## 3 Overview of IoT System Security Applications in the Industrial Field
It consists of two elements: the overall heat assessment (OHS) and the personal management system (PMS), the latter of which uses fuzzy theory to assess each worker's health risk level [65]. At the same time, the former organizes breaks for teams of workers based on a thermal comfort index (TCI). A microcontroller unit (MCU) incorporated in the device houses two modules (LoRa and GPS), three sensors (accelerometer, skin temperature, and PPG), and a power source. Furthermore, authorized staff may use a PC or smartphone to locate a construction worker's position and current state and make any required choices from a distance [66]. The work carried out by [PERSON] et al. in [67] combines the use of three items of PPE, a helmet, a bracelet, and a belt that can acquire biophysical and environmental parameters. The data are then processed through algorithms by a deep convolutional neural network (DCNN). Similarly, [PERSON] et al. in [68] have developed a new concept of PPE, in particular, gloves that integrate commercial devices, such as an energy collection system, a UHF-RFID tag for information exchange, and a microcontroller for the recognition of the correct use of the PPE. The exchange of information is performed by the EPCglobal Class-1 Generation-2 communication protocol. With the advancement of technology and the inventiveness of researchers, the concept of smart has not only been applied to jackets but has also been extended to T-shirts, as shown in Figure 10. All sensing devices integrated into the shirt for monitoring biological parameters are powered by a 380 mAh battery, which is recharged through sunlight, temperature, and the body movements of the operator who wears it. Furthermore, gas detection sensors can detect the concentrations of carbon dioxide, oxygen, formaldehyde, and hydrogen sulfide in the environment in which the worker operates [69; 70]. Figure 10: Schematic of the IoT paradigm applied to a smart shirt, highlighting all the sensing devices and the transfer and visualization scheme of the data collected during the worker’s operations [69; 70]. Figure 9: Overall representation of the IoT architecture, proposed by the authors in [62]; (**a**) reversing accident prevention; (**b**) intelligent warnings to avoid potential dangers; (**c**) IoT middleware platform. Finally, IoT systems have been able to contribute to worker safety in the agriculture industry as well. For this purpose, [PERSON] et al. in Ref. [71] propose a modular design to determine worker locations, machines, and obstacles to ensure the safety of the path of remote-controlled farm machines (RCFM) using specific notifications for timely interventions of workers on the ground. Figure 11 shows the main features and positioning functionalities based on passive ultra high frequency-radio frequency identification (UHFF-RFID) technology supported by commercial BLE beacons to monitor and prevent accidents that may occur when workers are on the ground. UHF-RFID tags were applied on PPE, such as helmets and jackets, obtaining the best configuration with all four tags positioned vertically [14]. The worker position is obtained with triangulation through the received signal strength indicator (RSSI) obtained from the Beacon devices deployed in the field. Another way to monitor worker safety is to verify that the operator is wearing PPE, presenting a new evolution of security strategies. For this reason, [PERSON] et al. used a deep learning approach to video streaming analysis with YOLOv4. In their case, when  
Figure 11: Architecture (**a**) and functional block diagram (**b**) of the monitoring system of worker security in the agricultural fields proposed in [71]. an operator is not wearing PPE, an acoustic or light signal is emitted [72]. The important aspect is that the streaming video is directly analyzed by the device placed in the area.