# Integration of AI in Self-Powered IoT Sensor Systems
## 1 Introduction  
Sensors initially used as measuring elements have become components of intelligent systems in the contemporary world through an Internet of Things (IoT) approach. Thus, IoT sensors provide real-time data in various physical environments, which is made possible by combining artificial intelligence (AI) technologies with other modern data acquisition and processing technologies. IoT sensors are processed through AI algorithms to make automated decisions for future predictions or to optimize various processes across many fields. This review explores how the convergence between IoT sensors and modern AI technologies contributes to transforming the surrounding world through an intelligent digital network. The technological evolution of recent years has taken place under the simultaneous development of self-powered sensors, AI algorithms, and IoT networks. However, these technologies have been treated in the specialized literature as separate components. The present paper aims to analyze the synergistic collaboration between these components. This study provides an overview of the current state of research and trends at a moment of technological transition that impacts various fields of activity. Self-powered IoT systems that use energy harvesting technologies represent a challenge in the context of AI due to constraints in energy, space, processing, connectivity, and communication. In the case of minimalist hardware systems that have self-powered sensors, AI algorithms based on ML require computational resources that are usually insufficient at the level of available hardware. Under these conditions, devices in this category have the ML component hosted in the cloud. In order to access cloud resources, the devices require Internet connectivity. Instability of energy sources, such as variation in vibration, illumination, or movement, affects the reliability of the continuous execution of these algorithms. For these reasons, the field is insufficiently explored, given that the majority of research addresses individual elements. For example, some works focus on optimizing energy consumption, while others address the performance of AI models, without analyzing the necessary trade-off between the two. On the other hand, the lack of integration standards and the difficulties in testing under real conditions delay the maturity of this technological intersection. Therefore, a systematic literature review addressing these components simultaneously will allow readers to understand current limitations and identify viable directions for further development. The present paper has as its main scope to carry out a bibliometric and thematic analysis of the way in which self-powered IoT sensors interact with AI technologies, especially machine learning ones. The paper does not limit itself exclusively to hardware components or to AI algorithms in isolation. The paper highlights trends in the literature, focusing on hardware-software integration challenges together with application directions within IoT contexts. The paper addresses both technological aspects, mentioning types of sensors, energy sources, and ML models, as well as the functional framework in which these are implemented. The purpose of such an approach is to provide the reader with an overview of the maturity and viability of these solutions in real-world applications. By analyzing the specialized literature, it was found that domains group sensors, and within each domain, there is a taxonomy of sensors based on the area in which they are used and the specific type of sensor. According to [PERSON] et al. [1], the classification includes the industrial domain, smart cities, and healthcare. The same paper states that the industrial field comprises the agricultural, logistics, and plant floor domains, while smart cities include transport, buildings, and environment, and healthcare includes monitoring and management. [PERSON] et al. [2] take a different approach to classifying sensors, emphasizing a classification based on their size and purpose.