# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
In this context, hierarchical network architecture, i.e., clustering, enhance the overall network performance of control messages management compared to flat architecture, by reducing energy consumption, interference, and packet loss, while increases scalability and application performance [20]. The hierarchical architecture provides better performance results in a broad mission area composed of a higher number of UAVs [6]. UAVs are organized into groups, and a set of UAVs are considered as Cluster-Head (CH), which work as a local controller to perform more complex tasks, such as the controlling of each group member, collecting data from non-CHs for data aggregation, and sending aggregated data to the controller [21]. It is essential to create groups based not only on placement but also on the residual energy of each node [6]. Besides, UAV mobility makes the network more dynamic to transmit data due to UAV mobility leads to communication flaws and void area. Thus, location, residual energy,and mobility prediction must be considered for an effective control message dissemination in FANET. However, to the best of our knowledge, there is not a unified solution that provides efficient control messages management together with a clustering algorithm. In this article, we present a cluster-based control plane message management for SDN-FANET, called CAPONE (Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NETwork). We introduce a new control message management approach for SDN based FANET by integrating acknowledgment, node location prediction, and a well known clustering approach. The controller is able to predict UAV information based on the UAVs contextual information without disseminating control messages. CAPONE divides the FANET into groups by considering two steps. Specifically, CAPONE determine the number of clusters using the Gap statistics method (i.e., step 1), which is input for a Fuzzy C-means method to determine the groups (i.e., step 2). For the control plane point of view, in each group, the CH works as a local controller to perform more complex tasks, while group members only send UAV contextual information based for the CH. In this sense, CAPONE minimizes the number of control messages without compromise application performance while increases the overall network lifetime. Simulation results show that CAPONE improves Packet Delivery Ratio (PDR), overhead, and remaining energy compared to state-of-the-art SDN-FANET architectures. The main contributions of this article are a cluster-based control message dissemination for SDN-FANET operations, efficient and reliable control message dissemination, implementation, and evaluation of CAPONE in a network simulator to assess network performance metrics in terms of PDR, position prediction error, overhead, and remaining energy. We organize the rest of this article as follows. Section 2 outlines the state-of-the-art about control message dissemination for SDN-FANET, their main drawbacks for an efficient and reliable control message transmission for FANET operations. Section 3 describes the CAPONE protocol implemented in an SDN-FANET architecture. Section 4 details the simulation methodology and results used to evaluate CAPONE. Finally, Section 5 presents the concluding remarks and future work.