Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing
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Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on the unmanned-aerial-vehicles, called ARMS, to efficiently build fine-grained AQI maps in real-time. Specifically, we first propose the Gaussian plume model on the basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a 2-D open space like a roadside park, and a 3-D space like a courtyard inside a building. The experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm.