## 2 Related Work  
Despite all the applicability scenarios of drone swarms, there are still several development and deployment constraints that need to be solved to control drone swarms in an automated, secure and scalable manner. Aiming to tackle such research challenges, a first attempt passes by relying on a Software-Defined Networking (SDN) approach [13], where a centralized entity takes decisions about the operations of all drones in the swarm, being such drone deployed only gives enough functionalities to allow them to execute the directives sent by the SDN controller [5]. Such SDN approach is mostly suitable to deploy drone swarms based on a non-interacting strategy (c.f., Section 3.2). Prior research efforts such as Soft-RAN [14] and the SDN-WISE reported [15], proposed the design of the centralized control system by removing control decisions from drones, thus requiring the implementation of programmable interfaces to allow them to receive management intents from a network operator. One example is the usage of edge computing to push to the edge of a cellular network (e.g., collocated with a set of base station, or installed in a cloud radio access network) a set of control functions that normally require low latency from the request to the execution of an action derived from a computational process installed in the edge [16]. However, such approaches normally aim only to optimize networking functionalities, while they do not consider the dynamics of the drone swarm, such as network formation or the inter-dependencies between control of networking functionalities and computational functions such as flight control. Besides centralized solutions, prior work already reports of some efforts to bring the computation not to the edge of the drone system, but to inside the drone system itself, more aligned with an interactive deployment strategy (c.f., Section 3.3). However, such efforts towards the distributed control of a drone system are usually conducted considering only the networking system [17] or the computational system [18]. However, the influences between the networking and the computational systems are not the focus of these surveys, although they can be instructive to deal with the problems from a cross-disciplinary perspective. With the development of network architectures for drone swarms overly of interest today, there is the need to search methods that can handle the in-network mobility requirement and distributed computation functions across the network. However, in seeking to implement such efforts for swarm communication concepts, current IP-based solutions seem to be unable to satisfactorily handle mobility management. Due to that, many studies have recently implemented novel networking models aiming to provide support to couple networking frameworks for mobile networks with the capability to embed computation functions across the network. Some of those paradigms include Named Data Networking (NDN) [19], Information Centric Networking (ICN) [20] and Named Function Networking (NFN) paradigms [21], which may be capable of providing seamless connection support for mobility management on top of the existing IP-based communication framework at the networking layer. With such support the design of communication protocols for drone swarm can strive as such approaches are capable of performing data forwarding based on content locators and not host locators as happens with the traditional IP-based delivery of data [19]. Furthermore, enhancements were made to the NDN concept to develop the NFN network model, thus bringing on board the functionality to query the network about computation functions and not only to data objects. The NFN concept computes on data and has functionality to convert computed workflows into Interest packets to be forwarded in the network aiming to find a suitable data source or cache.