# A Novel Cloud Approach for Connected Vehicles
## 6 Conclusions  
In this paper, we have presented our approach based on a context cloud architecture formed by C-ITS stations. We have considered a Smart City environment for our study, but it can be easily adapted to any kind of environment: a county, a mall, a region, a part of a road, etc. We have used the ICN paradigm for data exchanges within the cloud. We have selected the ICN approach because it fits sensory vehicular networks (data-oriented, instead of host-oriented) and it eases mobility handling. We have included ICN interests and data within the standardized cooperative awareness messages (CAM), which means that our approach can be adopted in the currently deployed C-ITS without any modification of the standards. Additionally, we have used a C-ITS central station to be able to merge the data of different sensors if it is necessary and to add a sequence number, which allows avoiding sending non-updated data. We have compared our approach to two existing solutions, one push-oriented method (RENE) and another pull-oriented method (NDN), in terms of the network overhead and data freshness metrics. For this purpose, we have proposed a probabilistic model, which takes into account the mobility of the vehicles, the number and the random location of roadside units (RSU), and the message dissemination method from vehicle to vehicle. In \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Parameters** & **NDN** & **RENE** & **VCDN** & **VCDNoCAM** & **GeVCDN** \\ \hline \multirow{4}{*}{VCDNoCAM} & \multirow{4}{*}{VCDN} & Number of vehicles & Very High & Middle & High & Very Low & Low \\  & & Segment’s length & Very High & Low & High & Very Low & Middle \\  & & RSUs’ range & Very High & Middle & High & Very Low & Middle \\  & & Coverage area & Very High & Middle & High & Very Low & Low \\  & & Vehicles’ range & Very High & Low & High & Very Low & Low \\  & & Number of RSUs & Very High & Middle & High & Very Low & Very Low \\  & & Data update frequency & Very High & Low & High & Very Low & Very Low \\ \hline \multirow{4}{*}{VCDNoCAM} & \multirow{4}{*}{VCDN} & Number of vehicles & Bad & Bad & Bad & Bad & Very Good \\  & & RSUs’ range & Middle & Middle & Middle & Middle & Very Good \\  & & Vehicles’ range & Bad & Bad & Bad & Bad & Very Good \\  & & Number of RSUs & Middle & Middle & Middle & Middle & Good \\  & & Data update frequency & Bad & Bad & Bad & Bad & Middle \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Approaches’ Performances. order to confirm the results obtained from the analytic evaluation, we have developed a simulator that takes into account the standardized ETSI communication stack, the mobility model, and the ICN exchanges. The slight differences observed between the simulation and the numeric results (in general, less than 2%) can be explained by the time-discrete simulator limits and the assumptions made on the analytic model. Our results are promising, as one can see in Table 19, both network throughput and data freshness are improved. Furthermore, we have shown that our approach is more scalable than the other approaches since it has better results for a large amount of data, a high number of vehicles, and a high number of RSUs (less network overhead and better data freshness). Our future works will focus on testing our proposed approach in a real environment. Moreover, we intend to explore some machine learning methods to be implemented on the central station, which would be able to adapt its naming strategy in real time, based on different parameters, such as the number of sensors and the data update frequency. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This work was made possible by EC Grant No. INEA/CEF/TRAN/A2014/1042281 from the ENEA Agency for the SCOOP project. The statements made herein are solely the responsibility of the authors.