## 4 Simulation of NDN Transport in V2X Scenarios
### Considerations for a V2X Simulation
However, there are currently no freely (open source) available implementations which are directly integrated into ns-3. Besides the physical models, one needs also a medium access and a transport protocol for simulating V2X communication. Ns-3 does support the IEEE 802.11p medium access [47] which wasdesigned for vehicle-to-vehicle communication. Furthermore, ns-3 also supports wireless access in vehicular environments (WAVE) [72] as transport mechanism. WAVE builds upon IEEE 802.11p and provides services like the communication of vehicle safety information. However, the 5-th generation (5G) of mobile networks does aim on providing new medium access control mechanism for V2X communication in order to enhance its performance in different ways (e.g., enhanced mobile broadband, massive machine type communication, and ultra reliable and low latency communication). In order to show how one can obtain completely different results when evaluating forwarding strategies for V2X scenarios using/misusing different propagation models, we provide a small preliminary simulation campaign. We use the ns-3 and ndnSIM to build our simulation. Regarding the scenario, we use an intersection with 19 cars approaching from all sides and turning left or right as depicted in Figure 5. The cars do vary their speed by accelerating by 2.6/1.6/3.5/4.6/5.0\(\frac{m}{s^{2}}\) up to a maximum of 14/16/17/20\(\frac{m}{s}\) and decelerating by 1.0/2.0/2.5/3.0/4.5\(\frac{m}{s^{2}}\). For the medium access control we use the IEEE 802.11p with a OFDM rate of 6 Mbps and a transmit power of 14 dBm [73]. Each car is also an NDN node and the receiver issues Interests for requesting data with 2 Mbps. For this mobile scenario, we select Stochastic Adaptive Forwarding (SAF) as forwarding strategy, because it is able to discover new paths and does not rely on a priori routing information. Furthermore, the reference implementation is freely available [12]. For the delay model we select the constant speed model available in ns-3. We use SUMO to generate the mobility traces for the 19 different cars, thus providing a scenario where we can reproduce results. For each propagation model we repeat the simulation twenty times and measured the interest satisfaction ratio and the delay of satisfied Interests. The results of this preliminary simulation campaign are summarized in Figures 6 and 7. The satisfaction ratio (cf. Figure 6) already indicates that different models provide different results when applied to the same scenario. Even more insights are gained by investigation of the CDF of the delay of satisfied Interests (cf. Figure 7). The CDF of the delay indicates that the various propagation models lead to different results. The results especially differ when receiver and transmitter are far  
Figure 5: Scenario for the simulation campaign. Receiver (RX) and transmitter (TX) are approaching the middle of an intersection with other vehicles. away of each other, such that the other vehicles are used as _relaying_ units by the forwarding strategy (reflected by the satisfied Interests with higher delay). For instance with Nakagami fading we obtained the lowest satisfaction ratio but the best performance with respect to delay. Conversely, the satisfaction ratio with the LogDistance model was one of the highest but scores as one of the lowest when it comes to delay (such as the TwoRayGround model). Thus, a meaningful conclusion cannot be drawn from the simulation with respect to the performance of SAF in the used scenario. Thus, optimizing parameters of SAF (such as interval length/numbers of Interests defining an calculation interval or other parameters) does not pay off as each of the used models will lead to different parameters. Figure 6: Satisfaction ratio for different propagation models using SAF. Figure 7: Cumulative Distribution Function (CDF) for satisfied Interests over time and a specific run (same run for all models).