# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results
#### 5.1.3 Data Freshness Simulation Results
The comparison between the expected values and the observed values for 267 vehicles are listed in Table 16.  
These results confirm the validity of the analytic model when we make the number of vehicles change. In this scenario, about a quarter of the segments are not covered by any RSU, which is why the analytic model gave such results. Increasing the range of the RSUs means increasing the number of vehicles within their ranges. In Figure 26, we observe that the consequence is the same for all the approaches with better performance for GeoVCDN. The more we increase the range of the RSUs, the more we reduce the hops that the messages have to do using the V2V exchanges. We note that for the GeoVCDN approach, in the end, the freshness reaches 100%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 42.75\% & 42\% & \(-\)0.75 pt \\ RENE & 41.67\% & 41\% & \(-\)0.67 pt \\ VCDN & 42.75\% & 42\% & \(-\)0.75 pt \\ VCDNoCAM & 42.75\% & 42\% & \(-\)0.75 pt \\ GeoVCDN & 76.67\% & 91\% & +24.33 pt \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Data Freshness Comparison for Number of Vehicles. Figure 26: RSUs’ Range. Figure 25: Number of Vehicles. A comparison between the expected values and the observed ones for an RSU transmission range of 100 m is presented in Table 17.  
For the approaches NDN, RENE, VCDN, and VCDNoCAM, the difference in the results is almost null. In Figure 27, we see that increasing the number of RSUs has a positive effect on the freshness of all the approaches. It increases the number of entering points of the coverage area, which increases the number of vehicles being in it when the data are generated. For GeoVCDN, increasing the number of RSUs means increasing the number of starting points for the propagation of the data in V2V, which explains that the curve increases quicker than the other approach For GeoVCDN, the more RSUs there are, the more vehicles are able to forward the data. This is why we have a logarithmic curve in Figure 27. A comparison between the expected values and the observed ones is given in Table 18.  
Again, since almost half of the segments do not have any RSU, the analytic model gave a worse result than the simulation for GeoVCDN. By increasing the data generation frequency, we decrease the hop limit and so the number of vehicles, which can be affected by the V2V communications (GeoVCDN). For VCDN, NDN, and VCDNoCAM, we observe, in Figure 27, a slight loss, caused by the decrease in the number of vehicles entering the coverage between two generations. For \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 26.63\% & 26\% & \(-\)0.63 pt \\ RENE & 25\% & 25\% & 0 pt \\ VCDN & 26.63\% & 26\% & \(-\)0.63 pt \\ VCDNoCAM & 26.63\% & 39\% & \(-\)0.63 pt \\ GeoVCDN & 95\% & 98\% & +3 pt \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Data Freshness Comparison for RSUs’ Range. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Expected Value** & **Observed Value** & **Difference** \\ \hline NDN & 26.72\% & 27\% & +0.28 pt \\ RENE & 25.04\% & 26\% & \(-\)0.4 pt \\ VCDN & 26.72\% & 27\% & \(-\)0.28 pt \\ VCDNoCAM & 26.72\% & 27\% & \(-\)0.28 pt \\ GeoVCDN & 54.17\% & 74\% & +19.83 pt \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Data Freshness Comparison for Number of RSUs. Figure 27: Number of RSUs. RENE, whatever the frequency is, the number of vehicles inside the coverage is constant, so the freshness also remains constant. Finally, the more often data are sent, the less time the vehicles have to forward them. That is why, as we can see in Figure 28, the freshness decreases only with the GeoVCDN approach. For RENE, the frequency does not have any effect. For the others, the effect is almost null, there are fewer vehicles entering the coverage area before the expiration of the data and that is why we observe a slight loss.