Smith, M.T., Ross, M., Ssematimba, J. et al. (3 more authors) (2023) Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society Series C: Applied Statistics, 72 (5). pp. 1187-1209. ISSN 0035-9254
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Oxford University Press. This is an author-produced version of a paper subsequently published in Journal of the Royal Statistical Society Series C: Applied Statistics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | air pollution; Bayesian modelling; calibration; Gaussian processes; low-cost sensors; variational inference |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/T00343X/1 GOOGLE.ORG Google AirQo Project |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Aug 2023 16:41 |
Last Modified: | 04 Oct 2024 14:16 |
Status: | Published |
Publisher: | Oxford University Press |
Refereed: | Yes |
Identification Number: | 10.1093/jrsssc/qlad075 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202440 |