## 5 Evaluation
### Consensus Mechanism Evaluation (PBFT)  
Evaluating the Practical Byzantine Fault Tolerance (PBFT) was crucial, as it ensures that the blockchain-based vehicular communication system operates securely and reliably even in the presence of malicious or faulty nodes. PBFT allows the network to reach consensus and validate transactions as long as fewer than a third of participating nodes are compromised. This makes PBFT ideal for environments where trust and data integrity are critical, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario** & **Description** & **Result** \\ \hline \multirow{5}{*}{Sybil attack} & A malicious node created 50 fake & 100\% success rate in detecting and \\  & identities to manipulate & blocking Sybil attacks, with a minimal \\  & vehicle-to-vehicle (V2V) & impact on latency \\  & communication & 95\% of malicious traffic was blocked, \\  & A malicious node flooded the network & 100\% of spoofed transactions were \\  & with 1000 fake transaction requests per & rejected, and throughput was reduced \\  & second to overload the RSUs and & by 20\% during the peak of the attack, \\  & blockchain nodes in & with no legitimate transaction delayed \\  & vehicle-to-infrastructure (V2I) & by more than 50 ms \\  & communication & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Security simulations and feedback. By testing PBFT in simulated scenarios with malicious nodes, we were able to evaluate the system's fault tolerance, verify its ability to maintain consensus stability, and ensure the system's robustness to attacks or failures. This evaluation was crucial in proving that the system is capable of securing vehicle communication networks while ensuring reliable transaction validation and network stability. * Faulty node-tolerance scenario-in a simulated scenario, faulty or malicious nodes were introduced into the network, with the number of faulty nodes gradually increasing from 0 to 5 out of a total of 10 nodes to simulate Byzantine failures. As a result, the system successfully reached a consensus with up to three faulty nodes (in accordance with the PBFT tolerance of up to f = 1/3 Byzantine nodes). When the number of faulty nodes increased to four or more, consensus could not be reached, and the network could not reach an agreement concerning transaction validation. * Fault tolerance-the system was able to tolerate up to 3 faulty nodes in a 10-node network while maintaining consensus and data integrity. * Impact on latency-With one to two faulty nodes, the consensus time increased by 10%. With three faulty nodes, the consensus time increased by 25%. When the number of faulty nodes reached four, the system could no longer agree on the validity of the transaction. The PBFT consensus algorithm worked as expected and successfully maintained a consensus with up to f = 1/3 faulty nodes. Beyond this limit, the network became unstable, as predicted through PBFT theory. * Malicious node injection scenario-malicious nodes attempted to participate in the consensus process by sending corrupted or false blocks to disrupt the network. PBFT successfully identified and excluded the malicious nodes during the preparation and handover phase. The corrupted blocks were discarded, and the network reached a consensus with valid blocks from honest nodes. * Mitigation rate-100% of malicious nodes were detected and excluded from the consensus process. * Impact on latency-the presence of malicious nodes increased the consensus time by 15%, but the system remained functional and stable. The PBFT consensus mechanism proved to be effective in identifying and excluding malicious nodes, ensuring network operability without interruption, results that we summarized in Table 2.