# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 6 DRAMBR Experimental Evaluation
### Simulation Setup  
This section provides the detailed simulation setup for the three scenarios, including the configurations, parameters, and communication dynamics used to analyze the generated MRs. To simulate realistic vehicular movements, we start our experiment by importing a map and integrating it into the SUMO environment [48; 49]. We used OpenStreetMap to import a map from the rural area of Peak District [50; 51], as shown in Figure 13. In this step, we implemented the three scenarios to see how vehicles evaluate each other's behaviors and generate MRs based on observed misbehavior without connectivity. Every vehicle has the DSRC/WAVE (IEEE 802.11p) protocol, a common wireless communication interface. SUMO simulation generates floating car data (FCD) output, which captures vehicle-specific metrics such as position, speed, angle, and other details at each timestep. This raw data are collected and saved in an FCD file, which serves as the primary dataset for analyzing vehicular behaviors and identifying potential misbehavior events. The FCD file is subsequently processed to extract relevant information,  
Figure 13: Simulated map of peak district area. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Scenario** & \(\mathbf{Rep_{\text{veh}}}\) & **Msg Type** & \(\mathbf{I_{inc}}\) & **Message Content** & **Triggered MR** \\ \hline \multirow{2}{*}{ACD-OCC} & \multirow{2}{*}{Honest} & \multirow{2}{*}{Accurate} & \multirow{2}{*}{IR-101} & \"Accident detected at location X, proceed cautiously.\" & Reports malicious vehicles for denying the accident. \\ \hline \multirow{4}{*}{ACD-OCC} & \multirow{2}{*}{Malicious} & \multirow{2}{*}{Conflicting} & \multirow{2}{*}{IR-101} & \"No incident at location X, road is clear.\" & Reports honest vehicles for claiming an accident. \\ \hline \multirow{2}{*}{ACD-ABS} & \multirow{2}{*}{Honest} & \multirow{2}{*}{Accurate} & \multirow{2}{*}{IR-201} & \"No incident at location X, road clear.\" & Reports malicious vehicles for claiming an accident. \\ \hline \multirow{4}{*}{ACD-RSL} & \multirow{2}{*}{Honest} & \multirow{2}{*}{False Positive} & \multirow{2}{*}{IR-201} & \"Accident detected at location X, avoid area.\" & Reports honest vehicles for denying the accident. \\ \hline \multirow{2}{*}{ACD-RSL} & \multirow{2}{*}{Honest} & \multirow{2}{*}{Resolution Update} & \multirow{2}{*}{IR-301} & \"Incident resolved at X, road clear.\" & Reports malicious vehicles for claiming the incident persists. \\ \hline \multirow{4}{*}{ACD-RSL} & \multirow{2}{*}{Malicious} & \multirow{2}{*}{Conflicting} & \multirow{2}{*}{IR-301} & \"Incident still active at X, avoid area.\" & Reports honest vehicles for claiming resolution. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accident scenarios and triggered MRs.  
and the data are stored in a CSV file. This CSV file initially contains information about misbehavior reports, including attributes such as the reporter ID, timestamp, and location. The simulation parameters in this experiment are given in Table 9.  
In our simulation, the RV is set to 0.5 to align with established reputation standards, which balance the trade-off between maintaining system reliability and minimizing false positive rates [52]. In vehicular networks, vehicles with trust values higher than 0.5 may be considered trustworthy when forwarding data, while lower values indicate distrust. Similarly, temporal network studies have employed a 0.5 credibility threshold to evaluate trustworthiness effectively [53]. Although 0.5 is a standard choice, its value may be adjusted based on system requirements or empirical data [54]. The resulting MRs are organized into three files, each file is associated with a specific event: (ACD-OCC), (ACD-ABS), and (ACD-RSL). We filter the data in each file to remove redundancy, ensuring that only relevant and unique MRs are retained. Subsequently, we proceed with processing the MRs for further analysis by implementing the DRAMBR system following the steps illustrated in Table 10.