# Integration of AI in Self-Powered IoT Sensor Systems
## 8 Discussion  
This paper stands out from other syntheses by addressing the interdisciplinary combination of self-powered IoT sensors and AI algorithms, particularly ML ones. Unlike other works, this synthesis highlights the collaboration between these modern technologies. Most synthesis papers address either IoT technology, AI applications in Edge systems, the collaboration between energy harvesting technologies, or exclusively energy consumption optimization techniques. Thus, the paper is an original contribution through its integrative and bibliometric structure on the intersection of the three domains, identifying the connections between the key components of this technological ecosystem. This research was guided by three RQs, which were answered based on the bibliometric and thematic analysis conducted from 1 January 2020 to 30 April 2025. The data were extracted using the WoS platform, and the study addressed the RQs to outline future directions for exploring self-powered IoT sensors integrated into AI technologies. The synthesis presents an overview of the current state of the art. The studies predominantly address the issue of TENG-type self-powered sensors and the manner in which they are integrated into applications utilizing ML algorithms such as LSTM, RF, or CNN. Most applications that integrate this combination of technologies are in the fields of healthcare, smart cities, and industrial settings. The fact that most of the works are in the area of human activity recognition, predictive maintenance, real-time monitoring, and intervention with ML models whose accuracy exceeds 95%, demonstrates the ability of these algorithms to contribute to the evolution of the fields in which they operate. However, the authors encourage researchers to explore other ML algorithms, which should be intensively studied in other fields where they are underdeveloped. The first WoS search aimed to identify the areas of use for IoT sensors, identifying 1739 articles. These articles extracted data regarding the seven regions of applicability smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. As part of this analysis, the authors also ranked the most commonly used types of sensors: temperature sensors in over 20 papers, electrocardiogram sensors in 15 papers, and accelerometers in 13 papers. The second search focused on integrating AI technologies in self-powered sensors, yielding 52 articles with an increasing distribution from 1 January 2020 to 30 April 2025, reflecting the steadily growing scientific interest in this field. However, the number of articles for such a vast field is minimal, which leads to the idea of the necessity for the maturation of the concept of self-powered sensors integrated with AI. Of the 52 articles, 70% were contribution articles, and 30% were reviews. The WoS scientific domain predominantly identifies these articles as multidisciplinary materials (39 articles), followed by nanotechnology (25), applied physics (25), chemistry-physics (23), and electrical and electronic engineering, with only 6 works. These values are at least surprising given the fact that sensors mostly have an electrical component and are integrated into systems associated with the field of electrical and electronic engineering, which reinforces the idea of the need for the concept itself to mature and for a much larger volume of scientific contributions in the field to be generated. Regarding geographical distribution, China has the highest contributions, accounting for approximately 88.4% of the total works, and the top publisher for disseminating the results is Elsevier, which has 19 articles. The VosViewer analysis identifies the semantic connections between key concepts, such as self-powered energy and AI technologies.