. (3) The research in this article mainly focuses on theoretical model design, and performance evaluation relies on simulation experiments. Therefore, the availability of the model in actual communication scenarios and the delay in transmission were not considered. The solutions of these limitations are the future research directions of the authors.  
Conceptualization, B.H., H.Z. and J.Y.; methodology, B.H.; software, B.H.; validation, B.H. and H.Z.; formal analysis, B.H. and Y.X.; writing--original draft preparation, B.H.; writing--review and editing, H.Z. and Y.Y.; supervision, Y.Y.; project administration, Y.X. and J.Y.; funding acquisition, Y.X. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research is funded by \" the National Key R&D Program of China\" (No. 2020 YFB1708600).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request.  
The author declared that there are no conflict of interest in this study.