## 4 Simulations and Results
### Description of Simulation Scenarios  
The simulation scenario consists of a multi-hop VANET, where we analyzed the performance of the channel modeling techniques described in Section 3. We used the Multi-Metric Map aware (MMMR)routing protocol [11], which is based on Greedy Perimeter Stateless Routing (GPSR) [16]. MMMR is a delay-tolerant protocol that improves the next forwarding node decision by employing four metrics; the distance to the destination, the vehicle density, the vehicle trajectory and the available bandwidth. This multi-metric parameter is obtained by each node, and it is used to find the neighboring node that is the candidate for being the next forwarding node. The scheme is self-configuring and able to adapt to the changing vehicle density in real time. We carried out several simulations using the Estinet Network Simulator and Emulator [17]. Estinet includes the standard IEEE 802.11p and a simple and accurate way of designing VANET realistic scenarios. We used two real city areas of 1.5 and 2 km\({}^{2}\), obtained from the example district of Barcelona and the downtown district of Tarragona (see Figure 7), to evaluate two urban areas formed by streets with different densities of crossroads and buildings. In our scenarios, the Barcelona area has almost 1.3-times per square kilometer more streets and junctions than the Tarragona area. Furthermore, The example district of Barcelona has a high density of buildings compared to downtown Tarragona, specifically around four-times higher for the selected areas. Seeking to simulate a realistic scenario, the mobility model was obtained from CityMob for Roadmaps (C4R) [19], which is a mobility generator that uses the Simulation of Urban MObility (SUMO)engine [20]. C4R is able to import maps directly from OpenStreetMap [6] and to generate Network Simulator 2 (NS-2) compatible traces. C4R considers random origins and destinations for each vehicle. These points are located with higher probability in areas specified by the user. The path for a specific start and end point is computed through [PERSON]'s algorithm in a directed graph (as a GPS-based navigation system computes a route). SUMO provides a realistic driver behavior in the route followed by a car during a movement simulation. We exported the NS-2 traces to Estinet, including the building information (orange lines, see Figure 7) using our own translating software, available at www.lfurquiza.com/research/estinet [18]. The scenarios also consist of fixed nodes, which are henceforth called access points or APs. The AP enables the connection, directly or by using multiple hops, to the services in the network. The Barcelona scenario only includes one AP (see Figure 7c), while the Tarragona area has six access points (see Figure 7d). We considered four vehicle densities of 67, 100, 133 and 167 cars per km\({}^{2}\). Each of these densities could represent different situations of a day; for instance, early morning, night, morning/afternoon and rush hour. The objective of using four arbitrary different densities is to test if the difference among the results coming from the models depends on the density of the scenario. A high vehicle density helps to avoid discarding packets, since a suitable next forwarding hop would surely be always available; however, data transmissions would be more prone to interference. Each node during the simulations sends 1000-byte packets to the destination APs, during 300 s. In the case of Barcelona, the inter-packet time follows a uniform distribution between 2 and 6 s that has a mean of 4 s. On the other side, in the Tarragona scenario, we consider that this time is exponentially distributed with a mean of 4 s, but truncated between 1 and 10 s.  
All of the results are presented with confidence intervals (CI) of 95%, obtained from ten simulations per each density value and attenuation model using ten different movement traces. This means that we generated ten different movement traces per density and scenario. We use each of them to evaluate the three attenuation models.