# Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks
## 4 Test and Evaluation of the Results
### Evaluation Results  
Note that in Figures 13-17, only for our method (denoted as CA-VNDN), values under different suppression angles are tested and plotted. For Basic-NDN, V-NDN, and GPSR, values are plotted at the point when the optimal suppression angle is achieved. In addition, the simulation time is 60 s and every 0.1 s a randomly selected vehicle will send an Interest packet requesting a type of content following the Zipf distribution. Figure 12: Map for the simulations. Figure 13: Bandwidth usage with the number of nodes (second line of \(x\)-axis) and suppression angle (degree, first line of \(x\)-axis). Figure 13 shows the bandwidth usage varies with the number of nodes on the road and the suppression angle. With the increase of the number of nodes, more vehicles take part in the data transmission, thus the bandwidth usage increases. Compared with Basic-NDN and V-NDN, our method has a lower bandwidth usage when the total number of content requests are the same, which means for the same number of data requests (and higher successful data delivery ratio, as shown in Figure 14), the total amount of data transmitted in the networks is less. This saves the network bandwidth and reduces data interference and congestion in the network, which also decreases the packet loss rate. GPSR causes least amount data in the network. This is because only one optimum node is selected for forwarding data in the intermediate nodes. However, the links are often disconnected when using GPSR, and the data delivery ratio is relatively low, as shown in Figure 14.  
Figure 16: Average data response time vs. the number of nodes and suppression angle. Figure 14: Successful content delivery ratio as a function of the number of nodes and suppression angle. Figure 15: Successful content delivery rate vs. the optimal suppression angle. Figure 14 illustrates the successful content delivery ratio. Here we can see that the highest successful content delivery ratio can be achieved by using CA-VNDN. This is because there are fewer packets transmitted in the network when packet suppression mechanisms are used, which reduces the interference and congestion at the physical, MAC (Media Access Control), and network layers. Also, the transmission mechanism of Interest and Data packets not only reduced the number of packets transmitted in the network but also guaranteed the successful delivery of Data packets. In addition, the successful delivery rate of our method is better than that of V-NDN. Especially, when the density of the vehicles is low, the difference is slightly bigger. This is because our method did not severely suppress the packet forwarding when the density of vehicles was low. Moreover, the optimal value of the delivery ratio happens under different values when the density of the vehicles are different. This can be seen more clearly in Figure 15.  
Figure 15 shows the optimal suppression angle when there are different numbers of vehicles on the road (shown in colorful lines) when the maximum successful delivery ratio is obtained. Here we can see that when there are fewer vehicles on the road (e.g., less than 200), the suppression angle does not play a role. This is because many of the vehicles are involved in the packet forwarding in order to achieve a high successful content delivery ratio. However, when the density of vehicles become bigger, the suppression angle plays an important role in achieving a higher data delivery ratio as, otherwise, the high node density can cause much contention in the wireless spectrum. Figure 16 illustrates the average data response time related to the suppression angle and the corresponding density of vehicles. From here we can see that our method has a similar curve to V-NDN, which does not fluctuate as heavily under different densities like the Basic-NDN does. Moreover, under a certain suppression angle, the response time of our method is much lower than V-NDN, which also shows that the suppression angle works well. In addition, when the density is low, our method performs better than V-NDN.