## 5 Experimentation
### Results
#### 5.5.3 Performance Evaluation  
This section presents and evaluates the results for the performance metrics listed in Section 5.1 for the Control and No-Control configurations in the different scenarios. Figure 9 shows that the overhead derived from the relay of the message copies depends on the \(l\). EP's overhead surpasses Static and Control by two to three orders of magnitude, while Statics's overhead increases for higher \(l\) values. The Control's overhead ratio is similar to that of Static*. The slight difference between the two configurations is due to Control's automatic recalculations of \(l\). The dropped messages and the overhead directly affect the delivery ratio performance. As we can see in Figure 10, a high replication limit takes its toll on the delivery ratio performance. With the highest replication limit, the EP policy floods the network, triggering a significant number of drops and, therefore, begets the worst delivery ratio. The behaviour above also applies to the Static policy. The higher the \(l\) is, the poorer the delivery ratio we obtain. Indeed, the Info5 and Campus scenarios (the more congested scenarios), obtain the highest delivery ratio with a static policy with a low \(l\): 2 in both cases. As \(l\) increases, the delivery ratio performance decreases. However, for the Emergency scenario, which combines a high message generation frequency with a low one, and for the Taxis scenario, with a low-medium congestion level, the delivery ratio increases for the values of \(l\) up to the inflexion point of the Static's \(l\) with the best delivery ratio. This behaviour is coherent with  
Figure 8: Percentage of dropped versus relayed messages by the different policies. The Static suffix denotes the replication limit. In the legend, the configuration named Static* corresponds to the Static configuration with the replication limit that the Control tends toward. the fact that high values of \(l\) prejudice the congested scenarios. In contrast, the low-medium congested scenarios admit higher values of \(l\), favouring a higher delivery ratio. Overall, the Control policy obtains the best delivery ratio for all the scenarios. We obtain the best increase ratio in the scenarios with low-medium congestion levels. We specifically obtain a 14% and an 11% increment in the delivery ratio for Taxis and Emergency, respectively, over the Static configuration with the best-performing \(l\) value. As we have previously seen, these scenarios admit higher \(l\) values, bringing on a higher delivery ratio. The ability of the Control policy to dynamically adapt the \(l\) provides an optimal \(l\) value depending on the current congestion situation at the current time. This flexibility allows the Control policy to stand out in low-medium congestion scenarios over the other configurations. On the other hand, for highly congested scenarios, where the best option is to keep a very low \(l\) close to direct delivery, the Control policy provides a low \(l\) value. It also benefits from the dynamism and slightly outperforms the Static policy with the best-performing \(l\) value by 4% and 9% for the Info5 and Campus scenarios, respectively. Latency Average  
Figure 11 shows that, despite the crushing effects of the message flooding strategy over the buffer occupancy, delivery ratio, and overhead, when it comes to the latency, message flooding benefits the arrival of the messages to their destination and, therefore, it obtains a good performance. Indeed, as pointed out by [PERSON] et al. [21], flooding-based replication benefits the latency of the messages at the expense of the delivery ratio in case of congestion. This is because dropped messages will not reach the destination, decreasing the delivery ratio. In contrast, with high message dissemination, the more buffered copies, the more chances that a message copy will have to be delivered upon an opportunistic contact, despite plenty of dropped messages.