# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 2 Related Work  
[PERSON] et al. [22] highlighted the importance of control plane management in a wireless network for the 5G. In 5G, the SDN architecture is increasingly becoming employed to manage a wide range of heterogeneous wireless devices and software systems. In this context, the authors analyzed recent architectures suitable for 5G networks and have presented a qualitative comparison between the direct wireless connection and wireless indirect connection schemes. The authors designed an effective direct and indirect wireless SDN control plane framework to improve the management of heterogeneous devices in recent 5G networks. [PERSON] et al. [23] proposed a secure mobility model between UAVs and wireless sensor network (WSN) nodes, where communication would be through the SDN controller for authentication and coordination. The authors exploit the advantages of SDN to support dynamic networks by establishing a central controller to manage the WSN coverage and authentication using UAVs. The UAVs trajectories are based on the density of each WSN cluster. This information is dynamically updated according to the statistics collected from the overall network. [PERSON] and [PERSON] [24] present an SDN/NFV for FANET in a scenario where terrestrial nodes and UAVs are both equipped with cameras for monitoring rural areas. UAVs serve as a backbone network for the platform, where end-users monitor a particular rural area. The architecture comprises backbone drone nodes that form a meshed network, where UAVs are equipped with NFVs to provide virtual network functions (VNFs) that implement video transcoding and streaming. A central drone manages the network and controls all aspects of virtualization. [PERSON] and [PERSON] [25] also have successfully applied an SDN architecture for FANET management. In this work, the authors present an amateur drone monitoring and tracking service as they might be a threat to citizens in a variety of scenarios. The proposed architecture is composed of a centralized controller present in clouds and UAV nodes responsible for monitoring, tracking, jamming, and hunting tasks. [PERSON] et al. [12] implemented an SDN-FANET that uses SDN to separate the control and data plane to manage the UAVs in a coordinated manner/way. They focus on the problem of optimal UAV Relay Node placement for real-time video services and collision avoidance by considering global UAV context information, such as UAV position, movement trajectories, and residual energy. However, this work does not present a method to avoid network overhead once the number of control messages is not reduced. [PERSON] et al. [11] introduced an SDN module to manage the UAV battery replenishment or replacement in scenarios of video surveillance. It takes advantage of an SDN architecture with a centralized controller to obtain the energy status of all UAVs and then decides when a node needs to go to a ground station to change or recharge its battery. This work considered that all the nodes have to send every second a beacon containing the location and energy status to the controller node. This way, the controller node is indeed always aware of the entire network, but the number of messages shall increase considerably with the increasing number of UAVs in different scenarios. In our work, one of the main goals is to reduce the amount of control messages transmissions in scenarios with clustered nodes. To face FANET issues, such as communications issues due to frequently changing topology, [PERSON] et al. [26] suggested a self-organized clustering scheme for FANET by using a behavioral study of Glowworm Swarm Optimization (GSO). In this case, the group formation and cluster head election consider the luciferin value and the residual energy of the UAVs and relies on the connectivity with a Ground Control Station (GCS). From a different perspective, we consider a centralized approach that takes advantage of a UAV as a Controller, which might attend larger and hard-to-reach areas. [PERSON] et al.