SMILE: Smart Monitoring IoT Learning Ecosystem

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior

SMILE: Smart Monitoring IoT Learning Ecosystem

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Hydrogeological Risk Management in Smart Cities: A New Approach to Rainfall Classification Based on LTE Cell Selection Parameters

The sudden climate change, that has taken place in recent years, has generated calamitous phenomena linked to hydrogeological instability in many areas of the world. An accurate estimate of rainfall levels is fundamental in smart city application scenarios: it becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for rainfall level estimation present numerous critical issues. This paper proposes a new approach to rainfall classification based on the LTE radio channel parameters adopted for the cell selection mechanism. In particular, this study highlights the correlation between the set of radio channel quality monitoring parameters and the relative rainfall intensity levels. Through a pattern recognition approach based on neural networks with Multi-Layer Perceptron (MLP), the proposed algorithm identifies five classes of rainfall levels with an average accuracy of 96 % and a F1 score of 93.6 %