A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup>
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 networ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/11/2/45 |
| _version_ | 1797603367709573120 |
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| author | Vasileios Tsoukas Anargyros Gkogkidis Eleni Boumpa Stefanos Papafotikas Athanasios Kakarountas |
| author_facet | Vasileios Tsoukas Anargyros Gkogkidis Eleni Boumpa Stefanos Papafotikas Athanasios Kakarountas |
| author_sort | Vasileios Tsoukas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:29:11Z |
| format | Article |
| id | doaj.art-84c31c4b942e455db292dc39ea313137 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-11T04:29:11Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-84c31c4b942e455db292dc39ea3131372023-11-17T21:36:00ZengMDPI AGTechnologies2227-70802023-03-011124510.3390/technologies11020045A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup>Vasileios Tsoukas0Anargyros Gkogkidis1Eleni Boumpa2Stefanos Papafotikas3Athanasios Kakarountas4Department of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, GreeceDepartment of Computer Science and Biomedical Informatics, University of Thessaly, 35 131 Lamia, GreeceInternet 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.https://www.mdpi.com/2227-7080/11/2/45TinyMLgas detectionmachine learningdeep learninginternet of thingssmart homes |
| spellingShingle | Vasileios Tsoukas Anargyros Gkogkidis Eleni Boumpa Stefanos Papafotikas Athanasios Kakarountas A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup> Technologies TinyML gas detection machine learning deep learning internet of things smart homes |
| title | A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup> |
| title_full | A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup> |
| title_fullStr | A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup> |
| title_full_unstemmed | A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup> |
| title_short | A Gas Leakage Detection Device Based on the Technology of TinyML <sup>†</sup> |
| title_sort | gas leakage detection device based on the technology of tinyml sup † sup |
| topic | TinyML gas detection machine learning deep learning internet of things smart homes |
| url | https://www.mdpi.com/2227-7080/11/2/45 |
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