Modelling calibration uncertainty in networks of environmental sensors
针对低成本环境传感器网络校准困难的问题，提出一种变分方法建模网络中的校准不确定性，在合成和真实空气污染数据上表现优于现有技术，有助于推动低成本传感器的实际部署。
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.