## 5 Experiments
### Simulation Results  
Figure 17 shows the results of the simulations according to the metrics presented in the previous section, as a function of vehicle densities. Especially, Figure 17a displays the performance results of all solutions investigated according to the overhead metric. The probabilistic solution displays the largest number of messages transmitted because all vehicles perform the tasks of information aggregation and generation of the knowledge, resulting in highly redundant traffic of knowledge. In addition, during the knowledge distribution process, no broadcast suppression mechanism is applied, thus producing a high rate of packet collisions in the network among the analysed solutions, as shown in Figure 17b. Due to the results previously discussed, the probabilistic solution displays the lowest coverage rate, reaching an average of 80%, for all analysed densities, as shown in Figure 17d. The long delays in the delivery of knowledge, compared to the other considered solutions (Figure 17c), are also visible. It is possible to notice that there is a slight drop in the coverage rate as the vehicle density increases. This is due to the high network overhead and the high collision rate. The other solution analysed is catch-up. It is possible to observe that the strategy of inserting an adaptive delay, in the message forwarding process, decreases the total number of messages transmitted and consequently, the collisions. This characteristic is shown in Figures 17a,b when compared to the probabilistic approach. This strategy was able to reduce, on average, 10% of both transmitted messages and packet collisions. In addition to that, catch-up increased the coverage by 5% (Figure 17d). Figure 17: Knowledge generation results. EBM, egocentric betweenness measure. Furthermore, in both the probabilistic approach and catch-up, there is a slight drop in the coverage rate mainly in high densities (250 and 300 vehicles/km\({}^{2}\)). In addition to this, catch-up still has a higher knowledge transmission rate and packet collisions compared to DA2 RFand EBM. It is known that neither the probabilistic approach nor catch-up use any type of selection mechanism to choose the most relevant vehicle to perform the tasks of information aggregation and generation of knowledge. The lack of such a mechanism is translated into the higher delays for both systems when compared to DARF and EBM, as can be seen in Figure 17c. DA2 RF employs a broadcast suppression mechanism in the knowledge forwarding process. Due to this approach, it is possible to see a decrease in the total number of messages transmitted (Figure 17a). On average, DA2 RF reached a reduction of 30% in comparison to the probabilistic method and 20% fewer messages when compared to catch-up. The same tendency was observed in regards to the packet collisions rate (Figure 17b). The simulation results showed a reduction of 30% and 25%, on average, compared to the probabilistic approach and catch-up, respectively. It is important to notice that DA2 RF implements only the broadcast suppression mechanism and does not have any selection mechanism. Because of this, it still introduces a delay closer to the other previously analysed solutions, as depicted in Figure 17c. DA2 RF's approach improves the coverage rate by 18% and 15% when compared to the probabilistic approach and catch-up, respectively. Finally, EBM applies the egocentric betweenness measure to perform the selection of the most relevant vehicle, which will carry out the information aggregation and knowledge generation. In addition to that, it also uses the broadcast suppression mechanism in the knowledge distribution process. This combination enables it to outperform all other solutions in all the metrics evaluated. EBM significantly reduces the total number of messages transmitted, with an average decrease of more than 85% in comparison to the probabilistic approach, as well as 80% and 70% compared to catch-up and DA2 RF, respectively (Figure 17a).