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.

UAVNet: Effective and Efficient UAV Network Deployment for Extending Cell Tower Coverage Nowadays we are witnessing an explosive growth of mobile data traffic, but users still often experience insufficient or unstable network bandwidth in many realistic scenarios. Unmanned aerial vehicle mounted base stations (UAVBSs) provide a novel and promising solution for serving regions with bandwidth shortfall. It is significant to investigate how to deploy UAVs for maximizing the sum throughput of a set of clients scattered in various locations in an effective and efficient way. A basic idea is to use RF ray tracing simulations as a hint to narrow down the search space of UAVs for conducting measurements. Furthermore, we study two key subproblems, chunk selection, which finds an optimal subset of chunks in the region as the search space of UAVs, and chunk search, which plans the scanning path to cover the search space with the minmax time consumption required for all UAVs. They are both proved to be NPhard, and heuristic algorithms are proposed to solve them efficiently. A prototype system, UAVNet, is implemented to conduct measurements by a UAV mounted WiFi AP communicating with 7 clients scattered in a campus, and extensive simulations are combined, reporting an obvious throughput gain with a small measurement overhead and time consumption.