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 (UAV-BSs) 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 sub-problems, 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 min-max time consumption required for all UAVs. They are both proved to be NP-hard, and heuristic algorithms are proposed to solve them efficiently. A prototype system, UAV-Net, 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.

Unmanned Aerial Vehicle (UAV) provides an effective way to create an Aerial Panoramic Map (APM). Generally, it consists of three steps: 1) select a set of locations from a map, 2) take photos from different angles at each selected location one by one, and 3) make panoramic images by the Panoramic Mosaic technology, and then create an APM with these images. However, it is always labor-intensive and time-consuming to complete these steps in a large region such as a campus and a park, due to multiple reasons: 1) inexperience for location selection, 2) low-efficiency for manually operating UAV to fly among different locations one by one and make photos from different angles, and 3) limited energy supply and low-efficiency for a single UAV. DJI GO[1] has been developed to simplify a part of operations in steps 2) and 3), by which we only need one button to take photos automatically from different angles at a selected location. However, it is still required to manually select locations and control UAV to fly among different locations. Moreover, the defects of using a single UAV still exist. By contrast, we aim to design an Auto nomously C ooperative U AV system platform for P anoramic map generation, AutoCUP, which leverages multiple UAVs to full-automatically and high-efficiently complete all steps of creating an APM.