Deviation Prediction and Correction on Low-Cost Atmospheric Pressure Sensors using a Machine-Learning Algorithm

Tiago de Araújo, Lígia Silva, Adriano Moreira

2020

Abstract

Atmospheric pressure sensors are important devices for several applications, including environment monitoring and indoor positioning tracking systems. This paper proposes a method to enhance the quality of data obtained from low-cost atmospheric pressure sensors using a machine learning algorithm to predict the error behaviour. By using the extremely Randomized Trees algorithm, a model was trained with a reference sensor data for temperature and humidity and with all low-cost sensor datasets that were co-located into an artificial climatic chamber that simulated different climatic situations. Fifteen low-cost environmental sensor units, composed by five different models, were considered. They measure – together – temperature, relative humidity and atmospheric pressure. In the evaluation, three categories of output metrics were considered: raw; trained by the independent sensor data; and trained by the low-cost sensor data. The model trained by the reference sensor was able to reduce the Mean Absolute Error (MAE) between atmospheric pressure sensor pairs by up to 67%, while the same ensemble trained with all low-cost data was able to reduce the MAE by up to 98%. These results suggest that low-cost environmental sensors can be a good asset if their data are properly processed.

Paper Citation

in Harvard Style

de Araújo T., Silva L. and Moreira A. (2020). Deviation Prediction and Correction on Low-Cost Atmospheric Pressure Sensors using a Machine-Learning Algorithm. In Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-403-9, pages 41-51. DOI: 10.5220/0008968400410051

in Bibtex Style

@conference{sensornets20,
author={Tiago de Araújo and Lígia Silva and Adriano Moreira},
title={Deviation Prediction and Correction on Low-Cost Atmospheric Pressure Sensors using a Machine-Learning Algorithm},
booktitle={Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2020},
pages={41-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008968400410051},
isbn={978-989-758-403-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Deviation Prediction and Correction on Low-Cost Atmospheric Pressure Sensors using a Machine-Learning Algorithm
SN - 978-989-758-403-9
AU - de Araújo T.
AU - Silva L.
AU - Moreira A.
PY - 2020
SP - 41
EP - 51
DO - 10.5220/0008968400410051