TinyML Models for a Low-cost Air Quality Monitoring Device

I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)1-4
Number of pages4
JournalIEEE Sensors Letters
DOIs
StatePublished - Sep 14 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-18
Acknowledgements: 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.

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