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TinyML_Models_for_a_Low-cost_Air_Quality_Monitoring_Device.pdf
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Accepted Manuscript
Type
ArticleKAUST Department
Computer Science ProgramExtreme Computing Research Center
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Date
2023-09-14Permanent link to this record
http://hdl.handle.net/10754/694466
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Low-cost air quality monitoring devices can provide high-density spatiotemporal pollution data, thus offering a better opportunity to apply machine learning. Low-cost sensor nodes usually utilize microcontrollers as the main processors, and tinyML brings machine learning (ML) models to these resource-constrained devices. In this letter, we reported the development of a low-cost air quality monitoring device with embedded tinyML models. We deployed two tinyML models on a single microcontroller and performed two tasks: predicting air quality and power parameters (using model predictor) and imputing missing features (using model imputer). The proposed model predictor can estimate parameters with a coefficient of determination above 0.70, and the model imputer effectively estimates the testing data when missing rates are below 80%. By performing the post-training quantization technique, we can further reduce the model size but slightly degrade the accuracies.Citation
Wardana, I. N. K., Fahmy, S. A., & Gardner, J. W. (2023). TinyML Models for a Low-cost Air Quality Monitoring Device. IEEE Sensors Letters, 1–4. https://doi.org/10.1109/lsens.2023.3315249Sponsors
This work was supported in part by Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia under grant number Ref: S-1027/LPDP.4/2019.Journal
IEEE Sensors LettersAdditional Links
https://ieeexplore.ieee.org/document/10251587/ae974a485f413a2113503eed53cd6c53
10.1109/lsens.2023.3315249