## 5 Performance Evaluation  
The performance of our scheme was evaluated by examining its ability to recognize untrustworthy nodes and evict malicious nodes from the network. The main concern of our scheme is increasing the received ratio of accurate data and reducing the ratio of corrupted data. The simulation model was built using MatLab. The key performance measures of interest in the simulations are:  
1. Throughput is the average rate at which a message is delivered successfully via a communication link. 2. Spectrum utilization is the average amount of time the spectrum is kept busy. The utilization is calculated as follows: \[U=\frac{T_{u}}{T_{s}}\] (16)  
where \(T_{s}\) is the simulation duration, and \(T_{u}\) is the length of time the spectrum is kept busy. The findings were then averaged across a sufficient number of independent runs to ensure that the confidence level is at least 95% and that the relative errors do not exceed 5%. In this section, we analyzed the performance using a variety of parameter values. Table 1 shows the parameters used to evaluate the proposed scheme. The input parameters' values were selected to reflect a portion of the reality of wireless applications, such as phone call traffic. Malicious nodes keep sending false messages and altering some of them while forwarding a received message. Furthermore, some nodes reject relaying some messages. Our scheme tries to exclude such nodes. As shown in Figure 2, our security scheme (secured VANET, (SV)) produced better throughput outcomes in the simulations than a VANET without any security mechanisms (UV). The figure displays the effect of varying load on the reported throughput for the SV and the UV schemes. The figure shows that the proposed SV scheme consistently outperforms UV since it excludes malicious nodes from VANET and reduces their number to the lowest possible number. Therefore, the reported throughput for SV shifts to a higher level for different traffic values. Sometimes, malicious nodes reject forwarding packets. Fortunately, nodes that continue to send false warnings to keep the spectrum busy are excluded from the system using our scheme. Besides utilizing the recommendation from other nodes, the RSU uses its experience to evaluate untrustworthy nodes and evict them from VANET. The malicious node evection ratio is plotted in Figure 3. It can be observed that the evection ratio increases as the number of malicious nodes increases. The attackers might continue sending the RSU false reports to gain exclusive access to the spectrum and prevent other nodes from accessing it. The utilization of resources is realized under the premise of the preceding experimental scenario and trust model. Spectrum utilization results are displayed in Figure 4. Clearly, the utilization decreases significantly as the percentage of malicious nodes increases. Fortunately, the figure shows that our scheme effectively utilizes the spectrum even when the percentage of attackers increases considerably. The SV scheme evicts misbehaving nodes that result in inefficient spectrum usage. However, the UV scheme's utilization reduces considerably as the percentage of malicious nodes increases. Because it cannot cope with malicious nodes, the UV fails to make effective spectrum use. In Figure 5, the average delay is plotted against various percentages of attackers. It can be shown that as the number of malicious nodes increases, the average delay increases. Attackers send dummy messages to keep the spectrum busy and reserve VANET resources. As a result, users will be unable to access the spectrum, resulting in a considerable increase in delay. Untrustworthy nodes that refuse to relay or drop packets are ejected from VANET using our scheme. The results in Figure 5 demonstrate our scheme's ability to decrease the delay in various system settings. In addition, to reject false alarms from untrustworthy