# Integration of AI in Self-Powered IoT Sensor Systems
## 8 Discussion
This highlights the interdisciplinary nature of these works, emphasizing the connection between TENG and AI, which demonstrates that modern technologies cannot function independently, that is, in isolation, but require an interdisciplinary approach. This analysis prioritizes future research directions, which indicate the need for new flexible and biocompatible materials that can be integrated into self-powered systems as well as the second research direction addresses the necessity of ML or DL algorithms to optimize the interpretation of data generated by sensors. A systematic perspective is provided on collaboration between AI algorithms and self-powered sensors by grouping keywords into clusters. This analysis has the following practical implications:  
1. For researchers, it serves as a basis for identifying gaps in the specialized literature and outlining future research directions. 2. For developers, it is a way to plan projects based on dominant trends, such as replacing traditional sensors with TENG sensors, which integrate very easily into wearable or IoT applications. 3. It provides an overview of the research priorities to which funders should allocate additional funding resources to encourage technological advancement. 4. In the industrial area, it facilitates the technological transfer of research results into commercial products by integrating powered sensors in medical, industrial, or urban fields, identified as reference points in this work. The VosViewer analysis is a methodology through which information was extracted from existing textual datasets in research disseminated in various publications, and the identified clusters highlighted three primary directions:  
1. Harvesting self-powered energy through the identification of new materials. 2. Integrating AI algorithms for interpreting and optimizing sensor-generated data. 3. Self-powered sensors should be widely integrated into applications across various fields. The third search focused on the number of articles that exclusively address ML algorithms in the context of self-powered sensors. The result provided 61 articles with remarkable accuracy, most having values over 90%. Many of these articles operated in the medical field for psychological therapy, arrhythmia classification, medical rehabilitation, assisting users with body posture, or movement monitoring. Beyond this achievement regarding the enhanced performance of ML algorithms, the research highlighted technologies capable of saving energy by up to 66.8% and operating under extreme conditions. AI models, especially CNN, LSTM, and RF, are intensively studied in low-energy consumption applications. The very low research values in the literature show many fields but also unexplored ML models. The annual increase in publications shows an emerging trend. Still, their volume is far too small compared to the number of ML models that need to be explored and the integration possibilities of these battery-free sensors into a vast field of applications. The study's limitations concern the single data source, WoS, which, although includes most of the literature research, may exclude some Scopus-type research or local databases. Another limitation of the study concerns the research analysis and the quality of ML models relating to accuracy, although the models are evaluated based on multiple performance indicators. RQ1 analyzes the areas not sufficiently explored in the context of AI technologies, particularly through the ML component. The analysis of keyword co-occurrence and the distribution of articles in specific domains led to identifying seven areas, four of which are considered dominant: smart cities, wearable devices, healthcare IoT, and industrial IoT. In contrast, agriculture, education, environmental monitoring, and smart homes are insufficiently explored in the context of AI. The lack of research indicates opportunities to integrate these sensors into prediction, monitoring, identification, or optimization tasks for crop production or disease prevention at the crop level.