# Integration of AI in Self-Powered IoT Sensor Systems
## 2 Methodology  
To answer the RQs, this paper analyzes specialized works through a detailed literature review and subsequently conducts a systematic analysis based on quantitative and qualitative research methods. Beyond the specialized literature review, the paper employs WoS analysis to identify IoT domains. Figure 1 presents the overall workflow of this study. For this, a search is conducted in this database corresponding to the period between 1 January 2020 and 30 April 2025, using the query TS = (\"IoT sensors\") AND PY = (2020-2025), with the primary goal of identifying the most frequent domains associated with IoT sensors. The authors classified the fields into smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. Subsequently, the sensors were established according to the measured input, and articles specifically addressing these types of sensors in the context of IoT were identified. The literature review on the link between IoT sensors and AI was conducted based on the query TS = (\"self-powered sensing\" AND (\"artificial intelligence\" OR \"AI\")) AND PY = (2020-2025). The models in which IoT sensors are addressed in the scientific literature concerning AI were evaluated at this stage. The thematic and bibliometric analysis conducted using the VOSviewer 1.6.20 software highlighted an analysis of the co-occurrence of keywords extracted from the selected articles. This allowed for identifying dominant thematic clusters to highlight the connections between the concepts of self-powered sensors and AI technology. The results are implemented in a detailed framework where the studies are compared regarding performance indices. Future research directions are identified along with recommendations for technological development in the field of self-powered IoT sensors, and ML, through additional filtering that includes only works that explicitly address ML algorithms (TS = (\"self-powered sensing\" AND (\"machine learning\" OR \"ML\")) AND PY = (2020-2025)). Thus, the publications were analyzed using the algorithms and the reported performance metrics. A schematic of the WoS filtering strategy used to select and categorize the papers is presented in Figure 2.  
Figure 1: Research methodology flowchart. Figure 2 presents the workflow diagram illustrating the WoS filtering strategy. The methodology includes three sequential queries to extract articles on IoT sensors, followed by filters related to AI and ML. This approach enables a targeted bibliometric and thematic analysis of research trends between 2020 and 2025. The search strategy is based on the combinations of key terms presented in Figure 2. These terms were extracted by the authors of this paper from the specialized literature and included in the queries performed in WoS. These terms were combined using the logical operators AND and OR to generate detailed searches in the specialized literature regarding self-powered sensors. The authors opted for the WoS database because it has a broad coverage of indexed sources and offers the possibility of extracting metadata for bibliometric analysis. Methodological limitations include the following:  
* The possible exclusion of works indexed only in alternative databases such as Scopus or IEEE Xplore;
* The volatility of citation numbers for recent work;
* The fact that the analysis is based exclusively on articles published in English;
* The inclusion of review articles in the analysis. To synthesize the interaction of self-powered sensors within the context of IoT domains, Figure 3 proposes a conceptual framework for integrating self-powered sensors into IoT applications that incorporate AI components. In this model, self-powered sensors act as autonomous data generators. They collect energy from the environment and transmit multimodal data to a cloud service. AI modules interpret these streams using ML algorithms. These algorithms extract patterns and perform classifications or predictions based on data that has been previously pre-processed.