## 4 Application of Blockchain Technology in the Internet of Vehicles
### POS Consensus Algorithm Improvement
In the experiment, the number of the malicious vehicles that sent forged messages or forged messages in VANET ranged from 10% to 45%. When malicious vehicles exceed 45%, VANET's efficiency will dramatically \begin{table}
\begin{tabular}{c c} \hline \hline
**The Parameter Name** & **Parameter Values** \\ \hline Simulation Scene Range (m\({}^{2}\)) & 4500 \(\times\) 3400 \\ \hline Simulation Time (s) & 0–43,200 \\ \hline Vehicle mobility model & Shortest path movement model \\ \hline Vehicle node type grouping (group) & 11 \\ \hline Total number of vehicle nodes (individual) & 200–600 \\ \hline Vehicle communication range (m) & 10 \\ \hline Node speed (m/s) & 2 \\ \hline RSU quantity (s) & 10 \\ \hline Vehicle Cache Size (M) & 40 \\ \hline Packet lifecycle (min) & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental simulation parameters are set. Figure 4: City map of Helsinki, Finland. decline, and it will be unable to provide any reliable services. Therefore, this extreme case is not considered in this paper. The locations of all nodes are shown in Figure 5.

To smooth the simulation experiment, some necessary assumptions in the theoretical algorithm and simulation experiments are presented as the basis of the proposed scheme. * Suppose that an attacker cannot attack more than half of the vehicles in the network. * Authorities and the RSU are equipped with customized hardware with high computing abilities. * Certification bodies and RSU are equipped with custom hardware with a much higher computing power than general computers. * As long as the public or private keys are not stolen, encryption technology can be used to provide secure communication channels between entities. ### Simulation Results

To objectively and effectively analyze the malicious node detection mechanism algorithm based on blockchain, the simulation experiment is divided into two groups, looking at the number of vehicles for low-flow-density and high-flow-density scenarios. The results of each experiment are the average of the data obtained from 20 simulations. The simulation experiment takes the proportion of malicious vehicle nodes in the network as the independent variable and analyzes the algorithm's performance under low and high traffic flow densities. The experimental simulation results are as follows (See Table 3). Overall, high-density scenarios outperform low-density scenarios using this algorithm. The proposed algorithm has a low _FAR_ at both high and low densities. This is because this algorithm uses the blockchain to jointly verify the legitimacy of the sending nodes, determine the integrity and reliability of the VANET transmission messages, and greatly improve the identification rate of malicious messages. _Dr_, _Ad_ and _Or_ were used to evaluate and analyze the performance, and each experiment was the average of the data obtained from 20 simulations. The simulation experiment

Figure 5: The location of all nodes. also takes the proportion of all vehicles of malicious vehicle nodes in the network as the independent variable and analyzes the algorithm's performance in the case of low traffic density and high flow density, respectively. The experimental simulation results are as follows (See Table 4). In general, comprehensive experimental performance analysis can conclude with an increase in experimental simulation time and the gradual increase of malicious nodes in the network. If faced with the experimental simulation time and the high density of malicious vehicle nodes, the above algorithm can still play a significant role, and it has been verified by many scholars. ## 6 Conclusions

After a series of performance tests and verification of the results of the malicious node identification algorithm based on blockchain technology, the algorithm proposed in this paper can identify vehicle information in either low- or high-density vehicle scenarios.