Sens-BERT: Enabling Transferability and Re-calibration of Calibration Models for Low-cost Sensors under Reference Measurements Scarcity
M V Narayana, Kranthi Kumar Rachvarapu, Devendra Jalihal, Shiva, Nagendra S M

TL;DR
Sens-BERT introduces a transfer learning approach for calibrating low-cost sensors in air quality monitoring, reducing the need for extensive re-calibration and reference data, thus enabling scalable and adaptable sensor calibration.
Contribution
This work presents Sens-BERT, a BERT-inspired model that enables transferability and re-calibration of low-cost sensors using only sensor data for pre-training, reducing dependence on reference measurements.
Findings
Sens-BERT achieves effective calibration with minimal reference data.
The model transfers across sensors of the same sensing principle.
It outperforms traditional calibration methods in adaptability and data efficiency.
Abstract
Low-cost sensors measurements are noisy, which limits large-scale adaptability in airquality monitoirng. Calibration is generally used to get good estimates of air quality measurements out from LCS. In order to do this, LCS sensors are typically co-located with reference stations for some duration. A calibration model is then developed to transfer the LCS sensor measurements to the reference station measurements. Existing works implement the calibration of LCS as an optimization problem in which a model is trained with the data obtained from real-time deployments; later, the trained model is employed to estimate the air quality measurements of that location. However, this approach is sensor-specific and location-specific and needs frequent re-calibration. The re-calibration also needs massive data like initial calibration, which is a cumbersome process in practical scenarios. To…
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Taxonomy
TopicsAir Quality Monitoring and Forecasting · Atmospheric aerosols and clouds · Air Quality and Health Impacts
