## 5 Experimentation
### Results
#### 5.5.4 Evaluation of the Control Settings Impact on the Delivery Ratio
Therefore, when the optimal congestion interval upper bound is set close to the maximum buffer occupation, the replication limit set by the controller is too high. Thus, a more conservative optimal congestion range gives better results which, in this case, is 0.6-0.7. For the Taxis scenario, the sparsest scenario with fewer contact opportunities, it can be seen that the most conservative interval configuration, 0.3-0.5, performs by far the worst (24% less than the best interval). In contrast, the intervals with a high \(o_{max}\) have a good performance. This is because using a high level of message replication promotes a higher message delivery in a sparse scenario. Finally, the Emergency scenario behaves similarly to the Taxis scenario. The performance of the most conservative interval is the worst (11% less than the best range). Nevertheless, the performance variance of the different intervals is 0.0004, whereas for the Taxis case it is a bit higher: 0.002. For an unpredictable scenario, similarly to the Taxis scenario, the best strategy is to use an interval with a high \(o_{max}\) to keep a high replication limit and, therefore, to have more chances for message delivery. Altogether, we have seen that in a congested scenario, the key is to maintain a steady replication limit if it maintains the congestion within the configured optimal congestion interval. It is better to set a conservative optimal replication limit for a medium- to high-congestion scenario to avoid high replication that could yield in a future congested scenario. On the contrary, for low-congestion scenarios, setting an optimal congestion interval with a high upper bound leads to a higher replication limit favouring message replication, which increases the delivery ratio. Figure 12: Delivery ratio by number of controllers. Additive Increase (\(k_{2}\)); Multiplicative Decrease (\(k_{1}\))  
Figure 14 shows that for the additive increase factor used in (7), the value that gives the best performance in all the scenarios is, undoubtedly, 1. From this result, it can be stated that it is essential that the replication limit grows slowly to mitigate the adverse effects of high replication as much as possible. As for the multiplicative decrease factor (MD), for all the scenarios except for the Campus scenario, the best option is to reduce 75% the replication limit as a drastic measure to decrease the congestion caused by replication. For all the scenarios except for the Taxis scenario, the delivery ratio continues decreasing for higher MD values (implying less replication reduction). For the particular case of the Taxis scenario, its sparse condition results in more fluctuating delivery ratios depending on the MD factor, but none overcome the ceiling achieved with the 75% of reduction. Going back to analyzing the results for the Campus scenario, which is highly connected and the most congested, we can see that we obtain the best result by applying a reduction  
Figure 14: Delivery ratio vs. different values for the AIMD control function. Figure 13: Delivery ratio depending on the optimal congestion interval per scenario. factor of 50%, maintaining a higher replication level. These results are consistent with those in the previous section, where it was stated that once the congestion status fitted in the congestion range thresholds, the best strategy was to keep the replication limit within the range. Precisely, reducing the replication limit to half is a good method for keeping the congestion steady within the optimal congestion interval.