## 6 Conclusions  
The motivation for this work was bringing the benefits of the SDN architecture into OppNets by having a controller to retrieve network context information to orchestrate the data plane in terms of tuning the forwarding strategy to achieve a better network performance. To state the soundness of the proposal, the control layer has been applied to manage the congestion derived from multi-copy-based forwarding algorithms. This controller-driven OppNet has been tested over four scenarios characterized by different mobility patterns and node densities against baseline forwarding strategies based on message replication. For the scenarios with a message distribution following the ISTH function, the simulations show that for the Control configuration, the replication limit tends toward an asymptote proximal to the replication limit of the best-performing Static policy in terms of delivery ratio (optimal). Therefore, it can be stated that under blind network knowledge, the Control configuration approaches the _optimal_ replication limit. Moreover, the Control configuration adapts to changes in the pattern of message generation distribution. It is precisely under these unpredictable conditions that the Control configuration stands out over the other configurations by leveraging its dynamic adaptability to the network conditions. This adaptability facilitates a significantly lower occupancy of the node's buffer and an important reduction in the overhead intrinsic to replication. Furthermore, the Control configuration improves the delivery ratio for all the scenarios. Its effectiveness is accentuated for scenarios with medium-low congestion, as a wider replication limit range can be considered. In contrast, a highly congested scenario is stuck  
Figure 19: Delivery ratio for different \(\alpha\) weights. Figure 20: Delivery ratio for different directive generation periods. to a low replication limit. Undoubtedly, latency benefits from a replication that does not overwhelm the nodes' cache system. The fact that the Control configuration keeps the replication limit at bay to avoid congestion and achieve a better delivery ratio affects the latency. Therefore, the application layer should determine whether to maximize the delivery ratio or minimize the latency, so the Control configuration could apply forwarding strategies to optimize one or the other. Furthermore, the control layer is highly configurable to provide the best performance depending on the Oppnet's nature. Nevertheless, generic values providing a good performance have been determined from simulations over the aforementioned scenarios. In this regard, simulations show that the controller must use recent congestion readings from contacts. Therefore, applying a decay weight over the measurements used to predict the network conditions is decisive. In this regard, it is more effective to perform a short-term prediction than a long-term prediction. Moreover, the simulation demonstrates that the sparser the network is (fewer contacts between nodes), the more directives are needed. For the use case of congestion control, the replication limit needs to grow slowly, whereas, in a congestion state, a sharp reduction is required. The optimal congestion interval is highly coupled to the characteristics of the scenario. Finally, simulations depict that, despite the disconnections, network partitioning, and long delay paths prone to OppNets, a small number of controllers suffices. Overall, this study asserts that (i) a context-aware system built upon the SDN pillar principles is a good approach for context-management in OppNets and (ii) using this context-aware system to regulate the replication in an OppNet driven by a multi-copy forwarding strategy leads to better network performance. Conceptualization, C.B. and S.R. ; data curation, M.d.T. ; formal analysis, M.d.T. ; funding acquisition, S.R. ; investigation, M.d.T. ; methodology, M.d.T. ; project administration, C.B. ; resources, C.B. ; software, M.d.T. ; supervision, C.B. ; validation, M.d.T.