# Review of Monitoring and Control Systems Based on Internet of Things
## 3 Monitoring and Control Systems Based on IoT
### IoT-Based Predictive Maintenance Systems  
[PERSON] et al. in [43] discusses a system for predicting damage in the manufacturing sector designed to minimize downtime and early detection of damage to production machines. This system integrates various IoT-based sensors and uses artificial intelligence to analyze and make decisions about potential failure points. The system consists of a network of sensors placed at strategic points that constantly monitor various operating parameters of machines on the production line, such as temperature, humidity, pressure, current, vibration, air quality, and gas. Additionally, specialized sensors, such as ultrasonic and photoionization, were used. These sensors are connected to a central management system that collects data from many sources and transmits it to the cloud for further processing and analysis. The data cloud uses advanced machine learning algorithms such as the PdM algorithm to identify patterns, forecast trends and generate reports for end users. The described IoT-based environmental monitoring system is an innovative solution that allows for effective monitoring and early detection of machine failures. Its versatile applications and advantages make it a valuable contribution to monitoring and predicting failures using modern technologies. [PERSON] et al. in [44] present an innovative system based on IoT technology, specifically designed for predictive maintenance of circular knitting machines. Machines of this type are crucial for the textile industry, and their unplanned downtimes can generate significant costs and disrupt the production process. The IoT-based system enables real-time monitoring of machines, identification of potential faults, and effective maintenance planning, contributing to increased operational efficiency and cost reduction associated with machine maintenance. The system consists of an advanced printed circuit board (PCB) with an ESP32 microcontroller and various sensors, including speed and machine stop detection. These sensors allow the system to monitor various machine stops such as lycra stop, gate stop, idle stop (caused by human interaction), feeder stop, needle stop, and completed roll. Data collected by the system are stored in a MongoDB database, enabling further analysis and utilization in predictive machine maintenance. The system has been deployed on two different PAILONG circular knitting machines operating at an ambient temperature of 26 degrees Celsius. By utilizing data collected from the sensors, the system provides comprehensive real-time monitoring of the machines, enabling quick detection of potential faults and efficient maintenance planning. The system's main advantage is its ability to provide real-time monitoring of machines and prompt response to any abnormalities in operation. By identifying the cause of machine stoppages, operators can take immediate corrective actions, minimizing downtime and preventing potential breakdowns. Additionally, IoT technology allows for collecting and analysing large amounts of data, creating increasingly accurate predictive models that can anticipate future machine faults with greater precision. The described IoT system for the predictive maintenance of circular knitting machines represents a significant step forward in the textile industry's industrial maintenance field. Its implementation can significantly increase operational efficiency, reduce machine maintenance costs, and ensure smooth production processes by minimizing unplanned downtimes. It is an excellent example of leveraging modern technologies to improve performance and competitiveness in the industrial sector. [PERSON] et al. in [45] noticed that the contemporary digitization of manufacturing processes and shifting market demand towards products and services with lower costs have led to the emergence of Product-Service Systems (PSS). The article emphasizes the importance of maintenance, repair, and training within PSS, combined with Augmented Reality (AR).