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5G Tiny-ML AI-Based IoT e-Nose System for Hazardous Odor Detection and Classification

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journal contribution
posted on 2025-08-11, 11:22 authored by Rafael Fayos-Jordan, Mohammad Alselek, Mohamed Khadmaoui-Bichouna, Jaume Segura-Garcia, Jose M Alcaraz-Calero, Qi WangQi Wang
More than 2 million people have died in the world in 2019 exposed to hazardous substances. In this context, it is of paramount importance to deliver effective systems to help minimize such number of dead. The usage of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), plays a critical role in the detection of hazardous substances within Industry 4.0. The combination of these technologies enhances the efficiency and accuracy of monitoring harmful materials, improving safety standards and operational processes in industrial environments. AI can be used to analyze vast amounts of data for identifying patterns and predicting potential hazards, while IoT connects various devices and sensors to ensure real-time tracking and prompt responses to risks. This technological synergy is essential for modern industries aiming to create safer and more automated systems. In this work, we propose a 5G AI-IoT electronic nose (e-nose) system for the real-time detection and classification of five hazardous odors. The proposed AI model is very lightweight, and it is affordable for our IoT microcontroller unit. The system has been validated in laboratory conditions but has the advantage and potential impact to be effective in any scenario.<p></p>

Funding

National Edge AI Hub for Real Data: Edge Intelligence for Cyber-disturbances and Data Quality

Engineering and Physical Sciences Research Council

Find out more...

European Union (EU) Horizon Programming Platform for Intelligent Collaborative Deployments Over Heterogeneous Edge-Internet of Things (IoT) Environments (P2CODE) Project (Grant Number: HORIZON-CL4-2022-DATA-01-03/101093069)

10.13039/501100008798-Ministry of Science and Innovation through the Project Agriculture 6.0 (Grant Number: PID2021-126823OB-I00 and TED2021-131040B-C33)

Generalitat Valenciana (Grant Number: CIBEST/2023/101)

History

Author affiliation

College of Science & Engineering Comp' & Math' Sciences

Version

  • AM (Accepted Manuscript)

Published in

IEEE Sensors Journal

Volume

25

Issue

13

Pagination

25439 - 25449

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1530-437X

eissn

1558-1748

Copyright date

2025

Available date

2025-08-11

Language

en

Deposited by

Professor Qi Wang

Deposit date

2025-07-28