Accurate calibration of low-cost environmental sensors is a prerequisite for their successful use in many monitoring applications. State-of-the-art calibration methods vary from simple linear regression to sophisticated deep models based on LSTMs and GRUs. The latter take past measurements to improve calibration accuracy.

In this article, we argue that both recent past and close future measurements help to achieve accurate calibration, whereas accuracy improvements beyond the past come with a delay introduced by the occurrence of the future.

We propose a generalized many-to-many calibration scheme called SensorFormer based on the successful Transformer model which takes both past and future raw measurements into account. We show that the proposed approach: 1) outperforms other methods by improving calibration accuracy by 16.5%–20.4% on public data sets and own field data and 2) can efficiently run on low-power microcontrollers with very limited computational and storage capabilities. The latter is achieved by a novel optimization technique based on learnable input subsampling taking advantage of the properties of typical sensor data.

We manage to reduce the model size by 20%–33% and minimize the overall floating point operations per second (FLOPs) by 65% while maintaining superior accuracy than state-of-the-art methods.

Y. Cheng, O. Saukh, L. Thiele, SensorFormer: Efficient Many-to-Many Sensor Calibration With Learnable Input Subsampling,  IEEE Internet of Things Journal 9(20) (2022)