## 4 IoT Overview for Predictive Maintenance in Smart Manufacturing
[105] introduced and tested a decision support system (DSS) powered by IoT, big data, and machine learning (ML) to solve a PdM task involving advanced processing and measurement machines. Unlike others, their novel approach relies on a feature extraction strategy and an ML prediction model based on specific topics collected at the lower and upper levels of the manufacturing system. The IoT sensing technology is based on Message Queuing Telemetry Transport (MQTT [106]) [107]. After synchronizing, all the data is gathered and stored in an Azure Blob cloud storage account and a Structured Query Language (SQL) database. During each MCM production cycle, feature extraction is performed using trapezoidal numerical integration (TNI) to calculate a key performance indicator (KPI) for each processing parameter. Within the steel sector, [PERSON] et al. examine the implementation of a PdM paradigm on the equipment used in the hot rolling process, which produces steel sheets, while keeping an eye on the drum deterioration within the heating coils of Steckel rolling mills [108]. Based on a Bayesian filter, a machine learning technique, this machinery's progressive deterioration  
Figure 18: Architecture proposed in [101]. The data are acquired, sent to a cloud, and recorded in a database. The MQTT protocol converts all the collected data into a single type. is estimated and predicted, enabling operators to make well-informed judgments about maintenance procedures. Many storage companies adopt the cold chain to store particular products that require the control of environmental parameters such as temperature, humidity, barometric pressure, carbon dioxide (CO\({}_{2}\)), and organic compounds. For this purpose, [PERSON] et al. have designed and created a framework for the remote monitoring of refrigeration and cold storage systems (RCSS) based on the implementation of a wireless sensor network (WSN) for data acquisition and intelligent algorithms for PdM of these plants [109]. The sensors are installed inside the cooling space of a refrigerator. More specifically, a WiFi XBee module has been used. The data are stored and processed by a set of algorithms developed as web services, constituting the WebSocket cloud platform [110]. [PERSON] et al. developed the NGS-PlatOne system to achieve continuous and widespread industrial machinery monitoring via battery-powered IoT sensing devices inside a power plant [111]. Their proposed system performs temperature and vibration monitoring. The temperature and vibration control monitoring devices and all other monitoring devices arranged as a sensor node may abstract the variables under observation and events observed as resources offered via embedded web services. Consequently, a RESTful paradigm is made possible in the limited environment by allowing each resource to be handled via the CoAP protocol operating on 6 LoWPAN. A simple vibration analysis is carried out in the accelerometer node by analyzing the root mean square (RMS) and peak-to-peak (P2P) characteristics throughout time periods on each axis. When these values are greater than the programmable thresholds, a failure event is signaled, and advanced algorithms that perform frequency domain analysis have not been implemented to process the accelerometer node data due to their complexity. The authors in [112] propose a novel approach for PdM. In particular, they propose a way to manage resources by integrating self-learning into genetic algorithm (GA)-based fog computing. In particular, the algorithm called Fogworkflowsim simulates GA's cost, energy, and time performance along with MaxMin, MinMin, RoundRobin, and FCFS [113]. [PERSON] et al. propose a new ML approach based on the vibration signal spectrum for bearing failure prediction [35]. Their study compares the effectiveness of traditional NN algorithms with a new approach integrating fuzzy logic, achieving a prediction accuracy of 99.40%. [PERSON] et al.