## 5 Evaluation
### Simulated Network Dynamics  
To better understand the practical feasibility and performance of the proposed blockchain-based vehicular communication system, we conducted additional simulations incorporating real-world network dynamics. The focus was to evaluate the effects of vehicle mobility, communication jitter, and latency variability on system reliability and performance. These simulations allowed us to bridge the gap between idealized testing conditions and real-world scenarios and the results of this adjusted scenarios are showcased in Table 3.  
* Impact of vehicle mobility on communication latency-in real-world vehicular networks, the continuous movement of vehicles leads to dynamic changes in topology, which can introduce latency variations. Simulations were designed to account for the following:
* Vehicles entering and exiting the communication range of roadside units (RSUs). * Changing distances between vehicles and RSUs, affecting the signal strength and communication delay. Observations:
* As vehicles moved, latency increased slightly when transitioning between RSUs, averaging a 15% rise in the communication delay compared to static conditions. * The algorithm for selecting the nearest RSU (Algorithm 1 in the paper) effectively minimized disruptions, with vehicles reconnecting to optimal RSUs within 2-3 ms.
* The dynamic handoff process demonstrated that the framework could maintain a consistent data exchange without losing critical information, even during high-mobility scenarios. * Effects of jitter and latency variability on performance-jitter refers to the variability in packet transmission times, which is common in mobile vehicular networks due to fluctuating signal strength and environmental interference. To simulate this, random delays ranging from 5 to 50 ms were introduced into the communication paths between vehicles, RSUs, and blockchain nodes. Observations:
* For low to moderate jitter (up to 10 ms), the framework's performance remained stable, with Sybil attack detection rates exceeding 97%. The system handled the variability without noticeable degradation in its mitigation capabilities. * When jitter increased to 20 ms or more, there were slight delays in the consensus process, which affected transaction throughput. During peak jitter conditions, the throughput decreased by approximately 5%, although no malicious transactions were validated. * Severe jitter (above 40 ms) occasionally caused temporary delays in the system's attack detection capabilities, but blockchain consistency was preserved \begin{table}
\begin{tabular}{l l l} \hline
**Scenario** & **Description** & **Result** \\ \hline \multirow{4}{*}{Malicious node injection.} & Malicious nodes attempted to participate in consensus by sending corrupted or false & PBFT successfully identified and excluded 100\% of the malicious nodes during the preparation and handover phase. \\  & & sending corrupted or false & The corrupted blocks were discarded, and the network reached a consensus with valid blocks. \\ \hline \multirow{2}{*}{Impact on latency.} & The presence of malicious nodes & The Consensus time increased by 15\% due to the presence of malicious nodes, but the system remained stable and functional. \\  & & increased the consensus time. & The Consensus time increased by 15\% due to the presence of malicious nodes, but the system remained stable and functional. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._as malicious nodes were eventually identified and excluded during subsequent consensus rounds. The results of these dynamic simulations were compared with those obtained under idealized conditions in which all nodes had consistent connectivity and minimal delays. Resilience to mobility-the framework effectively managed vehicle handovers between RSUs without a loss of communication integrity. The rapid re-establishment of connections supports its robustness in high-mobility environments such as urban traffic scenarios.