# Integration of AI in Self-Powered IoT Sensor Systems
## 9 Conclusions  
The analysis of the works from the specialized literature presented in this study provides an overview of the evolution of research in IoT sensors, emphasizing self-powered sensors and their integration into applications that use AI technologies. Based on the obtained data, the paper identifies the main IoT domains where sensors are used, classifies the types of sensors concerning these domains, and explores the level of academic interest in them concerning AI and ML technologies. The applications of self-powered sensors are discussed in detail, and the AI tools are integrated into data collection, processing, transmission, and interpretation. The study's results outline future research directions. One of the study's most important findings is that the field of IoT cannot be approached without integrating an AI component. Thus, the sensors used in modern applications perform extended functions. In addition to their measuring function, they have become standalone devices that integrate active elements of the connected digital ecosystem, allowing them to provide real-time data and make automated decisions. Decision-making is possible due to integrating AI components, mostly ML algorithms. The literature analysis reveals disparities within the IoT field regarding the degree of adaptation of AI technologies. Most works focus on the healthcare domain, where CNN and LSTM are addressed, leaving other ML algorithms underexplored. Fields such as agriculture have provided limited papers in the specialized literature. This difference indicates a need to expand research on the applicability of AI tools in all IoT domains where self-powered sensors are used. Another point of interest in discussions related to the energy autonomy of these IoT sensors highlights the capability of TENG, BFCs, and HEG sensors to be integrated into standalone applications. The paper highlighted significant gaps regarding the durability aspects of these sensors under extreme conditions, outlining future research directions:  
1. Comparative studies between different energy harvesting technologies by varying the environment in which they perform measurements;
2. Evaluating the impact of power variations on the accuracy of ML models;
3. The development of hybrid power systems that ensure energy redundancy in operation. The studies in the specialized literature that integrate AI models demonstrate their superior capabilities in transforming data to obtain helpful information necessary for automated systems. The most explored models are RF, LSTM, CNN, and SVM. These prove their efficiency in searching for human activity recognition, anomaly detection, industrial fault prediction, image classification, medical monitoring, etc. The performance analysis of these models is mainly based on the accuracy indicator, which raises significant issues related to the need for expanded research on performance indicators that can be used in a comparative analysis of these models. Another research direction in the context of AI-IoT models includes the following:Multicriteria comparative analysis that, in addition to accuracy, includes energy consumption, speed, complexity, etc. ;
2. Exploring optimization methods for AI models to function in tandem with the embedded hardware of self-powered sensors;
3. Comparison between edge computing versus cloud computing implementations in IoT-AI applications that include self-powered sensors. Based on a detailed analysis of the existing research in the literature, the authors derive the following future research directions:  
1. Standardization of IoT sensor taxonomy, as the lack of a universally accepted classification leads to difficulties in comparing research and guiding development strategies. Thus, the scientific community should establish a common classification framework for self-powered sensors. 2. Expanding AI research into underdeveloped fields, such as agriculture, light industry, education, and so on, future research projects should explore customized methods for integrating AI into these domains. 3.