Autonomous Tactical Deployment of the UAV Array Using SelfOrganizing Swarm Intelligence This article describes how swarm intelligence can be applied to an array of autonomous unmanned aerial vehicles (UAVs) for strategic deployment. In emergency or disasterstricken areas, telecommunications and geospatial surveillance are strictly critical for situational control. Base stations may fail to work due to natural disasters. To be rapidly deployed, lightweight drones with multirotors that provide stability are used as mobile stations. An autonomous learning approach, xe2x80x9cselforganizing maps (SOMs),xe2x80x9d which can automatically and adaptively coordinate a large array of autonomous dronesxe2x80x94selforganizing UAV arrayxe2x80x94based on requests from end users (EUs) is embedded inside the array. The size and the topology of a UAV array can be dynamically changed in response to various terrains and relocation of EUs. Moreover, with swarm intelligence, the UAV array is capable of reconfiguring its planar topology into a hierarchical one. Such a hierarchical topology divides the entire UAV array into subarrays and creates isolated heterogeneous networks. Thus, the UAV array can cope with diverse situations across geographical barriers by forming a flying ad hoc network.

Learning Sensor Placement from Demonstration for UAV networks This work demonstrates how to leverage previous network expert demonstrations of UAV deployment to automate the drones placement in civil applications. Optimal UAV placement is an NPcomplete problem: it requires a closedform utility function that defines the environment and the UAV constraints, it is not unique and must be defined for each new UAV mission. This complex and timeconsuming process hinders the development of UAVnetworks in civil applications. We propose a method that leverages previous network expert solutions of UAVnetwork deployment to learn the expertxe2x80x99s untold utility function form demonstrations only. This is especially interesting as it may be difficult for the inspection expert to explicit his expertise into such a function as it is too complex. Once learned, our model generates a utility function which maxima match expert UAV locations. We test this method on a WiFi UAV network application inside a crowd simulator and reach similar qualityofservice as the expert. We show that our method is not limited to this UAV application and can be extended to other missions such as building monitoring.