# A Novel Cloud Approach for Connected Vehicles
## 1 Introduction  
The deployment of cooperative intelligent transport systems (C-ITSs) started some years ago. They are critical for road safety. Therefore, they need to maintain high quality of service such as low latency, low packet loss, and low bandwidth consumption. For this purpose, one of the proposed strategies consists of submitting request packets in the network in order to collect named data; this mechanism is denoted as information-centric networking (ICN) [1]. When the latter is combined with the cloud approach, it provides the vehicular cloud networking (VCN) [2] paradigm. This paradigm helps to handle mobility thanks to different features (in-network caching, no session, etc.). However, we noticed that in a secure cloud environment, an overhead is generated in the network due to the security mechanisms. We recall that the bandwidth is 5.6 Mb/s for the IEEE 802.11 p MAC layer considered here.  
In this paper, we propose an approach, called vehicular central data networking (GeoVCDN), which takes advantage of the VCN and improves the network's performances in terms of network throughput and data freshness, compared to other well-known ICN solutions. To do so, our approach takes benefit from the application beacons sent by every C-ITS (vehicles, roadside units (RSU), pedestrians [3], etc.) between 1 and 10 times per second. In fact, instead of sending new specific packets, the ICN exchanges are inserted in the application beacons. This proposal allows us to reduce bandwidth consumption. To improve data freshness, we use multi-hop forwarding, which allows us to spread the data more efficiently over the network.  
In order to prove the efficiency of our approach, we have compared it with two other well-known solutions. The first one is the rendez-vous network (RENE), which is pushoriented. This means that a client will subscribe for named data and the data will be pushed as soon as it is available. The second one, denoted as named data networking (NDN), is pull-oriented. This means that a client will submit a request for named data and will receive the data as a response if it is available. These comparisons have been carried out through analytic evaluations based on a probabilistic model that we have proposed as well as extensive simulations. The latter has allowed us to confirm the analytic results. Results have shown that GeoVCDN improves some performance indicators, such as data freshness, while reducing the network overhead. Finally, we proposed a comparison between the different approaches regarding a use case: shortest pathfinding in a smart connected city.  
Our approach exploits standardized cooperative awareness messages (CAMs). Additionally, it merges the different sensor data into one set of metadata and includes a sequence number that is incremented whenever the data are updated. It implements a multi-hop feature to help extend the zone where the data are spread. All of these allow GeoVCDN to spread frequently updated data through the network without overloading the bandwidth. Moreover, we modeled our smart city as Manhattan topology and proposed an analytic model to estimate the network overhead and data freshness considering the mobility of vehicles. We compared all these approaches using a custom simulator.  
The remainder of this paper is organized as follows: Section 2 describes the related works on ICN, C-ITSs, vehicular cloud computing (VCC), and C-ITS standards. Section 3 details our approach's mechanism and architecture. Section 4 defines the probabilistic model that we proposed and gives the results for each approach in terms of the network overhead and data freshness. Section 5 describes our simulator, the results that we obtained for each approach, and the use case study. Finally, Section 6 concludes the paper.