# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results
#### 5.1.2 Network Overhead Simulation Results  
We define the network overhead as being the data size that is shared between the different actors, over the G5 channel, on the whole network, and which is associated with the ICN exchanges. For the following figures, we will not show the NDN approach, because its results are on average hundred times higher than the other approaches, which makes it impossible to observe the differences between the other ones (as is shown in Figure 18). Vehicles play a core role in the G5 network overhead. They represent the biggest part in terms of stations, so it is important that the proposed ICN approach scales with a high number of clients. In this section, we will see how the number of vehicles and their communication range impact the network overhead for each approach. First, in Figure 18, we compare NDN and VCDN when we increase the number of vehicles. For NDN, the amount of data and exchanged interests proportionally increases with the number of vehicles in the network. Using VCDN, the number of interests also increases in the same way, but slower because there is only one interest per vehicle. On one hand, for NDN, the data are sent once per vehicle, and every time a vehicle asks, for VCDN the data are only sent when updated. On the other hand, VCDN generates a gain on the security layer. Indeed, for NDN, we have as many security headers as sensors, whereas for VCDN there is only one security header whatever the number of data is. We see that the mechanisms of metadata and sequence number make a significant difference by reducing \begin{table}
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**Component** & **Model** \\ \hline CPU & i7 @3.6 GHZ \\ RAM & 32 Go \\ OS & Windows 10 \(\times\)64 \\ GPU & Intel HD Graphics 630 \\ IDE & Eclipse neon 3 \\ Programming Language & Java 8 \\ \hline \hline \end{tabular}
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Table 8: Computer Configuration. Figure 18: Network Overhead—Number of Vehicles. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Designation** & **Description** & **Value** \\ \hline \(R_{C}\) & Junction range & 300 \\ \(P_{V}\) & Vehicle’s range & 200 \\ \(P_{RSU}\) & RSU’s range & 250 \\ \(D\) & Grid size & 4 \\ \(\alpha\) & Number of vehicles & 267 \\ \(\beta\) & Number of RSUs & 12 \\ \(\sigma\) & Data update frequency & \(1\) \\ \(\omega\) & Number of sensor & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Variables and default values. the network overhead related to ICN exchanges and making ICN more scalable. For 20,000 vehicles, NDN generates 1.2 GB/s of data, whereas for VCDN it only generates 12 MB/s. We will compare the behavior of each approach by changing the parameters associated with the vehicles (number and transmission range), the RSUs (number and transmission range), the network (size and coverage area), and the data (frequency, size, and the number of sensors). In Figure 19, we compare all the approaches excluding NDN. We already show that NDN is worse than VCDN and it appears that VCDN is the worst approach of the remaining ones, which that is why in the following figures we will not show NDN. Yet, the results will appear in the comparison tables. Using RENE, the vehicles send more interest than VCDN (one per sensor), but only the updated sensors send their data. For VCDN, the data of every sensor are sent when at least one sensor is updated. Because the data packet's size is higher than the interest's one, the performances of RENE are better than VCDN when the number of vehicles increases. The best performance is obtained for VCDNoCAM since the number of vehicles has no effect on the network overhead caused by the data sending. Using GeoVCDN, we add the exchanges between vehicles out of the coverage area and that is why GeoVCDN's overhead is higher than VCDNoCAM's. Still, despite this mechanism that increases the network overhead, GeoVCDN is more scalable than RENE.