# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 5 Simulation Results
### Performance Comparison between GARSUD and Other Existing Approaches  
Several schemes have been proposed in order to achieve an efficient RSU deployment in a given scenario, but most of them do not provide enough details to perform a fair comparison. In order to obtain relevant performance results, two well-known algorithms were chosen: (i) geographic deployment and (ii) D-RSU. The geographic approach [4] provides a uniform coverage of an area by maximizing the distance between RSUs. We slightly modified the geographic approach to place each RSU in main street crosses, facilitating the communication with vehicles. On the contrary,  
Figure 9: Evolution of warning notification time for each vehicle density: Madrid and Valencia scenarios. (**a**) Four RSUs: Madrid; (**b**) 9 RSUs: Madrid; (**c**) 4 RSUs: Valencia; (**d**) 9 RSUs: Valencia. the D-RSU scheme [4] requires previous knowledge of the distribution of vehicular traffic to perform an asymmetric deployment, assigning less RSUs to the areas with higher vehicle densities (where the wireless multi-hop communication is easier), while increasing the number of RSUs to cover areas with fewer vehicles where communication is often blocked by buildings, urban fixtures and other obstacles. We have compared, in both the Madrid and Valencia scenarios, the performance of our proposed GARSUD algorithm against these two existing schemes (i.e., geographic and D-RSU) in terms of warning notification time. Figure 10 depicts the different deployment schemes. As shown, the geographic distribution does not account for different traffic densities in different areas of the cities; it tries to increase the coverage area by deploying RSUs in a grid fashion. On the other hand, the D-RSU assigns a fixed number of RSUs to each subarea (i.e., downtown and outskirts) as a function of the expected vehicle densities. The D-RSU does not consider the street layout of each area to find the most suitable location for the available RSUs, but it applies a geographic mechanism to each individual subarea given the number of RSUs to be deployed. Regarding GARSUD, in order to better observe the effect of different traffic flows on RSU deployment and to further explore the flexibility of our proposal, Figures 10c,d and 11c,d show  
Figure 10: Comparison of the different RSU deployment policies for nine RSUs: Madrid scenario. (**a**) Geographic approach; (**b**) Density-based Road Side Unit deployment policy (D-RSU); (**c**) GARSUD with a central downtown; (**d**) GARSUD uniform traffic flow (i.e., without any downtown). the final positions of the nine RSUs, in Madrid and Valencia, computed by GARSUD in the two cases: (a) accounting for a central downtown area commonly observed in real cities and (b) a more uniform traffic distribution where no clear attraction points exist in the vicinity, a traffic pattern more likely to be found in the outskirts of cities and rural areas. Noticeable differences in the computed deployment can be detected in the results, since RSUs in Figure 10c are located mainly in the areas with low density where communication is often blocked by buildings, and incomplete paths between the sender and the RSUs can be found in a reasonable time. However, the distribution of RSUs is more compact in Figure 10d, where only a few selected locations in the surrounding areas are enough to reduce the warning notification time. This philosophy is the basis of the D-RSU deployment scheme where the RSUs are distributed inversely proportional to the vehicle density, but unlike GARSUD, the specific locations in each area are determined using geographic uniform distribution, not exploiting the street layout of the scenario. Figure 11 shows the same results for the Valencia map layout, and again, the distribution of RSUs computed by GARSUD is coherent with the principles followed by D-RSU, with more RSUs covering areas with low vehicle density where successful communications are harder to achieve, but the deployment obtained is far from following a geographic distribution.