  Due to the significant air pollution problem, monitoring and prediction for
air quality have become increasingly necessary. To provide real-time
fine-grained air quality monitoring and prediction in urban areas, we have
established our own Internet-of-Things-based sensing system in Peking
University. Due to the energy constraint of the sensors, it is preferred that
the sensors wake up alternatively in an asynchronous pattern, which leads to a
sparse sensing dataset. In this paper, we propose a novel approach to predict
the real-time fine-grained air quality based on asynchronous sensing. The
sparse dataset and the spatial-temporal-meteorological relations are modeled
into the correlation graph, in which way the prediction procedures are
carefully designed. The advantage of the proposed solution over existing ones
is evaluated over the dataset collected by our air quality monitoring system.
