Efficient UAV Scheduling for Air Pollution Source Detection From Chimneys in an Industrial Area

Abstract

With the rapid development of unmanned aerial vehicles (UAVs), many related applications using UAVs are to monitor air quality in urban, rural, or industrial areas. They often focus on how to monitor the propagation of air pollution when the pollution source is known. However, the pollution source is often unknown and hard to identify because many chimneys will emit smoke in an industrial area. Therefore, we focus on studying the problems of using single and multiple UAVs to detect the pollution source in an industrial area such that the search time is minimized when the locations of UAVs are predetermined, which is termed the air pollution source detection with single UAV (APSD-SUAV) problem and the APSD with multiple UAV (APSD-MUAV) problem, respectively. We also study the same problem if the locations of UAVs are not predetermined, which is termed the UAV deployment and APSD (UAVD-APSD) problem. Then, we propose three heuristics, including the interference-graph-based algorithm (IGBA), the extended IGBA (EIGBA), and the Hungarian-based algorithm (HBA), respectively, for the APSD-SUAV, the APSD-MUAV, and the UAVD-APSD problems. Simulations are conducted to show the performances of the proposed algorithms.