# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 3 Simulation and Results
### Analysis of UAV Node Trust Evaluation Results  
Through simulation experiments, we evaluated the performance of a secondary blockchain-based drone cluster in terms of network performance. Here are some of our performance metrics and observations. (1) Malicious node judgment accuracy  
In order to analyze the effectiveness of the identification of malicious nodes in the experimental process, this paper adds the node result set to the malicious nodes in the UAV node data transmission. The scheme [29] and the Dynamic Evaluation Method for Drone Node Trust Based On Task Perception (DEMDT-TPT) in this paper are introduced to conduct a comparative test, we introduce the method proposed in scheme [29] as the benchmark. Through eight groups of simulated trust value evaluation rounds, the Bayesian inference model is used to judge the correctness of the results of ordinary nodes to malicious nodes, and five experiments are carried out in scenarios with different ratios of malicious nodes, and for the accuracy of the final judgment of malicious nodes, the average value is removed, and the comparison chart is shown in Figure 7. It can be concluded from Figure 7 that the scheme designed in this paper performs better, and can still maintain a high accuracy rate as the proportion of malicious nodes increases. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Number of clusters & 3 \\ Number of drones in each cluster & 10 \\ Bandwidth & 10 MHz \\ UAV transmission power & DssRate 11 MHz \\ Routing protocol & AODV \\ Mobile model & GroupMobilityModel \\ Loss model & FriisPropagationLossModel \\ Communication distance & 50 m \\ Initial trust value & 0.5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation initial setup. The scheme in this paper has the change in trust value caused by node behavior. To verify the anti-spoofing ability of the experimental scheme against malicious nodes, this paper counts the change rate of the trust value of UAV nodes and introduces the scheme [18] as the benchmark of this paper; the scheme [29] is an experiment on the rate of change of the trust value in the UAV field, which can better reflect the change in the trust value in general situations. Through ten groups of simulation experiments, the change rate of the latest trust value in each trust value evaluation round compared with the trust value of the previous round is recorded, and five simulation experiments for each group are conducted, and the average value of the trust value change rate after the team is taken, and the situation is compared in Figure 8.  
In the figure, the scheme in this paper has a lower trust value change rate in the period 0-6, because the interaction of the UAV is honest; in the period 6-12, the trust value change rate decreases, and for the UAV, the interaction of malicious nodes appears in the nodes. It can be seen that the trust value of this paper drops faster, and the punishment for malicious nodes is higher than the baseline scheme. After that, in the period 12-20, the UAV nodes begin to reply to the trust value, but in this paper, the recovery speed in the scheme is obviously slower, which can prevent malicious nodes from obtaining high trust  
Figure 8: Change rate of node trust value. Figure 7: Malicious node judgment success rate. values in a short period, and can significantly improve the anti-spoofing ability of malicious UAV nodes. Based on the collected experimental data, we conducted an in-depth analysis and discussion of the experimental results. In addition to paying attention to the performance of performance evaluation, packet loss rate, and routing performance, we pay more attention to the data of the correct rate of judging the malicious nodes of drones and the rate of change of the trust value of drone nodes. The following is a summary of some of the experimental results. First, in terms of performance evaluation, we evaluated the security performance of secondary blockchain-based UAV swarms through simulation experiments.