# Integration of AI in Self-Powered IoT Sensor Systems
## 3 Domain Identification in the Literature Using IoT Sensors  
For a rigorous analysis of the specialized literature, WoS searches were conducted for 2020-2025, searching for all review articles focusing on IoT sensors. From the analysis of the specialized literature, seven reference domains were identified in which IoT sensors are used. The seven domains are smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. Regarding the field of smart cities, most papers discuss the use of IoT sensors in traffic monitoring, urban infrastructure, the development of parking at the urban level and in airports, and the transformation of urban services through modern technologies specific to AI and big data. Refs. [6; 7] analyze how smart cities provide better air quality, improving the population's health. It monitors early disease detection and personalized treatments in real-time through IoT sensors connected to mobile applications. [PERSON] et al. [8] study the integration of IoT sensors in monitoring traffic, drainage, and urban mobility by integrating AI technologies, specifically deep learning (DL), a component of ML. Following these studies, [PERSON] et al. [9] collect data from IoT sensors, focusing on mobility and infrastructure. Also at the level of AI technology, [PERSON] and [PERSON] [10] detail the transformation of urban traffic from the perspective of IoT and AI, and analyzes optimization methods regarding the operation of traffic lights and the response to various emergencies. Refs. [11; 12] conduct systematic analyses in the specialized literature regarding smart parking systems for identifying free spaces using cameras that provide data to ML algorithms. Similarly, [PERSON] et al. [13] examine a perspective on smart airports by applying IoT sensors for the automation, security, and real-time monitoring of airport traffic. [PERSON] et al. [14] analyze the impact of smart cities on public health using IoT sensors and mobile applications to improve access to medical services. Also, in an AI approach, [PERSON] et al. [15] and [PERSON] et al. [16] discuss modeling urban forms to address challenges related to health, security, mobility, and other aspects that affect the population's quality of life. Wearable devices have generated many articles in the literature that analyze the role of self-powered wearable sensors. Health monitoring applications, such as fall detection and arterial pulse, step count monitoring, and heart rate monitoring, have facilitated the study of IoT sensors concerning AI technologies, simultaneously emphasizing energy autonomy [17]. In this context, [PERSON] et al. [6] analyze self-powered wearable sensors and TENG technologies for medical applications and human-machine interfaces. In continuation of this research, [PERSON] and [PERSON] [18] present a detailed analysis of TENGs in health monitoring applications, specifically targeting fall detection and pulse monitoring. The discussion on energy conversion in TENG systems for wearable devices is identified by [PERSON] and [PERSON] [18]. It mentions the conversion technologies and their performance. Another reference work for wearable devices concerning IoT is the research by [PERSON] et al. [19], which analyzes the applications of wearable sensors in health, identifying a series of gaps, including energy efficiency [20]. The Industrial IoT field investigates predictive maintenance (PdM) approaches, cybersecurity for industrial infrastructure, and modern technologies such as ML and IoT sensors to ensure process safety. [PERSON] et al. [21] use a communication protocol to transmit IoT-specific wireless data, Long Range Wide Area Network (LoRaWAN), and IoT sensors to monitor underground mines. Another direction in the industrial sector is outlined by [PERSON] et al. [22], who review cybersecurity for industrial water and wastewater systems. ML technologies in industrial PdM are discussed by [PERSON] et al.