## 4 IoT Overview for Predictive Maintenance in Smart Manufacturing
### Comparative Analysis of Predictive Maintenance Applications  
The following discussion shows the different methodologies employed in predictive maintenance in various industries, highlighting the integration of IoT and machine learning approaches to improve maintenance practices, reduce downtime, and optimize operational efficiency. Each study uniquely contributes to the evolving PdM landscape, offering frameworks, algorithms, and case studies highlighting this technological advancement. The key points and contributions of the different field studies are summarized below, followed by the limitations that a PdM approach may entail. Most studies exploit IoT technologies, smart sensors, and machine learning techniques, significantly improving the ability to monitor and predict machine failures in real-time by focusing on predictive maintenance. This proactive approach improves operational efficiency by reducing downtime and costs related to unscheduled maintenance. Integrating advanced data analysis techniques, such as SVM (support vector machine), random forest, XGBoost, and LSTM, improves the accuracy of failure predictions. Some studies, such as [PERSON] et al., show how integrating data from different sources (maintenance and value chain) can lead to significant performance improvements. The adoption of IoT and cloud architectures facilitates the expansion of such systems to various industries and applications, making them adaptable to different operational needs. Finally, creating models for predicting residual useful life and resource management promotes greater efficiency and the responsible use of materials. On the other hand, integrating IoT systems and advanced algorithms can be complex and require specialized skills and a significant initial investment. In particular, many approaches and models have been developed for specific situations and may not be easily transferable to other applications or industries. Although many solutions are scalable in theory, in practice, there may be technical difficulties in managing and analyzing large volumes of data without considering that the quality of data collected by sensors can affect predictions. Issues such as noisy signals or faulty sensors can compromise the results. Furthermore, the costs associated with IoT monitoring systems and supporting technologies (e.g., cloud, big data analytics) can be burdensome for companies, especially small and medium-sized enterprises. Data collection and analysis at large scales can threaten privacy and security, making additional protection measures necessary, and predictive models, such as those based on LSTMs, may require an adequate amount of historical data to be effective, which can be a limitation in contexts with scarce or unreliable data. In Table 4, a comparative analysis of the IoT paradigm related to predictive maintenance previously analyzed through some proposals found in the literature is illustrated synthetically. The comparative analysis is based on the nature of the monitored system, the communication technology for data exchange, the prediction algorithm used by the authors, and the performances obtained by them.