# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 5 Simulation Results
### Performance Comparison between GARSUD and Other Existing Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Vehicles** & **Vehicle** & **Geographic** & **D-RSU** & **GARSUD** \\ \hline \multirow{3}{*}{100} & \#1 & 2.861 s & 0.825 s & 0.826 s \\  & \#2 & 0.204 s & 0.833 s & 0.849 s \\  & \#3 & 10.898 s & 0.829 s & 0.852 s \\ \hline \multirow{3}{*}{200} & \#1 & - & 0.203 s & 0.231 s \\  & \#2 & 0.837 s & 0.876 s & 0.203 s \\  & \#3 & 0.211 s & 24.221 s & 0.241 s \\ \hline \multirow{3}{*}{300} & \#1 & - & 18.825 s & 0.906 s \\  & \#2 & 0.872 s & 10.206 s & 0.902 s \\ \cline{1-1}  & \#3 & 0.858 s & 6.869 s & 0.859 s \\ \hline \multirow{3}{*}{400} & \#1 & 0.225 s & 2.239 s & 0.223 s \\  & \#2 & 0.843 s & 0.866 s & 0.841 s \\ \cline{1-1}  & \#3 & 0.905 s & 0.214 s & 0.831 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Warning notification time (in seconds) for each warning vehicle when deploying 9 RSUs in Valencia considering a uniform traffic flow. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Vehicles** & **Vehicle** & **Geographic** & **D-RSU** & **GARSUD** \\ \hline \multirow{3}{*}{100} & \#1 & 0.834 s & 36.210 s & 0.833 s \\  & \#2 & - & 0.204 s & 0.839 s \\  & \#3 & 0.829 s & - & 0.839 s \\ \hline \multirow{3}{*}{200} & \#1 & - & - & 0.205 s \\  & \#1 & - & - & 0.205 s \\  & \#2 & 13.837 s & 0.840 s & 0.838 s \\  & \#3 & - & 0.221 s & 0.226 s \\ \hline \multirow{3}{*}{300} & \#1 & - & - & 0.252 s \\  & \#2 & 1.237 s & 0.845 s & 0.850 s \\  & \#3 & - & 0.860 s & 0.851 s \\ \hline \multirow{3}{*}{400} & \#1 & 0.287 s & 0.227 s & 0.238 s \\  & \#2 & 0.722 s & 0.848 s & 0.838 s \\ \cline{1-1}  & \#3 & - & 0.911 s & 0.912 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Warning notification time (in seconds) for each warning vehicle when deploying 4 RSUs in Valencia considering a uniform traffic flow. D-RSU, Density-based Road Side Unit deployment policy. even under high vehicle density conditions. This confirms that the traffic flow is a crucial part of the scenario and should be taken into account to achieve adequate RSU deployments. The amount of communications having failed using the D-RSU is more reduced, although several vehicles that are not able to report their information can be found when using four RSUs, as well. On the contrary, the deployments obtained with GARSUD allow communication for all the densities analyzed. In order to provide a comprehensive performance analysis of the GARSUD solution, we also performed the same study in the Madrid scenario. Tables 5 and 6 present the study applying the Krauss mobility model considering four and nine RSU deployments, respectively. As mentioned along the paper, the Madrid map layout presents a less complex scenario than Valencia; therefore, all vehicles were able to communicate and to transmit their messages under all RSU deployment policies. As observed in both tables, GARSUD always performs better than the other two solutions, allowing vehicles to transmit the warning notification messages faster. Figure 10 presents the final deployments for the Madrid scenario achieved by each RSU deployment policy (i.e., geographic, D-RSU and GARSUD). Overall, as demonstrated across the different sets of experiments, our approach outperforms others in a variety of scenarios such as different vehicular densities, map layouts and considering different mobility models.