Enabling Artificial Intelligent Virtual Sensors in an IoT Environment
The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degra...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/3/1328 |
| _version_ | 1797623254495526912 |
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| author | Georgios Stavropoulos John Violos Stylianos Tsanakas Aris Leivadeas |
| author_facet | Georgios Stavropoulos John Violos Stylianos Tsanakas Aris Leivadeas |
| author_sort | Georgios Stavropoulos |
| collection | DOAJ |
| description | The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology. |
| first_indexed | 2024-03-11T09:26:04Z |
| format | Article |
| id | doaj.art-55c4a24a73bd474fabcb99db7e48b0ad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:26:04Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-55c4a24a73bd474fabcb99db7e48b0ad2023-11-16T17:59:28ZengMDPI AGSensors1424-82202023-01-01233132810.3390/s23031328Enabling Artificial Intelligent Virtual Sensors in an IoT EnvironmentGeorgios Stavropoulos0John Violos1Stylianos Tsanakas2Aris Leivadeas3Department of Informatics and Telematics, Harokopio University of Athens, 17778 Tavros, GreeceDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaSchool of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaThe demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology.https://www.mdpi.com/1424-8220/23/3/1328virtual sensorsmachine learningregressionInternet of ThingsIoT platformsmart homes |
| spellingShingle | Georgios Stavropoulos John Violos Stylianos Tsanakas Aris Leivadeas Enabling Artificial Intelligent Virtual Sensors in an IoT Environment Sensors virtual sensors machine learning regression Internet of Things IoT platform smart homes |
| title | Enabling Artificial Intelligent Virtual Sensors in an IoT Environment |
| title_full | Enabling Artificial Intelligent Virtual Sensors in an IoT Environment |
| title_fullStr | Enabling Artificial Intelligent Virtual Sensors in an IoT Environment |
| title_full_unstemmed | Enabling Artificial Intelligent Virtual Sensors in an IoT Environment |
| title_short | Enabling Artificial Intelligent Virtual Sensors in an IoT Environment |
| title_sort | enabling artificial intelligent virtual sensors in an iot environment |
| topic | virtual sensors machine learning regression Internet of Things IoT platform smart homes |
| url | https://www.mdpi.com/1424-8220/23/3/1328 |
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