REAL TIME PREDICTION FOR FINE GRAINED AIR QUALITY MONITORING SYSTEM

INFORMATION SCIENCE AND ENGINEERING

Air pollution is one of the serious problems in the urban cities, where particulate matter (PM2.5) has greater effect on the humans than any other impure substances. The significant air pollution problem, monitoring and prediction for Air Ambiance have become increasingly necessary. To provide instantaneous close-grained Air Ambiance 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 instantaneous close-grained Air Ambiance 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 Ambiance monitoring system.

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