# Integration of AI in Self-Powered IoT Sensor Systems
## 4 IoT Sensors Identification Based on Measured Input
[145] demonstrates the practical integration of digital twin technologies in a production line for packaging biological drugs. The model simulates reality and identifies bottlenecks. In this way, the packaging process is optimized without disrupting actual operations. The accuracy of the virtual model is over 90% correlation with reality. Overall, \begin{table}
\begin{tabular}{c c} \hline
**Sensor Type** & **References** \\ \hline Load cell & [106] \\ Motion sensor & [43; 49; 64; 66; 107; 108; 109; 110; 111; 112] \\ Passive infrared & [101; 113] \\ Pressure sensor & [61; 95; 114; 115] \\ Proximity sensor & [110] \\ Rain sensor & [116] \\ Smoke sensor & [117] \\ Sound sensor & [118] \\ Strain gauge & [46] \\ Temperature sensor & [46; 92; 111; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135] \\ Tilt sensor & [95] \\ Ultrasonic sensor & [48; 106; 136; 137; 138] \\ Vibration sensor & [109; 140; 141; 142] \\ Water quality sensor & [142] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._the two studies [144,145] demonstrate the capabilities of digital twin technologies in the industry through the integration of AI and IoT technologies in relation to sensors. Portable IoT sensors monitor health by detecting obstructive apnea [73] and by integrating Convolutional Neural Network (CNN) models for multimodal analysis for the same purpose [79]. The CNN model presented in the research by [PERSON] et al. [74] detects atrial fibrillation from ECG, using pruning and quantization, with 91.1\(\times\) compression and 91.7% accuracy. The one-dimensional convolutional neural network (1D CNN) for classifying heartbeats from ECG signals obtained with wearable IoT sensors has been studied by [PERSON] et al. [75]. At the same time, [PERSON] et al. [76] compare AI classifiers for detecting the integrity of ECG data from IoT sensors, achieving 99.47% accuracy. Early diagnosis through arrhythmia classification from ECG is achieved with the help of ML algorithms, reaching 99.92% accuracy [80]. These algorithms operate in real-time by acquiring data from the electrocardiogram sensor and processing it at the algorithm level, achieving remarkable performance metrics, such as 99.75% accuracy [81]. [PERSON] et al. [78] mention that human errors cause 80% of road accidents. In this context, ECG monitoring can improve traffic safety by detecting conditions harmful to drivers. [PERSON] et al. [82] developed an NN for real-time ECG anomaly detection, with the primary objective of reducing energy consumption in wearable IoT devices. The same major concerns aimed at improving the efficiency of these devices are addressed by [PERSON] et al. [77]. [PERSON] et al. [84] present an IoT ECG module for predicting vascular age and the impact of smoking on ECG traces, and the results are validated through ML models. Analyzing the specialized literature, the accelerometer is one of the most popular sensors. It is identified by [PERSON] et al. [43] as helping recognize human activities at the level of smart buildings. From a logistical standpoint, [PERSON] et al. [49] use accelerometers for real-time monitoring of office occupancy. [PERSON] et al. [146] continuously monitor the condition of motor bearing faults to prevent various accidents. With wireless signal processing, IoT sensors in the accelerometer category maximize energy efficiency by integrating AI methods using Edge and Cloud technologies [44]. In Industry 4.0, accelerometers are used for monitoring bridges [45], examining the integration of composite materials at the railway level [46], or are utilized in medical applications to improve the prediction of alcohol intoxication [47,147]. [PERSON] and [PERSON] [53] use these sensors at an industrial level to increase data production in defect detection through ML technologies. In the paper by [PERSON] [48], accelerometers identify potholes, ensuring high road safety.