# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Simulation Environment  
In order to investigate the efficiency of our solution, we implemented and compared the OLSR-PSO [24], UM-OLSR [42], OLSR-Gomez [24], and OLSR RFC-3626 [25] protocols within the Network Simulator. For more accurate vehicular mobility patterns, this work uses the real map tool to define realistic urban environment scenarios using the freely available OpenStreetMap [39]. In particular, we use Simulation of Urban Mobility (SUMO) [40] as a tool uses to generate realistic VANETs simulations, where the nodes follow the behavior of vehicles in a road. This tool extract the most common information form the open street map such as road edges, road direction, traffic signs, road conditions, traffic lights and constraints [43]. In our simulations, we also consider a wide range of traffic densities according to the specific mobility data models proposed in [22], which performs 10 scenarios for each group of cars moving randomly in different directions through an area of 1400 m \(\times\) 1200 m within three minutes as in Table 3. The movement mobility and number of cars are proportional to their corresponding selected area as suggested in [22].  
The vehicles move through urban roads at varying speeds of 10-50 km/h. The transmission power range is adjusted to 250 m according to the characteristics of the network system (IEEE 802.11p) interface card [44]. The simulation is performed using an i7 processor (6 cores, 15 MB cache 3.8 GHz), 8 GB Quad channel DDR3 RAM, 2 TB hard drive, and Linux Fedora core 16. For each run, there are fixed data rates that transmit 4 packets per second. Each vehicle sends a packet in 512 bytes size. This means the vehicles exchange the data packets by the constant bit rate (CBR). Table 3 summarizes the simulation phase parameters.  
Figure 5: Conversion the Realistic Road Map to Traffic Mobility.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Symbol** \\ \hline Simulation area & 1400\(\times\)1200 m\({}^{2}\) \\ Number of Scenarios & 10 \\ Number of vehicles & 10, 15, 20, 25, 30, 35, 40 \\ Vehicle speed & 10–50 km/h \\ Transmutation range & 250 m \\ Channel type & Wireless \\ MAC layer type & IEEE 802.11p \\ Radio-propagation model & Nakagami \\ Routing layer & OLSR \\ Channel bandwidth & 6 Mbps \\ CBR Packet Size & 512 bytes \\ CBR Data Rate & 33, 66, 100, 333, 666, 1000 kbps \\ CBR time & 60 s \\ Simulation time & 180 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters Settings.  
In this simulation, we performed 40 independent runs of every scenario in order to ensure the accurate values of the average communication cost. This cost should be as close as possible to real world environment, where the quality of cost is evaluated using six network data loads (33, 66, 100, 333, 666, 1000 kbps) in this area. Table 4 summarizes the main parameters of the PSO algorithm that can be chosen. In order to increase the efficiency of the PSO algorithm in the search space, the particle population and the number of iterations are performed according to [19]. Furthermore, the random search algorithm (RAND) [45] is implemented to compare the efficiency of the proposed parameters. Several metrics can be used to evaluate and compare the BOLSR-PSO performance. These metrics correctly describe the average change in a response variable produced by a change in the level of a factor.  
We used the most widely accepted parameters, such as average E2 ED, PDR, NRL, and PL.