# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Use Case Simulation Study  
In this part, we propose to apply our data exchange method, in a context cloud, to the pathfinding problem.  
The used simulator is the same as the one presented in Figure 1: a city such as Manhattan and the vehicles are uniformly spread. First, we place traffic lights and RSU on five fixed junctions. A traffic light can be in two states: quick or slow. A quick state means that it will switch between red and green every 5 s and a slow state means that it will switch every 15 s. Then, we randomly select one or several junctions that will also be equipped with RSUs. At the beginning of the simulation, we insert three additional vehicles. One uses the NDN approach, one uses RENE, and the last vehicle uses GeoVCDN. In their initial state, each vehicle has the knowledge of the configuration topology and the state of all the traffic lights in the city and, then, they must obtain the same result for their pathfinding algorithm. After 2 s, every traffic light toggles its state, switching from quick to slow or from slow to quick which has for effect to change the shortest path. An ICN message is emitted to inform about this change. When the change occurs, if the vehicles are in the range of an RSU, they will receive the information and can adapt their paths. If they are not in the range, a vehicle with NDN will receive the information as soon as it will be in the range of an RSU and a vehicle with GeoVCDN will receive the information from other vehicles. However, a vehicle with RENE will not receive it since it is based on push mode.  
Figure 29 is extracted from the simulator and represents a junction. An RSU is placed at the center and a traffic light is placed on each entry. The traffic lights on the line are synchronized. The red circle represents the transmission range of the RSU. The yellow square represents a vehicle with GeoVCDN.  
First, we randomly place RSUs in addition to the already fixed five. In the first configuration, only one RSU is randomly placed. In the second, two RSUs are randomly placed, and then up to eight for the third configuration. Finally, for the fourth one, an RSU is placed at every junction. For every configuration, we launched 100 simulations (the location of the RSU changes at each run). In the end, we measure the time needed for a vehicle to cross the city.  
Figure 30 presents the results for the first configuration. The vehicle using GeoVCDN spends 2989 s on the road, whereas the other vehicles (with NDN or RENE) spent 3566 s, which is an improvement of 17% for the GeoVCDN approach.  
Figure 29: Junction configuration.  
Figure 31a-c show the results for other configurations. We observe that the more RSUs there are, the more the results are closer. With RSUs placed everywhere, there is only a 1 or 2% difference. With few RSUs, the initial pathfinding had the effect of dodging the traffic light to gain time. Ironically, by dodging it, the vehicles also made it impossible for them to receive the updated information. By increasing the number of RSUs, we also increase the probability for a vehicle with NDN or RENE to receive the information, because it becomes more and more complicated to dodge every traffic light.