# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results
#### 5.1.2 Network Overhead Simulation Results
In order to validate the proposed model, we present, in Table 10, a comparison between the expected values from the analytical model and the observed values from the simulations for 411 vehicles. We note a difference around of 1%, which confirms our model's behavior based on the number of vehicles. Furthermore, considering that the range of the vehicle is lower than the RSU's (which is a realistic consideration), this only has an impact on the GeoVCDN approach, because it is the only one which handles the V2V communications, as we can see in Figure 20. We have a smoother increase between 50 and 100, which refers to the phase when the inter-distance \begin{table}
\begin{tabular}{c c c c} \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 3,507,200.00 & 3,712,913.93 & +5.86\% \\ RENE & 27,021.88 & 27,453.20 & +1.5\% \\ VCDN & 252,934.88 & 254,684.26 & +0.69\% \\ VCDNoCAM & 14,596.58 & 14,678.86 & +0.56\% \\ GeoVCDN & 40,220.45 & 39,822.73 & \(-\)0.99\% \\ \hline \end{tabular}
\end{table} 
Table 10: Network Overhead Comparison for Number of Vehicles. Figure 19: Number of Vehicles. is almost sufficient enough to have all vehicles connected together. In this simulation, we had a 107-meter inter-distance. After this threshold, we observe a slow decrease, as the number of hops to cover a segment decreases. For a vehicle range of 150 m, in Table 11, the difference between the model and the simulation is very low (about 0.5%), except for the NDN. In an intelligent transport system network, RSUs are the gateway between the vehicles and the rest of the world. Increasing the number or the range of RSUs will increase the number of data access points. Thus, in most cases, the network overhead will increase. We will show how the network overhead is affected by the number or the quality of RSUs for each approach. Increasing the number of RSUs makes the coverage area bigger, which increases the number of vehicles concerned by the ICN messages. Furthermore, it also increases the number of entering points to the coverage area. The increase in the number of RSUs has a good impact on the GeoVCDN approach, as we can see in Figure 21. The more RSUs there are, the less there are exchanges between vehicles, since the RSUs already cover a part of the city. Additionally, we see that for RENE and VCDN, raising the number of RSUs leads to a significant increase in the overhead. For RENE, the overhead generated with interests is related to the number of entering points to the coverage area, whereas for VCDN it is about the coverage area itself. Concerning the data, with RENE and VCDN there are more vehicles to which the data must be delivered. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 5,126,400.00 & 5,422,778.06 & +6\% \\ RENE & 28,483.56 & 28,486.66 & +0.01\% \\ VCDN & 251,951.88 & 253,434.53 & +0.59\% \\ VCDNoCAM & 15,049.23 & 15,079.13 & +0.20\% \\ GeoVCDN & 31,614.90 & 31,383 & \(-0.73\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Network Overhead Comparison for Vehicles’ Range. Figure 21: Number of RSUs. Figure 20: Vehicles’ range. Table 12 lists a comparison between the expected values and the observed ones for five RSUs. For RENE, VCDN, and VCDNoCAM, the difference between the analytic model and the simulation's results is around 2% (Table 12). For the NDN approach, the simulation gives a result 7% higher, whereas for GeoVCDN, the result of the simulation is lower than the one given by the analytic model (7%). Furthermore, the RSUs' range modifies the coverage area and so the number of vehicles receiving the data (Figure 22). For the VCDN case, the consequence is the same as if we increase the number of vehicles: more interest and more data. For RENE, the coverage area does not affect the overhead related to the interests, which represents the biggest part of the overhead for this approach. However, we send the data to more vehicles, which is why we can observe a slight increase.