## 5 Experiments
### Simulation Setup  
The experiments were carried out with the aid of three different simulators, namely OMNeT++ 5.0 [31] (event-based network simulator), SUMO 0.29.0 [32] (road traffic simulator) and Veins 4.5 [33] (vehicular network simulator, which integrates both aforementioned simulators). The physical (PHY) and medium access control (MAC) layers were implemented based on the WAVE (Wireless Access in Vehicular Environment) standard, also known as IEEE 802.11p.  
As for simulation parameters, each vehicle had a transmission rate of 6 Mbps, a transmission power of 0.98 mW, a receiver sensitivity of \(-82\) dBm and a transmission range of 200 m. Channel 178 (control channel (CCH)) was used to exchange beacon packets, thereby excluding the effects caused by channel switching between the CCH and the SCH (service channel).  
Figure 5: The simulation setup layers.  
In order to evaluate the applicability of the egocentric betweenness approach in vehicular networks, a real map clipping of the Erlangen area (Germany), obtained from OpenStreetMap (www.openstreetmap.org), was used (Figure 6). Meanwhile, a set of feasible vehicle routes was synthetically generated with the aid of SUMO. Vehicle mobility used the Krauss car following model [34]. Five different sets of vehicles traffic densities were generated to validate our approach (40, 60, 80, 100 and 150 vehicles/km\({}^{2}\)).  
Finally, all experimental results of this work were executed thirty-three times on different vehicle traffic densities with a confidence interval of 95%. Table 3 summarizes the simulation parameter settings.  
In order to evaluate the performance of the proposed approach, eight metrics were used and are described in detail below.  
* Overhead: shows the number of beacon packets transmitted in the network by all vehicles during the simulation run;
* Beacon transmitted per vehicle: gives the number of beacon packets transmitted per each vehicle during the simulation run;
* Beacon received: displays the number of beacon packets received per vehicle during the simulation run;
* Total of lost packets: is the sum of both RxTx (receive/transmit) and SNIR (signal to noise plus interference ratio) lost packets; the first one occurs due to the busy communication channel, whereas the second one occurs due to bit errors in received packets;
* Channel busy ratio: indicates the fraction of the time in which the channel is identified as busy;
* Regression analysis: is a set of statistical processes to estimate the linear relationships between two datasets;
* Pearson correlation coefficient: expresses the strength of a linear association between two datasets;  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Density of vehicles & 40–150 vehicles/km\({}^{2}\) \\ MAC layer & 802.11 p \\ Channel & 178 (5.89 GHz) \\ Bandwidth & 10 MHz \\ Transmission power & 0.98 mW \\ Bitrate & 6 Mbps \\ Sensitivity & \(-82\) dBm \\ Transmission range & 200 m \\ Beacon transmission frequency & 1 Hz \\ Simulation time & 350 s \\ Confidence interval & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Figure 6: Map clipping from Erlangen, Germany. The figure on the left was imported from OSM and on the right represents the road topology used in our simulations.  
* Window time: points out the smallest window time under which there are no changes in the egocentric betweenness.  
In order to provide a better understanding of our approach, results are compared to the ones obtained from the socioeconomic betweenness approach. For this purpose, a dynamic graph was generated, with the aid of the Dynamic Graph Library ([PERSON], 2016), to perform the socioeconomic betweenness calculation ([PERSON], 2016). This library requires floating car data (FCD) as the input parameter. FCD is a method applied to gather traffic knowledge. In the socioeconomic approach, all the vehicle network topology knowledge was used as input.