# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 4 Evaluation
### Simulation Results  
SDN-FANET [12] considers multi-hop communication to disseminate control messages by deploying a constant number of forwarder node \(CN_{fw}\). These forwarder nodes are \(CN_{fw}\) hovering on fixed positions to guarantee the connection range between the \(CN\) and distant UAVs. To better define the number of forwarding nodes (\(CN_{fw}\)) in SDN-FANET, we analyze the PDR by varying the number of forwarding nodes \(CN_{fw}\) distributed around the controller nod in the scenario, i.e., 3, 4, and 5. By analyzing the results, we achieved better PDR using 4 forwarding nodes \(CN_{fw}\). The results depicted in Figure 3 shows PDR using different number of static controller forwarder \(CN_{fw}\). Based on the results of Figure 3, it is possible to conclude that 4 controller forwarder \(CN_{fw}\) provided the highest PDR for control message dissemination in SDN-FANET operations. This is because the forwarder nodes are equally distributed around the \(CN\) in static positions forming a square with a fixed distance. Besides, the distance between a \(CN_{fw}\) and the edge of the scenario corresponds to the maximum transmission range that a UAV can reach. Considering 3 forwarder node \(CN_{fw}\), some UAVs are not able to reach the controller node \(CN\), i.e., caused by void area. In addition, 5 or more forwarder node \(CN_{fw}\) increases the number of hops and the interference caused by forwarding tasks, which causes more collisions at the MAC layer and increases the delay, where control messages with high delay are dropped. Figure 4 shows the PDR for control message dissemination considering different mechanisms. As shown in Figure 4, CAPONE and SDN-FANET + PREDICTION deliver more packets compared to SDN-FANET in most cases. Both reduce the number of transmitted control messages by considering a prediction algorithm. However, it is important to highlight that the standard deviation of CAPONE is lower than SDN-FANET + PREDICTION, since CAPONE considers a clustering approach, where the CH is responsible for control packets aggregation to reduce the network overhead. On the other hand, SDN-FANET considers a continuous and periodic control message transmission, causing more collisions at the MAC layer and increasing the delay, where the delayed packets are dropped, such as explained for the results of Figure 3.  
Figure 5 shows the position error considering different control plane message management mechanisms. By analyzing the results, we conclude that SDN-FANET has a lower position error compared to SDN-FANET + PREDICTION and CAPONE. This is because in SDN-FANET, as the messages are continuously sent by all the nodes every second, it is more likely that the controller node \(CN\) receives a more accurate location. On the other hand, SDN-FANET + PREDICTION and CAPONE considers mobility prediction to reduce the contextual information dissemination, which introduces an acceptable error for decision making in FANET scenarios. Figure 3: PDR considering different number of forwarder nodes for SDN-FANET. Figure 6 shows the total number of control messages transmitted by all UAVs during the entire simulation i.e., overhead, introduced by different control plane message management mechanisms. By analyzing the results, we can conclude that UAVs considering SDN-FANET transmitted about 6500 control messages during its operation, due to its constant control message transmissions. The prediction approach considered by SDN-FANET + PREDICTION decreased this value to approximately 550 messages by only send data when the UAV considerably changes its position, while CAPONE obtained the best result, transmitting about 390 messages during its operation. CAPONE reduces the network overhead due to its predicting and clustering approach, making the communication process more efficient. Figure 7 shows the average remaining energy in each node considering evaluated SDN-FANET architectures.