## 4 Application of Blockchain Technology in the Internet of Vehicles
### POS Consensus Algorithm Improvement
Based on the judgment of _FAR_, _MDR_ and other indicators, the Internet of Vehicles' malicious node identification algorithm based on blockchain technology has a very significant false alarm rate and missing detection rate when identifying vehicle information. In this paper, _Dr_, _Ad_, _Or_ and other indicators were selected to reveal malicious node identification algorithm's performance in the Internet of Vehicles. In general, the computing system based on blockchain technology is feasible, and ITS network performance is superior to other algorithms. In the face of the increasingly prominent security needs and the complex diversity of malicious node attacks in the Internet of Vehicles, to further improve the detection of malicious nodes, the following perspectives could be the focus of future research work. First, the malicious node recognition algorithm model could be optimized to improve the system availability. The algorithm presented here is a result of the limited experimental conditions. A transportation service platform should be considered to evaluate the performance of this plan in real-world transportation networks. Second, the evaluation indicators should be \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of Malicious** \\ **Vehicle Nodes in the Network** \\ \end{tabular} } & \multicolumn{2}{c}{_Dr (\%)_} & \multicolumn{2}{c}{_Ad (s)_} & \multicolumn{2}{c}{_Or (KB/S)_} \\ \cline{2-7}  & **Low Flow** & **High Flow** & **Low Flow** & **High Flow** & **Low Flow** & **High Flow** \\ \cline{2-7}  & **Density** & **Density** & **Density** & **Density** & **Density** & **Density** \\ \hline \(10\)\% & 70.02 & 94.25 & 0.102 & 0.118 & 29.52 & 129.20 \\ \hline \(15\)\% & 69.52 & 90.21 & 0.161 & 0.156 & 40.20 & 134.56 \\ \hline \(20\)\% & 68.65 & 88.68 & 0.175 & 0.167 & 43.25 & 142.30 \\ \hline \(25\)\% & 67.25 & 86.98 & 0.201 & 0.210 & 50.20 & 149.52 \\ \hline \(30\)\% & 66.82 & 86.70 & 0.214 & 0.234 & 53.29 & 150.29 \\ \hline \(35\)\% & 66.30 & 84.02 & 0.226 & 0.301 & 58.63 & 167.98 \\ \hline \(40\)\% & 65.48 & 80.20 & 0.238 & 0.365 & 61.30 & 165.32 \\ \hline \(45\)\% & 65.00 & 78.68 & 0.262 & 0.402 & 67.05 & 172.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results of the D, A, and O. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Malicious Vehicle**} & \multicolumn{2}{c}{_FAR_} & \multicolumn{2}{c}{_MDR_} \\ \cline{2-5}  & **Low Flow** & **High Flow** & **Low Flow** & **High Flow** \\ \cline{2-5}  & **Density** & **Density** & **Density** & **Density** \\ \hline \(15\)\% & 0.156 & 0.112 & 0.100 & 0.105 \\ \hline \(25\)\% & 0.168 & 0.125 & 0.125 & 0.119 \\ \hline \(35\)\% & 0.171 & 0.135 & 0.129 & 0.120 \\ \hline \(45\)\% & 0.182 & 0.152 & 0.130 & 0.125 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The _FAR_ versus _MDR_ comparison results. increased to improve system comprehensiveness. The application of blockchain technology in car networking applications is relatively low at present. Although the application of blockchain technology in the field of car networking has shown a good effect in this paper, the article used in the network evaluation index is not comprehensive. In future, other security metrics will need to be performance evaluated to integrate blockchain technology more comprehensively with automotive networks. Third, traffic scenarios should be enriched to improve the robustness of the system. This blockchain, car networking, malicious-node-detection mechanism used a ONE simulation platform simulation experiment, looking at high-density and low-density scenario car scenarios to analyze the networking and malicious node identification algorithm performance. The malicious node detection performance of _FAR_ and _MDR_, for algorithm models _Dr_, _Ad_, _Or_ were also looked at, to conduct a comparative analysis of the algorithm performance.