## 5 Experiments
### Simulation Results
Figure 9: CDF of the number of one-hop neighbours in relation to the vehicle traffic densities. The relationship between the egocentric betweenness scores and the number of one-hop neighbours is depicted in Figure 10. This figure shows the average egocentric betweenness score (red line) and the average number of one-hop neighbours (blue line) for all vehicle traffic densities. Therefore, it summarizes all the information presented in the two sets of Figures 8 and 9. The observed behaviour of both measures is in agreement: as the traffic density increased, the number of vehicles in the vicinity and the egocentric betweenness scores also increased. For instance, in a low traffic density (40 vehicles/km\({}^{2}\)), the egocentric betweenness score was around 2.5, and the number of one-hop neighbours was around 3.9, on average. On the other hand, in a high traffic density (150 vehicles/km\({}^{2}\)), the egocentric betweenness score and the number of one-hop neighbours were around 12.2 and 9.8 on average, respectively. Another important analysis that can be performed in the egocentric betweenness measure is the calculation of the smallest time window duration in which there were no changes to the egocentric betweenness scores in relation to the vehicle traffic densities. The CDF of the time window duration in each traffic density is shown in the Figure 11 set. In this case, it is possible to notice that 90% of the samples, for densities of 40, 60, 80, 100 and 150 vehicles/km\({}^{2}\), have time window durations that were lower than 9, 8, 7, 6 and 5 s, respectively. Figure 12 shows the average time window duration in each traffic density. This metric is important in vehicular networks because many applications rely on a stable period of connectivity between nodes [36; 37; 38]. The figure shows that as the traffic increased, the average time window duration decreased, until reaching a stable plateau. For example, when the density was 40 vehicles/km\({}^{2}\), the average time window was around 3.55 s. When the density increased, the average time window rapidly decreased until reaching the plateau at 2.95 s, for the cases of 100 vehicles/km\({}^{2}\) and 150 vehicles/km\({}^{2}\). For many distributed applications, the real-time content distribution within the area of interest was less than 2 s [37; 38]. Therefore, the average time window reached into all densities of the simulations was sufficient to meet the requirements of such applications. The behaviour depicted in the picture confirmed our expectation: as traffic increased, the trend was that the list of one-hop neighbours fluctuated rapidly over time. One point worth highlighting is that the time can vary according to the scenario used, as well as the mobility model and the vehicle traffic densities applied. Figure 10: The relationship between the egocentric betweenness score and the number of one-hop neighbours. The second set of experiments consisted of performing the analysis of the network traffic. This analysis is needed to demonstrate the scalability of our proposed approach, since the periodic exchange of beacon packets, to stay aware of the one-hop neighbour topology, was carried out by means of vehicle-to-vehicle communications. The experiment results of the metrics such as overhead, beacon transmitted per vehicle, beacon received and total lost packets are depicted in Figure 13. The detailed results of each one of these metrics are given below. Figure 13a provides a macroscopic view of the total number of the beacon packets transmitted in each traffic density. For instance, in densities of 40, 60, 80, 100 and 150 vehicles/km\({}^{2}\), we had on average  
Figure 11: CDF of the time window duration in which there were no changes to the egocentric betweenness score in relation to the vehicle traffic densities. Figure 12: Average time window duration in which there were no changes to the egocentric betweenness scores. 49,000, 70,000, 90,000, 120,000 and 180,000 transmitted beacon packets, respectively.