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| Title | Integration Of Ai In Self-Powered Iot Sensor Systems |
|---|---|
| ID_Doc | 32113 |
| Authors | Rosca C.-M.; Stancu A. |
| Year | 2025 |
| Published | Applied Sciences (Switzerland), 15, 13 |
| DOI | http://dx.doi.org/10.3390/app15137008 |
| Abstract | 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. © 2025 by the authors. |
| Author Keywords | data-driven systems; environmental monitoring; healthcare IoT; industrial IoT; IoT sensors; machine learning algorithms; self-powered sensors; smart cities; smart homes; smart mobility; wearable devices |
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