## 5 Experiments
### Simulation Results
As can be seen, the beacon overhead increased linearly as a function of the traffic density, as expected. This expectation was well founded since as the density of vehicles increased, the higher the transmission rate of beacon packets into the network would be. The microscopic view is depicted in Figure 12(b), which shows the average number of beacon packets transmitted by each vehicle in each traffic density. When the experimental scenario had a density of 40 vehicles/km\({}^{2}\), each vehicle, on average, transmitted around 148 beacons during the simulation time; while, in the scenarios with 60 and 80 vehicles/km\({}^{2}\), on average, 134 and 138 beacons were transmitted, respectively. For 100 and 150 vehicles/km\({}^{2}\), there were, on average, 144 and 150 beacons transmitted by each vehicle, respectively. It is easy to see that the number of beacon packets transmitted, for each vehicle, is directly related to its trip time during the simulation time. With that in mind, Figure 14 depicts the average trip time of the vehicles during the simulation. It is possible to observe that in both of the aforementioned figures, the same behaviour appears in all the vehicle traffic densities. Figure 13c depicts the total number of beacon packets lost either by the fact that the communication channel was busy, or by errors in the received packets. As can be observed, the low densities (40 and 60 vehicles/km\({}^{2}\)) presented a minimum packet loss rate. As the vehicle traffic density increased up to 150 vehicles/km\({}^{2}\), the total number of packets lost also increased. The observed behaviour was directly related to the channel busy ratio. Taking this into account, Figure 15 shows the average channel busy ratio for each vehicle traffic density. As the simulation time was set to 100 s, the calculation of the total busy time was nothing more than the channel busy ratio multiplied by the simulation time. In our case, for densities of 40 and 60 vehicles/km\({}^{2}\), the channel was busy for the shortest time, and as the density increased, the average time also increased. Even in the density of 150 vehicles/km\({}^{2}\), a maximum of 35% of channel availability was consumed. These results show that the beacon transmission frequency of 1 Hz was suitable, for this scenario, together with the mobility model applied, due to low channel utilization. The number of beacon packets received per vehicle is depicted in Figure 13d. This metric, combined with the channel busy ratio (Figure 15), can indicate if the beacon transmission frequency is adequate or not. In the same way as the total number of beacon packets transmitted, the number of beacon packets received also increased linearly as a function of the vehicle traffic density. For instance, for densities of 40, 60, 80, 100 and 150 vehicles/km\({}^{2}\), there were, on average, 480, 1300, 1700, 2000 and 3450 beacon packets received per vehicle, respectively. As mentioned before, the channel utilization in  
Figure 14: Average trip time of vehicles vs. densities. Figure 15: Impact on channel busy ratio vs. densities. our approach was low; this confirmed, once again, that the beacon transmission frequency of 1 Hz was proper. Egocentric Betweenness Measure as a Vehicle Selection Mechanism for Knowledge Generation about Traffic Congestion  
In recent years, several intelligent transportation systems (ITS) that deal with local information management about traffic conditions have been proposed [23; 25; 26]. This type of system usually extracts information related to the traffic condition of a given road by processing the aggregated local information. This information is, more often than not, received from the one-hop neighbour vehicles through beacon packages. However, the above-cited systems have the same shortcoming, which is the absence of a VSM to perform the task of knowledge generation. Without any type of selection mechanism, all the vehicles are candidates to carry out such a task, resulting in highly redundant traffic of knowledge, as well as high bandwidth consumption.