# Integration of AI in Self-Powered IoT Sensor Systems
## 8 Discussion
The performance of these systems is influenced by local factors such as humidity, extreme temperatures, solar exposure, or other weather conditions, justifying the necessary studies to validate their applicability in various climatic and industrial contexts. In this way, the interest of researchers in these technologies is also justified, even if they are in different regions (China, the United States, Europe, Southeast Asia, etc.). The fields of applicability of self-powered IoT sensors have been identified based on their frequency of occurrence in the scientific literature. According to the bibliometric analysis, the most studied fields from the perspective of integrating AI technologies with self-powered sensors have been identified. The authors acknowledge that this classification is influenced by the uneven distribution of scientific interest. Additionally, fields such as agriculture are underrepresented in the implementation of these technologies, an aspect that the authors highlighted in their paper [268]. Moreover, the authors of the present paper have experience in integrating IoT sensors in the agricultural field [269]. The authors \begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **Emerging** & **Established** \\  & **Technologies** & **Technologies** \\ \hline Standardization & 0 & 1 \\ Interoperability & 0 & 1 \\ Hardware compatibility & 0 & 1 \\ Support for existing protocols & 0 & 1 \\ Market acceptance & 0 & 1 \\ Energy efficiency & 1 & 0 \\ Innovation potential & 1 & 0 \\ Suitability for battery-free operation & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis between emerging IoT communication technologies and existing standardized technologies. will contribute to future research that directly addresses these underrepresented areas, especially in the agricultural field, and encourage other authors to take the same initiatives in their research. The list of future research directions that emerge from the analyses conducted in this paper is as follows:  
* Standardization of the taxonomy of self-powered IoT sensors;
* The integration of less commonly addressed ML algorithms in self-powered system applications;
* Development of end-to-end systems that cover the entire functional chain from capture--local processing--automatic decision-making;
* Creating interconnected IoT platforms between applications to ensure compatibility between infrastructures;
* Conducting comparative studies between self-powering technologies (TENG, piezo, BFC, HEG) tested under real conditions;
* Optimizing AI architectures for energy-limited hardware when Cloud infrastructure is not a viable option;
* Expanding field studies in varied climatic and geographical conditions (tropical, polar, desert, harsh industrial;
* Exploration of emerging subfields such as Federated Learning for distributed training without raw data transfer, neuromorphic computing for event-based inference in ultra-low-power systems, and implementations on microcontrollers powered by renewable sources. In addition to the trends identified in the bibliometric analysis, there are subdomains that redefine, from an architectural point of view, self-powered IoT networks. Among these are the following:  
* Federated learning, which trains ML models in a distributed manner without transferring raw data;
* AI model compression makes it possible to reduce memory space and energy consumption for local inference;
* Implementations on microcontrollers powered exclusively from recoverable energy sources demonstrate the feasibility of applications in real-world environments. * Neuromorphic computing is a research direction that processes event-based AI components in ultra-low-power systems. These directions are underrepresented in the analyzed literature at this stage and may represent future development paths for AI components in self-powered IoT nodes. AI algorithms integrated into self-powered IoT systems highlight a series of specific technical challenges. These are rarely addressed directly in the specialized literature.