# Individual Environmental Risk Assessment and Management in Industry 4.0: An IoT-Based Model
## 4 Discussion  
As described, the server has most of its planned features implemented and tested. The web interface for registering jobs, risks, and employees works properly. The integration of Mosquitto, Telegraf, InfluxDB, and Grafana proved to be adequate for the proposal. There were no failures in any of the services during the tests. All these services were installed on the same machine (a VPS provided by a web-hosting company), and the InfluxDB database can only be accessed from that same machine for security reasons. The VPS did not show any slowness during the tests, but if it does, it is possible to improve its settings by purchasing a new plan. This can be carried out with a single command on the website of the company providing the service and takes effect immediately. The monitoring device still needs to be tested with all its sensors. In addition, a battery must be added to the circuit so that the device works during the work shift (ideally) without needing to recharge. Then, the additional configuration must be performed in the server to handle new data. When it comes to privacy concerns, our project is in line with the GPDR recommendations, and the communication is secured by TLS-PSK. However, at the time of tests in a company, it will be necessary to provide information in a didactic and detailed way to the participants to provide transparency in the treatment of personal data. Regarding similar works, as mentioned above, various smart PPE and other monitoring systems for OSH have been proposed in recent years. For example, in [19], a piece of smart PPE was presented that can respond in real-time to the risks present in workplaces. The system uses a sensor network located on a helmet and a belt to monitor the state of the worker and the environment. The data collected by the system can be visualized by the user on a tablet or a smartphone. For example, if the worker is in a poorly lit environment, a flashlight on the helmet activates automatically. In [20], the authors proposed a device to continuously monitor the physiological variables of miners at high altitudes. In this system, the noninvasive sensors are embedded throughout a T-shirt and enable the calculation of heart and respiration rate. The device exchanges data with a central monitoring station. In [23], the authors presented an IoT-based construction worker physiological data-monitoring platform using an off-the-shelf wearable smart band that collects a worker's physiological data through heart rate, temperature, and accelerometer sensors. Data regarding workers' current physiological status are sent to the web and a smartphone app. In [24], a wearable system embedded into firefighters' garments was proposed to estimate their physiological state according to the data collected from sensors. Data are sent to the command center, which evaluates the gravity of the risk scenario and sends messages to the firefighters. In [25], the authors proposed a system capable of detecting anomalies in workplaces with a helmet, a belt, and a bracelet. Intelligent algorithms are applied to collected data to predict and notify anomalies with edge computing, where processing occurs closer to data sources to offer fast services. All data are sent to the cloud, where deep learning models verify anomalies due to the training with data inserted previously. In general, solutions based on IoT have been used for monitoring physiological data and trigger alarms when dangerous conditions are identified, as presented in the studies mentioned above. To improve this type of solution and help companies in long-term risk management, the large volume of collected data may be analyzed with AI techniques, as presented in [25, 26, 27, 28, 29]. For example, the amount of data obtained from a construction site could help the OSH department to know exactly what stages of construction are most critical in terms of exposure to one or another hazardous agent and which workers are most exposed to such agents. However, artificial intelligence is still not widely used in OSH applications.