## 5 Simulation Results
### Evaluating the Impact of the Radio Propagation Model  
Figure 3 shows the simulation results when varying the number of vehicles. We selected the TwoRay Ground, the Nakagami fading, and the RAV models. Table 4 shows some of the parameters used for the simulations; the rest of parameters are the following: the roadmap used is Rome, vehicles follow the Krauss mobility model, there are 3 warning mode vehicles, the periodicity of messages is 1 message per second, normal message priority is AC0, the broadcast scheme applied is eMDR, and the channel bandwidth is 6 Mbps. According to the \(2^{k}\) factorial analysis, the results show that the warning notification time is highly affected by the RPM used. When using the TRG model, information reaches 30% of the vehicles in less than \(1\) s, and propagation is completed in less than \(8\) s. When using the RAV model, the system needs \(2\) s to reach 30% of the vehicles, although the propagation process was completed in only \(2.5\) s.  
Table 7 shows the percentage of blind vehicles and the number of packets received per vehicle when varying the RPM. As shown, the behavior in terms of percentage of blind vehicles and the number of packets received also highly depends on this factor. In fact, when using TRG and Nakagami fading models, there are practically no blind vehicles, while we find \(60.92\%\) of blind vehicles when using RAV. Therefore, when the model is more realistic, more time is needed to reach the same percentage of vehicles, and thus the percentage of blind vehicles increases. This occurs because both TRG and Nakagami models are really optimistic, and they do not account for the presence of obstacles in signal \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Variation Explained (\%)**} \\ \cline{2-4}
**Factors** & _Warning Notification Time_ & _\% of Blind Vehicles_ & _Number of Packets Received_ \\ \hline \(DH\) & 0.07 & 0.13 & 1.16 \\ \(DI\) & 0.94 & 0.13 & 0.03 \\ \(EF\) & 0.00 & 0.00 & 0.00 \\ \(EG\) & 0.00 & 0.00 & 0.00 \\ \(EH\) & 0.00 & 0.00 & 0.00 \\ \(EI\) & 0.00 & 0.00 & 0.00 \\ \(FG\) & 0.00 & 0.00 & 0.00 \\ \(FH\) & 0.00 & 0.00 & 0.00 \\ \(FI\) & 0.00 & 0.00 & 0.00 \\ \(GH\) & 0.25 & 2.60 & 0.00 \\ \(GI\) & 0.94 & 1.17 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._propagation. Moreover, the average number of packets received per vehicle highly differs depending on the model (see Table 7). The number of packets received decreases considerably for RAV since signal propagation encounters more restrictions. In order to better understand the warning dissemination process, Figure 4 offers a heat map of the number of messages received in one of our simulations at different time instants. Each heat map was obtained by splitting the Rome scenario in a 100 \(\times\) 100 grid, meaning that each cell depicted represents 400 m\({}^{2}\) (20 m \(\times\) 20 m). Figure 4 shows the number of warning messages received in each area when using TRG and RAV radio propagation models, respectively. White areas indicate that no messages were received during the simulation (blind zones and buildings), whereas yellow areas represent locations where 5 or more messages were received. Yellow areas indicate more messages received and blue areas represent fewer messages. \begin{table}
\begin{tabular}{c c c} \hline \hline
**RPM** & **\% of Blind Vehicles** & **Packets Received** \\ \hline TRG & 0\% & 3,417.10 \\ Nakagami & 0.1\% & 1,291.10 \\ RAV & 60.92\% & 229.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Blind vehicles and packets received per vehicle when varying the Radio Propagation Model. Figure 3: Cumulative histogram for the time evolution of disseminated warning messages when varying the RPM used. When using the TRG model the dissemination process is able to reach a wider area of the scenario since the signal encounters no restrictions except the maximum transmission range.