# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 4 Parameter Tuning and Evaluation through Simulations
### Results
#### 4.3.3 Comparative Evaluation of the Store-Carry-Forward Strategies  
Given that store-carry-forward achieves a higher coverage over time by increasing the number of duplicates, we show the evolution of the performance along the first 450 s since the source emits the message. We use the X axis to represent the time after the emission, and we have chosen four instants to measure each metric: after 1 s, 5 s, 250 s and 450 s. The first milestone corresponds to the first moments of the dissemination, before store-carry-forward can be applied. The next three show the evolution until all of the vehicles that were in the scenario at the beginning of the dissemination have left it. In this lapse, vehicles keep entering and leaving the scenario (though the density is kept constant thanks to the VACaMobil extension). At each milestone, we need to take into account the activity of the vehicles that have been in the scenario up until that point. The graphs that show the performance results in the following sections reflect this facet. Speed-Adaptive Approach  
Figures 7-9 contain the simulation results when using Equation (5) in Section 3.2.2 to compute the interval between subsequent retransmissions. In this case, we have included the results of applying  
Figure 7: Involved vehicles with the speed-adaptive approach for store-carry-forward in the New York scenario: (**a**) roadway scheme, 25 vehicles/km\({}^{2}\); (**b**) map-based scheme, 25 vehicles/km\({}^{2}\); (**c**) roadway scheme, 100 vehicles/km\({}^{2}\); (**d**) map-based scheme, 100 vehicles/km\({}^{2}\). the store-carry-forward mechanism to the map-based urban scheme, as well. It is worth noticing that the results of the roadway distance-based and map-based adapted schemes are practically the same in the long run. We have cut down on the number of graphs for brevity, but this aspect is also true for the fixed interval and the map polling strategies. In Figure 7, we can see the delivery ratio along with the portion of vehicles that acted as relays, as well as how many of them applied store-carry-forward. Figure 7a,b correspond to a low traffic density, and thus, they show a higher proportion of relays than what we see in Figure 7c,d. We observe that the portion of relays applying store-carry-forward is 0% until after 5 s since the beginning of the dissemination. The reason is that the speed-adaptive period is larger than that. Given our configured communication range, vehicles should be traveling at more than 167 km/h in order to compute a shorter delay. The absolute portion of relays that apply store-carry-forward after 450 s is slightly higher in the scenarios with 100 vehicles/km\({}^{2}\). This is, in fact, relatively much lower than in the sparse traffic situations. We could expect this effect, given that large gaps are more prone to occur in the latter. In Figure 8, we can see the average number of duplicates that were necessary for the dissemination of a single message, together with how many of them were the second or more retransmission from the same relay. As we could already appreciate in the previous set of graphs, the proportion of messages due to store-carry-forward is reduced. The number of messages shows a predictable rise in time as new vehicles enter the ROI and become targets and relays. Finally, we see the general efficiency at a glance in Figure 9. The ratio of duplicates by receivers goes up slowly, and the number of retransmissions does not reach the accumulated number of targets after 450 s. We will be able to put this information in context when we compare this approach with the other two. Figure 8: Sent duplicates with the speed-adaptive approach for store-carry-forward in the New York scenario: (**a**) roadway scheme, 25 vehicles/km\({}^{2}\); (**b**) map-based scheme, 25 vehicles/km\({}^{2}\); (**c**) roadway scheme, 100 vehicles/km\({}^{2}\); (**d**) map-based scheme, 100 vehicles/km\({}^{2}\).