The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this d...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6516 |
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| author | Jan A. Persson Joseph Bugeja Paul Davidsson Johan Holmberg Victor R. Kebande Radu-Casian Mihailescu Arezoo Sarkheyli-Hägele Agnes Tegen |
| author_facet | Jan A. Persson Joseph Bugeja Paul Davidsson Johan Holmberg Victor R. Kebande Radu-Casian Mihailescu Arezoo Sarkheyli-Hägele Agnes Tegen |
| author_sort | Jan A. Persson |
| collection | DOAJ |
| description | This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed. |
| first_indexed | 2024-03-11T03:11:44Z |
| format | Article |
| id | doaj.art-b1eadea1d6e24ebaa95bf1c1f0b84523 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:11:44Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b1eadea1d6e24ebaa95bf1c1f0b845232023-11-18T07:33:11ZengMDPI AGApplied Sciences2076-34172023-05-011311651610.3390/app13116516The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine LearningJan A. Persson0Joseph Bugeja1Paul Davidsson2Johan Holmberg3Victor R. Kebande4Radu-Casian Mihailescu5Arezoo Sarkheyli-Hägele6Agnes Tegen7Internet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenInternet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, SwedenThis paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.https://www.mdpi.com/2076-3417/13/11/6516virtual sensorssoft sensorssensor data fusiononline learninginteractive machine learningtransfer learning |
| spellingShingle | Jan A. Persson Joseph Bugeja Paul Davidsson Johan Holmberg Victor R. Kebande Radu-Casian Mihailescu Arezoo Sarkheyli-Hägele Agnes Tegen The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning Applied Sciences virtual sensors soft sensors sensor data fusion online learning interactive machine learning transfer learning |
| title | The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning |
| title_full | The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning |
| title_fullStr | The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning |
| title_full_unstemmed | The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning |
| title_short | The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning |
| title_sort | concept of interactive dynamic intelligent virtual sensors idivs bridging the gap between sensors services and users through machine learning |
| topic | virtual sensors soft sensors sensor data fusion online learning interactive machine learning transfer learning |
| url | https://www.mdpi.com/2076-3417/13/11/6516 |
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