# Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks
## 4 Test and Evaluation of the Results  
### Simulation Environment and Evaluation Metrics  
To carry out evaluations of the proposed protocols and extensions to previous work, we implemented the functionality in ndnSIM [8], which realizes the basic mechanisms of NDN in the network simulator ns-3 [9]. The ndnSIM implementation was extended to support the algorithms and data structures described in Section 3. To generate realistic scenarios for our evaluations, the Simulation of Urban Mobility (SUMO) [11] tool was used together with the freely exportable maps of cities available from OpenStreetMap (OSM) [10] to create different mobility traces. OSM contains road information that is generated and validated by satellite images and GPS (Global Positioning System) traces and is commonly regarded as the highest quality road data publicly available today. SUMO is an open-source traffic simulator with continuous space and discrete time. SUMO is capable of importing maps in multiple formats, including OSM. In our simulation, we use OSM to create the real-world map, and use SUMO to generate vehicle nodes and traffic, and mobility model in the real-world roadmaps. Table 1 shows the parameters of ndnSIM used in our simulations. Note that, after some experiments, we set the constant \(c\) in Equations (2)-(10), and \(m\) in Equation (3) to 8 ms (i.e., \(T_{m}\) = 7 ms), demarcation vehicular density \(d_{s}\) = \(10/\pi R^{2}\) (i.e., 10 neighboring vehicles in the radio transmission area of each vehicle, and we set the radio transmission range \(R\) to 300 m) and \(\lambda\) in Equation (6) to 5 s in the following simulations. The distance \(D_{m}\) related to the maximum velocity of the vehicles and the maximum tolerated latency for one hop is set to 250 m.  
Figure 11 illustrates the modules of a vehicular node in our implementation for processing data from the network. The geo-position information needed by the Interest and Data packets can be obtained from the GPS of the vehicles. The priorities of different types of content can be configured through the graphical user interface (GUI) during the simulation. Some performance parameters and curves can also be displayed in the GUI. The forwarding control algorithms and tables are implementations introduced in Section 3 and used together to process the packets from the network. Figure 10: Forwarding control of Data packet. The following metrics are used to evaluate the proposed method:  
* _Bandwidth usage_: The total amount of data (kilobytes) transmitted in the network per unit time (second). It also represents the total number of packets successfully transmitted in the network, which reflects the effectiveness of the redundant packet-forwarding suppression, greedy distance-based packet forwarding, and the reverse-path routing Data packet forwarding. * _Successful content delivery ratio_: The ratio of the number of Data packets a node obtains successfully to the number of Interest packet it sends to request the Data. For simplicity, in our tests, we did not segment the content requested through an Interest packet into multiple Data packets. * _Data response time_: The time between when an Interest packet is sent for requesting content data and the first Data packet is received. We use the average data response time of multiple requests at a certain density of vehicles to evaluate our method. * _Number of hops for data packets_: The number of hops that a Data packet has been forwarded in the network before it reaches the node that sent the Interest. We measure the above metrics when there are different numbers of vehicles distributed in the sections of roads randomly, and compare the metrics with that when using basic NDN as described in Section 2 (denoted as Basic-NDN below), V-NDN [20] as described in Section 2, and a GPSR [14] approach. Since GPRS is the classical routing protocol based on geographical position for an ad-hoc