Drones are unmanned aerial vehicles (UAVs) operated remotely with the help of cameras, GPS, and on-device SD cards. These are used for many applications including civilian as well as military. On the other hand, drone swarms are a fleet of drones that work together to achieve a special goal through swarm intelligence approaches. These provide a lot of advantages such as better coverage, accuracy, increased safety, and improved flexibility when compared to a single drone. However, the deployment of such swarms in an adversarial environment poses significant challenges. This work provides an overview of the current state of research on drone swarms in adversarial environments including algorithms for swarming formation of robotic attack drones with their strengths and weaknesses as well as the attack strategies used by attackers. This work also outlines the common adversarial counter-attack methods to disrupt drone attacks consisting of detection and destruction of drone swarms along with their drawbacks, a counter UAV defense system, and splitting large-scale drones into unconnected clusters. After identifying several challenges, an optimized algorithm is proposed to split the large-scale drone swarms more efficiently.

Objective: The challenge of keeping humans in meaningful control of highly automated systems is growing as these systems become more common in high-risk domains like aviation, the military, and emergency services. One suggested method to ensure human control and responsibility is to apply the principles of meaningful human control. This study aimed to explore the applicability of meaningful human control through a case study involving operators interacting with a prototype interface designed for controlling multiple unmanned aerial vehicles fighting forest fires.  Method: A simulated scenario was created and implemented through a prototype interface for human-swarm interaction. Empirical data included screen- and audio recordings of participants engaging with the simulated scenario through the prototype. The Joint Control Framework was used to transcribe and analyze the interaction.  Results: The results indicate that the level of meaningful human control in the interaction between the operator and the unmanned aerial vehicle system is dynamic and context-dependent. It varies based on the type of task, the operator’s level of cognitive control, the level of interaction with the swarm, and the system’s level of autonomy. It is realized through the joint actions performed by the operator and the system.  Conclusion: For meaningful human control to be applicable, it needs to be operationalized as a situated and contextual measure, rather than a binary concept. Future measures of meaningful human control, whether subjective or objective, should reflect this approach.