# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 5 Simulation Results
### Performance Comparison between GARSUD and Other Existing Approaches
Figure 11: Comparison of the different RSU deployment policies for nine RSUs: Valencia scenario. (a) Geographic deployment; (b) D-RSU; (c) GARSUD with a central downtown; (d) GARSUD uniform traffic flow (i.e., without any downtown). Figure 12 presents the average warning notification time using the geographic deployment, the D-RSU and our GARSUD approach for both the Madrid and Valencia map layouts. These results confirm the potential of GARSUD, i.e., that our approach provides suitable solutions for different layout complexities, varying vehicle densities, as well as different mobility patterns. As demonstrated, GARSUD outperforms both the geographic and the D-RSU schemes, reducing the time required to notify safety authorities about dangerous situation, by testing the computed deployments against several realistic traffic patterns until the best result is found. Regarding Madrid (see Figure 12a,b), the improvement is particularly noticeable in low-density situations, where our GARSUD approach only requires 25% and 15% of the time needed by the other two RSU placement schemes, when deploying 4 and 9 RSUs, respectively. Notice that increasing the vehicular density over 400 vehicles (i.e., 100 vehicles/km\({}^{2}\)) in Madrid, the overall performance of the three schemes is reduced. This is due to high densities increasing the communication packets' contention and collisions in the shared transmission medium, forcing packet retransmission and delaying the overall dissemination system. Nevertheless, this effect is reduced by GARSUD (see Figure 12b) since it selects the most adequate location for the RSUs (e.g., RSUs are placed on the junctions where the average wireless traffic does not exceed the capacity of the channel). As for Valencia (see Figure 12c,d), GARSUD clearly outperforms both the geographic and D-RSU schemes in all the vehicle densities tested. Compared to Madrid, the warning notification time obtained is noticeably higher due to the increased complexity of the map, therefore creating blind areas where vehicles are isolated without access to RSUs. As an example, the average warning notification time in simulations involving 200 vehicles was below 5 s for all schemes when the scenario was the Madrid  
Figure 12: Warning notification time in Madrid and Valencia with a central downtown area when varying the density of vehicles. (**a**) Four RSUs: Madrid scenario; (**b**) 9 RSUs: Madrid scenario; (**c**) 4 RSUs: Valencia scenario; (**d**) 9 RSUs: Valencia scenario. layout, whereas this time ranges between 14.57 s for GARSUD and 38.28 s using the geographic deployment. However, the results obtained by our proposal are the most suitable in all the tested scenarios. As demonstrated, the RSU deployment determined by GARSUD is suitable under a wide range of vehicle densities, ranging from rush hours to off-peak traffic times. Regarding the performance of GARSUD in Valencia when using the Krauss mobility model to simulate a more uniform traffic distribution in the map, the probability of isolated clusters of vehicles unable to reach RSUs is increased, especially when the number of RSUs is too low to cover a significant portion of the area. Comparing warning notification time between approaches is complicated in this kind of complex environment since there are many situations where vehicles are not able to communicate with any available RSU during all the simulation time. To illustrate this condition, Tables 3 and 4 show the results of three warning mode vehicles in a given scenario using the three different RSU deployment policies studied, including GARSUD. As can be seen, those vehicles not being able to communicate with any available RSU are marked with a hyphen (-). The final deployments provided by each policy (i.e., geographic, D-RSU and GARSUD) were previously presented in Figure 11.  
As demonstrated, using a simple geographic distribution is highly ineffective under these especially adverse conditions. In fact, several vehicles are not able to reach any deployed RSU