# Integration of AI in Self-Powered IoT Sensor Systems
## 1 Introduction
Thus, sensors are classified according to Industry 4.0 into nanosensors, microsensors, nuclear sensors, and passive sensors. In the past, sensors represented measuring devices. With the evolution of technology, they have become active components of an innovative ecosystem that, in most cases, is connected to an infrastructure that allows real-time data transmission in the context of automation. Therefore, sensors are mostly treated from the perspective of IoT applications as measuring devices with advanced capabilities. According to [PERSON] et al. [3], IoT applications are divided into seven domains: transportation, smart homes, markets, agriculture, healthcare, and smart cities. [PERSON] and [PERSON] [4] provide a rigorous classification of sensors based on most of the relevant criteria for the automation field. Thus, sensors are classified based on the type of physical quantity measured, dividing them into acoustic, electrical, optical, biological,magnetic, mechanical, chemical, thermal, and radiation sensors. Furthermore, in the same research [4], sensors are grouped into active and passive based on their operating principle. Also, depending on the physical contact typology, sensors are categorized into contact and non-contact sensors. Sensors can also be classified into analog and digital sensors based on signal output. Also, in the research by [PERSON] and [PERSON] [4], sensors are organized based on signal conversion into biological, physical, and chemical sensors. Additionally, sensors can also be grouped based on application, specificity, and comparability. In the context of IoT, the classification based on application is considered the reference. Thus, [PERSON] and [PERSON] [4] identify the following industries as domains in which sensors operate: agriculture, automotive, engineering, domestic appliance, information and telecommunication, health and medicine, marine, defense, space, transportation, energy, and manufacturing. [PERSON] et al. [5] divide the specific domains of smart cities into energy, education, traffic, transport, city services, infrastructure, health, home, and environment. Still, upon closer analysis, it can be observed that these elements are standalone domains. Analyzing the matters from the specialized literature, it is found that most sensors are classified based on their field of applicability. Still, this approach is disparate, lacking a standard classification to which most works can refer. Moreover, as already mentioned, most modern sensors are of the IoT type. Under these conditions, the issue arises regarding the level of research on AI technologies for IoT sensors in each field of activity. This research aims to answer the following research questions (RQs). 1. What IoT domains are not explored in the context of AI technologies, especially those based on machine learning (ML)? 2. What future research directions should focus on IoT sensors, AI technologies, and the integration of ML in applications that use these sensors? 3. What field should be the primary focus of interest for researchers to accelerate the evolution of AI techniques applied in combination with self-powered IoT sensors? To answer these questions, the authors have the following contributions:  
1. Identifying IoT domains in the literature through a Web of Science (WoS) analysis to propose standardizing sensor classification domains with IoT technology. 2. Distinguishing the reference sensors for each IoT domain and the level of interest of researchers in these types of sensors over the past five years, namely 2020-2025. 3. Exploring how many works in the specialized literature address each type of sensor in explicit relation to IoT for the previously mentioned period. 4. Adding restrictions to sensor analysis by incorporating AI constraints to outline how research on IoT sensors has been reported about AI technologies in the last five years. 5. Narrowing down to target a specific direction of AI that focuses on ML algorithms.