Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting low-cost sensors' measurements accurately and detecting the calibrators' drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators' strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of low-cost sensors suffered from sensing accuracy.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Issue number2
Pages (from-to)1366-1379
Number of pages14
Publication statusPublished - Feb 2023
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences
  • 1172 Environmental sciences

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