Modelling calibration uncertainty in networks of environmental sensors
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv preprint arXiv
Abstract
Networks of low-cost 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). We
extend it to categorical data produced by citizen-scientist labelling. In Summary: The method achieves uncertainty quantified calibration, which has been one of the barriers to
low-cost sensor deployment and citizen-science research.
Description
Keywords
air pollution, Bayesian modelling, calibration, Gaussian processes, low-cost sensors, variational inference
Citation
Smith, M. T., Ross, M., Ssematimba, J., Alvarado, P. A., Alverez, M., Bainomugisha, E., & Wilkinson, R. (2022). Modelling calibration uncertainty in networks of environmental sensors. arXiv preprint arXiv:2205.01988.