# Integration of AI in Self-Powered IoT Sensor Systems
## 4 IoT Sensors Identification Based on Measured Input  
IoT sensors are classified into various types and used in the 7 previously identified fields, depending on the nature of the measured input parameter. Table 1 summarizes a series of articles from the literature by running the WoS query TS = ((\"IoT sensors\")) AND PY = (2020-2025), which yielded 1739 results. From these articles, those materials that have a specific type of sensor as their central element were selected. Table 1 shows that the most commonly used sensors are the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Type** & **References** \\ \hline Accelerometer & [43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60] \\ Air quality sensor & [56; 57; 58; 59; 60] \\ Blood pressure sensor & [61] \\ Chemical sensor & [62; 63; 64] \\ Color sensor & [65] \\ Contact sensor & [66] \\ Current sensor & [67; 68; 69] \\ Electrochemical sensor & [63] \\ Electrocardiogram & [70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84] \\ Electroencephalogram & [85] \\ Flow meter & [86] \\ Gas sensor & [63; 64; 87; 88; 89; 90] \\ Gyroscope & [43; 44; 51; 53; 54] \\ Humidity sensor & [91; 92; 93; 94; 95; 96; 97; 98; 99; 100] \\ Infrared sensor & [101] \\ Level sensor & [102] \\ Light sensor & [103; 104; 105] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types of IoT sensors. From the totality of materials synthesized in Table 1, the following analyzes the first five types of sensors that have provided the highest number of articles in the specialized literature. The temperature sensor is the most studied, due to its degree of use in all IoT fields. From the earliest days of human life, temperature sensors have been considered essential monitoring equipment [120; 143]. In this regard, there is extensive research on real-time body temperature monitoring with automatic alerts sent to emergency teams [121]. These sensors were considered essential even during the COVID-19 pandemic [123]. One of the most bizarre applications identified in the literature was the research [133] that uses IoT and DL to identify Egyptian cobra bites in real-time, achieving an accuracy of 90.9%. Another sector where temperature sensors are used is the agricultural field. The approaches in this field are varied, ranging from real-time fire prediction [126], continuing with the optimization of decisions regarding crops [135], and ending with the study of energy savings to extend the battery life of IoT devices by over 75% [125]. Aquaculture is a distinct branch of agriculture that also uses temperature sensors [128] to monitor water quality and prevent diseases. The transportation sector also uses temperature sensors [122], analyzing the integration of IoT and composite materials for real-time monitoring of railway infrastructure health [46]. Most of these studies also integrate AI components [134], data security [129], modern software tools [130], or blockchain [111]. The second most popular sensor in this analysis is the electrocardiogram (ECG) sensor. Arrhythmia disorders are the leading causes of global mortality, which is why IoT devices measure ECG, and the data provided by this sensor is trained by ML algorithms for classification, aiming at patient prevention and monitoring [83]. [PERSON] et al. [70] present a technique for evaluating the quality of ECG signals in IoT sensors based on the k-nearest neighbors (KNN) classification algorithm. Modern medical facilities are made possible by digital tools that integrate digital twins and AI [71]. The detection of heartbeats based on the data provided by these sensors is examined by [PERSON] et al. [72]. In the pharmaceutical industry, the integration of digital twin technologies alongside AI components strategically aligns the operational needs of companies with data processing requirements, enabling them to generate the best metrics associated with AI components [144]. The study by [PERSON] et al.