# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 3 Simulation Results and Discussion
### Results  
Initially, we are interested in the efficiency of the proposed BC-TMF. Figures 4 and 5 show the average global trust metric (TM) of non-malicious vehicles having PM = 0 as a function of addBlockTimer for an entry rate of 2 vehicles/s and 4 vehicles/s, respectively. Figure 3: Network Map. It is obvious from Figures 4 and 5 that the average trust metric of non-malicious vehicles varies between 0.92 and 0.95 for both scenarios. Besides, it is obvious that it is slightly higher in the case of 50 km/h compared to the case of 65 km/h. This result is due to the fact that the high speed causes an intermittent connection between the vehicles, \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation period & 1200 s \\ MAC/PHY & IEEE 802.11p \\ PM & (0–1) \\ Number of Miners & 50\% of the total number of vehicles \\ Percentage of Malicious vehicles & 15\% \\ Maximum speed & 50, 65 km/h \\ Transmission range & 350 m \\ SendTransTimer & 50, 100 s \\ AddBlockTimer & (150–350) s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Set up. Figure 4: The average trust metric of non-malicious vehicles as a function of addBlockTimer(s), entry rate = 2 vehicles/s. Figure 5: The average trust metric of non-malicious vehicles as a function of addBlockTimer(s), entry rate = 4 vehicles/s. consequently, the monitors may not receive a sufficient number of alert messages from the target vehicles to increase their trust metrics. Furthermore, few vehicles did not reach a trust metric equal to 1 and this is maybe because they have recently joined the network, so they need more time to coordinate with their monitors and increase their trust metrics. Furthermore, we observe that addBlockTimer does not affect the trust metrics of non-malicious. Figures 6 and 7 portray the average trust metric of malicious vehicles as a function of the PM for different speeds and addBlockTimer values. On one hand, we notice that in all the considered scenarios the average trust metric decreases inversely to the PM. This is an expected result because the PM impacts the behavior of the vehicles in terms of the percentage of non-authentic messages disseminated in the network. The more PM increases, the more the authenticity of the detected events is altered. Consequently, when the percentage of non-authentic messages increases, the trust metric of the source vehicle decreases. More importantly, although only 10% of the messages sent by malicious vehicles are non-authentic in the case of PM = 0.1, it is obvious that their average trust metric does not exceed 0.3 for all scenarios, which confirms the efficiency of the proposed BC-TMF in calculating the trust metrics of the vehicles. It does not allow malicious vehicles to be trustworthy (TM = 1). Besides Figures 6 and 7 point out the importance of the sendTansTimer parameter on the convergence of the global TM of the malicious vehicles to lower values. Particularly, we remark that the average TM slightly decreases when sendTransTimer increases mainly for high PM values (>0.5). We notice in Figures 6 and 7 that the average TM slightly decreases also when addBlockTimer increases for high PM values. It passes from 0.25 in case of addBlockTimer = 150 s to 0.19 in case of addBlockTimer = 350 s, for PM = 0.7 and sendTransTimer = 50 s. This result provides enough evidence that addBlockTimer and SendTransTimer must be accurately tuned to obtain exact trust metrics and ensure that the global trust metrics of malicious vehicles converge rapidly to lower values. To emphasize the accuracy of the proposed BC-TMF compared to existing frameworks, we portray in Figure 8, the average global trust metric of malicious vehicles as a function of the percentage of malicious vehicles in the network. The average trust metric computed by the proposed BC-TMF is compared to that computed by NBC-TMV framework proposed in [22]. For both scenarios PM is equal to 0.7.