Integration of AI in Self-Powered IoT Sensor Systems
The acceleration of digitalization has caused an increase in demand for autonomous devices. In this paper, the technologies of artificial intelligence (AI), and especially machine learning (ML), integrated into applications that use self-powered Internet of Things (IoT) sensors are analyzed. The stu...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7008 |
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| author | Cosmina-Mihaela Rosca Adrian Stancu |
| author_facet | Cosmina-Mihaela Rosca Adrian Stancu |
| author_sort | Cosmina-Mihaela Rosca |
| collection | DOAJ |
| description | The acceleration of digitalization has caused an increase in demand for autonomous devices. In this paper, the technologies of artificial intelligence (AI), and especially machine learning (ML), integrated into applications that use self-powered Internet of Things (IoT) sensors are analyzed. The study addresses the issue of the lack of a standardized classification of IoT domains and the uneven distribution of AI integration in these domains. The systematic bibliometric analysis of the scientific literature between 1 January 2020 and 30 April 2025, using the Web of Science database, outlines the seven main areas of IoT sensor usage: smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. The thematic searches highlight the consistent number of articles in the health sector and the underrepresentation of other areas, such as agriculture. The study identifies that the most commonly used sensors are the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor, and analyzes the performance of AI models in self-powered systems, identifying accuracies that can reach up to 99.92% in medical and industrial applications. The conclusions drawn from these results underscore the need for an interdisciplinary approach and detailed exploration of ML algorithms to be adapted to the hardware infrastructures of autonomous sensors. The paper proposes future research directions to expand AI’s applicability in developing systems that integrate self-powered IoT sensors. The paper lays the groundwork for future projects in this field, serving as a reference for researchers who wish to explore these areas. |
| format | Article |
| id | doaj-art-e3a8714bb1ed49af963ff64c8add3d1f |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e3a8714bb1ed49af963ff64c8add3d1f2025-08-20T02:35:51ZengMDPI AGApplied Sciences2076-34172025-06-011513700810.3390/app15137008Integration of AI in Self-Powered IoT Sensor SystemsCosmina-Mihaela Rosca0Adrian Stancu1Department of Automatic Control, Computers, and Electronics, Faculty of Mechanical and Electrical Engineering, Petroleum-Gas University of Ploiesti, 39 Bucharest Avenue, 100680 Ploiesti, RomaniaDepartment of Business Administration, Faculty of Economic Sciences, Petroleum-Gas University of Ploiesti, 39 Bucharest Avenue, 100680 Ploiesti, RomaniaThe acceleration of digitalization has caused an increase in demand for autonomous devices. In this paper, the technologies of artificial intelligence (AI), and especially machine learning (ML), integrated into applications that use self-powered Internet of Things (IoT) sensors are analyzed. The study addresses the issue of the lack of a standardized classification of IoT domains and the uneven distribution of AI integration in these domains. The systematic bibliometric analysis of the scientific literature between 1 January 2020 and 30 April 2025, using the Web of Science database, outlines the seven main areas of IoT sensor usage: smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. The thematic searches highlight the consistent number of articles in the health sector and the underrepresentation of other areas, such as agriculture. The study identifies that the most commonly used sensors are the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor, and analyzes the performance of AI models in self-powered systems, identifying accuracies that can reach up to 99.92% in medical and industrial applications. The conclusions drawn from these results underscore the need for an interdisciplinary approach and detailed exploration of ML algorithms to be adapted to the hardware infrastructures of autonomous sensors. The paper proposes future research directions to expand AI’s applicability in developing systems that integrate self-powered IoT sensors. The paper lays the groundwork for future projects in this field, serving as a reference for researchers who wish to explore these areas.https://www.mdpi.com/2076-3417/15/13/7008machine learning algorithmsIoT sensorsdata-driven systemsself-powered sensorssmart citieswearable devices |
| spellingShingle | Cosmina-Mihaela Rosca Adrian Stancu Integration of AI in Self-Powered IoT Sensor Systems Applied Sciences machine learning algorithms IoT sensors data-driven systems self-powered sensors smart cities wearable devices |
| title | Integration of AI in Self-Powered IoT Sensor Systems |
| title_full | Integration of AI in Self-Powered IoT Sensor Systems |
| title_fullStr | Integration of AI in Self-Powered IoT Sensor Systems |
| title_full_unstemmed | Integration of AI in Self-Powered IoT Sensor Systems |
| title_short | Integration of AI in Self-Powered IoT Sensor Systems |
| title_sort | integration of ai in self powered iot sensor systems |
| topic | machine learning algorithms IoT sensors data-driven systems self-powered sensors smart cities wearable devices |
| url | https://www.mdpi.com/2076-3417/15/13/7008 |
| work_keys_str_mv | AT cosminamihaelarosca integrationofaiinselfpowerediotsensorsystems AT adrianstancu integrationofaiinselfpowerediotsensorsystems |