# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.1 Configuration of Vehicular Mobility Scenarios  
The communication requirement of vehicular networks varies depending on the mobility scenario. Using the OpenStreetMap utility, we chose areas in Sri Lanka with maps with a size of 1500 m \(\times\) 1500 m in the municipal town of Colombo, 6000 m \(\times\) 6000 m in the Neluwa area, and an area the size of 4000 m \(\times\) 7500 m along the Southern expressway in the Dodangoda area to represent urban, non-urban, and autobahn vehicular traffic, respectively, as shown in Figure 5.  
Mobility traces for each scenario are generated using Simulation of Urban Mobility (SUMO) and OpenStreetMap. We configure the SUMO configuration file to add 200 vehicles (60 passenger cars, 60 trucks, 60 buses, and 20 motorcycles) for urban and non-urban scenarios, and 200 passenger cars for the autobahn mobility scenario, and generate mobility traces for each case by varying the maxSpeed parameter of all the vehicle types in increments of 10 kph (2.78 ms\({}^{-1}\)). The SUMO utility selects the maximum speed of a vehicle as the minimum of the maxSpeed parameter of the vehicle category and the speed limit of the particular lane. By increasing the maxSpeed parameter, we expect the average mobility to increase. However, the speed is not only constrained by the maximum allowable speed since many other factors, such as road signs and rules which the vehicle has to obey, the presence of other vehicles, intersections, obstacles, etc., governing the speed of a vehicle exist. In the Colombo municipal area (urban town), as shown in Figure 5a, most of the road  
Figure 5: Different vehicular network mobility scenarios. (**a**) Vehicular network in an urban area in Colombo; (**b**) Vehicular network in a non-urban area in Neluwa; (**c**) Vehicular network on the southern expressway in Dodangoda.  
speed limits are around 40-60 kph. Therefore, we vary the maximum speed parameter of the vehicles from 0-60 kph. However, for the Gelanigama expressway (autobahn scenario), we set the speed limit of the expressway to 250 kph to simulate high-speed mobility scenarios, thus we vary the maxSpeed parameter of the vehicles in the autobahn scenario from 0-250 kph. In the non-urban area of Neluwa, the speed limit of the roads is set to 100 kph such that the maxSpeed parameter of the vehicles is changed from 0-100 kph. Further, by modifying the SUMO configuration file, we set trips of the 200 vehicles to start at time \(t=0\), set the maximum number of vehicles in the simulation for 200, and set the simulation end time as 600 s. Furthermore, to prevent the vehicles from leaving the map during the course of the simulation, we reroute a given vehicle back to the source edge, upon reaching the destination edge of the map and vice versa. We had to set the maximum number of vehicles to 200, as simulations were run in a computer with eleventh generation Intel Core i7 11800H CPU with 16 GB RAM, where further increments of the number of nodes resulted in very long simulation times. Therefore, due to limited processing and memory resources, we could not simulate the high traffic density scenario of 3000-4000 vehicles per square kilometer. However, we have been able to represent a fairly moderate traffic density scenario, since we have spawned 200 vehicles in the given map. Using the SUMO configuration file and the trip files, the Tool Command Language (TCL) file is generated for the mobility scenario, which can be imported to the NS3 simulation, which represents real-world vehicular traffic. The number of vehicular nodes (\(N\)) can be varied in the NS3 simulation to any number less than 200 based on the requirement where the particular number of vehicular nodes (say \(\mathcal{X}\)) will extract mobility traces from the generated TCL file for the first \(\mathcal{X}\) nodes automatically.