  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 unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on 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 two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. 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.
