EMULATION OF AIR QUALITY SENSORS IN AN URBAN SMART CITY ENVIRONMENT

Abstract

Urban air pollution poses a serious threat to human health. To monitor it, both individual sensors and systems are used to assess the concentration of dust particles PM1, PM2.5, PM10 and organic compounds. However, the reliability of the sensor system cannot be 100 percent. From time to time, certain sensors in a distributed system fail. For this reason, it is very useful to emulate their readings based on the readings of the remaining sensors. The work describes a data set and proposes a machine learning model, which, based on the readings of functional sensors and weather conditions at the data collection sites, simulates the readings of a failed sensor. The accuracy of such emulation for certain types of pollution has been assessed (the coefficient of determination ranges from 0.43 to 0.61).

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