Therefore, the development of drone swarms will increase the impact of drone systems based on the coordination of efforts among drones allowing for wider coverage, higher flexibility and robustness through redundancy [8]. In a swarm, each drone acts as a simple agent in a networked control system. All such agents are conjoined in a multi-agent system to work based on the integration of a networking system and a computational system aiming to allow simple behaviors to be distributed across all agents (drones), giving rise to collaborative behaviors capable of solving complex problems [9]. To tackle the technical challenges of drone swarms, researchers have been dedicating some attention to advances in wireless communications and in performance computation able to sustain for instance decentralized flight control operations [10]. However, most of these efforts aim to contribute to the creation of drone systems orchestrated through a centralized control, which to start with poses some scalability and security limitations. Moreover, in a wireless environment in which communication channels are subject to variations in its quality as well as potential malicious attacks, swarm functionalities such as collision avoidance and formation control may be jeopardized. In this scenario a drone swarm system will gain if developed based on self-organizing properties, thus requiring no reliance on any well-known controller, which would need frequent updates of status of the complete swarm system. A successful operation of drone swarms in a self-organized manner requires the integration of the networking and computational systems [11], which until now have been mostly investigated in isolation. The self-organized operation of a drone swarm will rely on the tight consolidation of the networking and computational systems. Each of these two systems shall benefit from the presence of the other: for instance, the networking system should be able to find better transmission channels towards neighbors and/or better end-to-end paths towards faraway drones when a computational system estimates (or predicts if based on some artificial intelligence algorithm) that the quality (e.g., packet loss) of some wireless links may be endangered due for instance to shadowing effect, or some end-to-end path may become intermittently available, leading to a higher network robustness [12]. In this review, we intend to provide a state-of-the-art analysis of how to develop self-organized drone swarms as networked control systems via the integration of networking and computational system, including their coupling effects. In addition to literature overview, we also identify key research challenges and open issues to integrate networking and computing for developing drone swarms as networked control systems. To the best of our knowledge this is the first review that investigates the fundamental design choices to devise drone swarms as networked control systems via the integration of the networking and the computational systems. We summarize the main research contributions of this review as follows:  
1. Analysis of prior efforts to develop drone swarms, in particular as networked control systems. 2. Analysis of two different types of design strategies to deploy drone swarms, namely non-interactive and interactive strategies. 3. Description of the basic functionality that needs to be provided by the networking and the computational systems. 4. Analysis of how the networking and computational system should be integrated to realize a networked control system. 5. Introduction to open issues related to the different natures of the networking and computing systems, as well as challenges related among others to the management of resources. We start by providing a characterization of related research efforts in Section 2. Section 3 provides a description of how drone swarms look like as networked control systems, providing an analysis of two different deployment strategies.