# Integration of AI in Self-Powered IoT Sensor Systems
## 6 Self-Powered IoT Sensors in the AI Field
### Bibliometric and Systematic Thematic Analysis for Self-Powered Sensors and AI Components  
The temporal evolution of publications demonstrates, through the obtained results, a growing international interest in self-powered sensing technologies integrated with AI components. In 2020, the search yielded two publications, reaching 16 publications in this field by 2024. The first four months of 2025 led to seven publications, which is estimated to increase the number of publications in the field. The results are presented in Figure 6, where a constant rise in publications can be observed. Regarding the type of publications, the distribution shows 37 research papers and 16 review papers, according to Figure 7. The fact that most publications are original articles, namely 69.81%, indicates an intense experimental research activity. Additionally, 30.19% of review articles provide a synthetic perspective on progress. The publications are classified into various WoS categories, most in multidisciplinary material science, specifically 39. The following fields are nanotechnology and applied physics, with 25 papers each, and physical chemistry, with 23 papers. The last two positions correspond to multidisciplinary chemistry with 14 articles and electrical and electronics engineering with 6, according to Figure 8. Thus, raw materials occupy the top position in the categories of powered sensors, indicating potential for development or a necessity for growth in other fields. Figure 6: Evolution of published papers by year (2020–2025). Figure 7: Number of publications by type (2020–2025). The geographical distribution by country places China first, with 46 studies, followed by the USA with 9, and Singapore with 6. Figure 9 shows the low interest of other countries in these top technologies for the evolution of autonomous devices. The main publications that disseminated these results were Elsevier, with 19 articles, followed by Wiley, with 17, and the American Chemical Society, with 6. Paradoxically, IEEE,  
Figure 8: Number of publications by WoS category (2020–2025). Figure 9: Number of publications by country (2020–2025). Figure 11: Distribution of cited reference counts for publications (2020–2025) [152,163,178,188,197–244]. The value analysis of the keyword clusters obtained using the VOSViewer 1.6.20 tool represents a way to identify the main themes in the publication collection based on the frequency of keyword occurrences. This analysis highlights the connections between key concepts and identifies the dominant trends in the researched field, as seen in Figure 14. In this analysis, 316 keywords were used, of which the 12 keywords that appeared at least five times were selected. These keywords were subsequently grouped into three distinct clusters, each highlighting a subdomain or a main research direction. This way, a systematic picture of the thematic structure of the scientific literature on self-powered sensors integrated into AI applications was obtained. The first cluster (colored in red) contains six elements (energy harvesting, sensor, system, transparent, performance, triboelectric nanogenerator) and focuses on the sensors'  
Figure 14: Keyword co-occurrence network for self-powered sensing and AI. Figure 13: Number of publications by research area (2020–2025). energy and performance. This cluster is associated with collecting ambient mechanical energy through self-powered sensors, including TENG sensors in this category. Within the cluster, performance elements regarding the functionality of these devices without an external power supply are included. The term transparent suggests an interest in transparent or flexible materials used by these devices, which are usually in the wearable or innovative category. The second cluster (colored in green) contains five elements (artificial intelligence, energy, nanogenerators, self-powered sensing, triboelectric nanogenerator) and is associated with the combination of AI and TENG.