## 6 Open Research Issues  
A first challenge comes from the need to integrate networking and computing, as mentioned in Section 5, due to the need to combine discrete computational with continuous networking processes. Mostly because in a drone swarm both the networking and the computational systems continuously try to estimate the best behavior that the swarm should have under specific conditions in strict spatial-temporal constraints. Moreover, modelling a drone swarm as a NCS needs to consider the influence of time in the overall system behavior, which is sometime neglected when looking at the system from an event model or agent model point of view. Alternatively, some networking functions are based on a best effort service that are not in line with the requirements of a NCS, namely the need for high reliability. Finally, drone swarms are usually distributed, which means that the system components may operate in different devices. Hence, when modelling these distributed systems, there are a set of fundamental properties that need to be included in the overall system, such as synchronization, handling timing and network state inconsistencies, and network delay.  
A second challenge is related to the resource constraint nature of drones, when facing services that require high computational power, such as pattern recognition, and video processing. Therefore, there is the need to perform some computation offloading to address such challenges, by using novel paradigms such as mobile edge computing. For instance, game theory could be used together with resource scheduling and task allocation schemes to achieve a trade-off between computing time and energy consumption while offloading part of the computing task to a nearby ground infrastructure.  
This leads to another challenge related to the management of resources in drone swarms. In a drone swarm resources are limited, meaning that the task of managing resources in the overall system may be done via cooperation to increase its efficiency. This scenario leads to the challenge of assigning tasks to various drones while trying to achieve parallel processing goals.  
Among the resources in a drone, the energy is of special importance. In this aspect devising an efficient networking system is of importance since it consumes most of the energy when compared to the computational system. However, it would be of interest to extend the lifetime of the drone system and its capability to communicate. Hence, a challenge is related to the best way to orchestrate the networking and computational system to increase the lifetime of each drone and of the overall swarm. This orchestration process requires further cooperation among different drones, which is quite complicated when considering the motion characteristic of each drone and the tight coupling between the performance of the networking system and the overall behavior of the swarm.  
Finally, when compared with traditional NCS, a drone swarm leads to increased security issues. First, drones operating in open spaces face a few limitations in what concerns potential access by third parties. Moreover, since the drone swarm is deployed based on a wireless communication infrastructure, its security is harder to achieve than when deploying a regular NCS based on a wired infrastructure. In this perspective, drone communication requires further investigation in what concerns security, since drones are more subjected to malicious attacks than a normal wired NCS. Section 5.1 highlights the benefits that embedding computing into the network may bring to tackle security challenges.