A Gas Leakage Detection Device Based on the Technology of TinyML †

Author:

Tsoukas Vasileios1ORCID,Gkogkidis Anargyros1ORCID,Boumpa Eleni1ORCID,Papafotikas Stefanos1,Kakarountas Athanasios1ORCID

Affiliation:

1. Department of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, Greece

Abstract

Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingly costly nature of present neural network systems, it is necessary to transport information to the cloud for intelligent machine analysis. TinyML is a potential technology that has been presented by the research world for building fully independent and safe devices that can gather, analyze, and produce data, without transferring it to distant organizations. This paper describes a gas leakage detection system based on TinyML. The proposed solution can be programmed to identify anomalies and warn occupants via the utilization of the BLE technology, in addition to an incorporated LCD screen. Experiments have been employed to show and assess two distinct test situations. For the first occasion, the smoke detection test case, the system earned an F1-Score of 0.77, whereas the F1-Score for the ammonia test case was 0.70.

Funder

ParICT_CENG: Enhancing ICT research infrastructure in Central Greece to enable processing of Big data from sensor stream, multimedia content, and complex mathematical modeling and simulations

Operational Programme “Competitiveness, Entrepreneurship and Innovation”

the European Union

Publisher

MDPI AG

Subject

General Medicine

Reference49 articles.

1. Monitoring indoor air quality for enhanced occupational health;Rui;J. Med. Syst.,2017

2. Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department;Baqer;Signa Vitae,2023

3. Sreevas, R., Shanmughasundaram, R., and Vadali, V.S. (2019). Development of an IoT based air quality monitoring system. Int. J. Innov. Technol. Explor. Eng., 8.

4. Omidvarborna, H., Kumar, P., Hayward, J., Gupta, M., and Nascimento, E.G.S. (2021). Low-cost air quality sensing towards smart homes. Atmosphere, 12.

5. Zhao, Y., Haddadi, H., and Barnaghi, P. (2023, January 23). Edge Intelligence for Connected In-home Healthcare: Challenges and Visions. Available online: https://iot.ieee.org/newsletter/march-2021/edge-intelligence-for-connected-in-home-healthcare-challenges-and-visions.

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