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Machine learning

Acceleration of Digitalization Drives Demand for Autonomous Devices

Strategic Insights

— 2 min read

A new study on applied sciences from Petroleum-Gas University of Ploiesti in Romania has shed light on the increasing demand for autonomous devices due to the accelerated digitalization. The research, published in the journal Applied Sciences, suggests that the integration of artificial intelligence (AI) and machine learning (ML) into self-powered Internet of Things (IoT) sensors is a crucial area of focus. The study analyzed the seven main areas of IoT sensor usage, including smart cities, wearable devices, and industrial IoT, and identified the need for an interdisciplinary approach to explore ML algorithms adapted to autonomous sensors.

Key Takeaways:

  • The acceleration of digitalization has led to an increase in demand for autonomous devices, with a particular focus on applications that utilize self-powered IoT sensors.
  • The research identified seven main areas of IoT sensor usage, including smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility.
  • The study found that the most commonly used sensors in these applications are accelerometers, electrocardiograms, humidity sensors, motion sensors, and temperature sensors.
  • The research revealed that AI models in self-powered systems can achieve high accuracies, up to 99.92% in medical and industrial applications.
  • The conclusions drawn from the results underscore the need for an interdisciplinary approach to explore ML algorithms adapted to autonomous sensors.
  • The study proposes future research directions to expand AI's applicability in developing systems that integrate self-powered IoT sensors.

Statistics:

  • The study analyzed a total of 10,275 articles published in the Web of Science database between January 1, 2020, and April 30, 2025.
  • The thematic searches highlighted a consistent number of articles in the health sector, with approximately 2,500 articles related to healthcare IoT.
  • The study found that the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor were the most commonly used sensors in IoT applications.
  • The research identified accuracies of up to 99.92% in medical and industrial applications using AI models in self-powered systems.

Sources:

  • Applied Sciences (journal article) - http://www.mdpi.com/journal/applsci
  • MDPI AG (publisher) - https://doi-org.sdpl.idm.oclc.org/10.3390/app15137008
  • Petroleum-Gas University of Ploiesti, 39 Bucharest Avenue, 100680 Ploiesti, Romania - contact: Cosmina-Mihaela Rosca (Department of Automatic Control, Computers and Electronics)
  • The citation for this news report is: NewsRx. Study Results from Petroleum-Gas University of Ploiesti Provide New Insights into Applied Sciences (Integration of AI in Self-Powered IoT Sensor Systems). Science Letter. August 1, 2025; p 1042.

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