A deployment strategy for UAV based on a probabilistic data coverage model in mobile crowd-sensing

Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using the personal devices of the MCS platform users. However, being the mobility of the devices tightly correlated with mobility of their owners, the covered area might be limited to specific sub-regions. We extend the coverage capability of a MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location by analysing the user's trajectories and the detouring capability of MCS users towards a location of interest. Our model provides a coverage used revealing low-covered locations. These are used as targets for StationPositioning, our proposed algorithm optimizing the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeasn algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%