Modelling calibration uncertainty in networks of environmental sensors

Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. Howe...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society Series C: Applied Statistics 2023-12, Vol.72 (5), p.1187-1209
Main Authors: Smith, Michael Thomas, Ross, Magnus, Ssematimba, Joel, Álvarez, Mauricio A, Bainomugisha, Engineer, Wilkinson, Richard
Format: Article
Language:English
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Summary:Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.
ISSN:0035-9254
1467-9876
DOI:10.1093/jrsssc/qlad075