Development of a web application for industrial IoT machine monitoring and data visualization

The Internet of Things (IoT) enables to connect heterogeneous electronic devices in an efficient, sustainable and continuous way, allowing a level of human-to-machine and machine-to-machine interaction never reached before. These devices can be exploited to collect a large amount of data, which in turn allow making decisions and driving other electronic devices for a wide variety of applications. Thanks to the technologies developed in the field of Big Data, the collected data can be stored and automatically processed in order to support decisions, improve processes, and improve the quality of services and products. With the advent of Industry 4.0, IoT technologies have been increasingly applied in the industrial sector. In this way, industrial machines can be always connected, and equipped with many devices, which enable the interaction with the outside world. In this context, new applications can be developed, such as predictive maintenance and automatic quality control. The aim of the thesis is to firstly analyse the different approaches, protocols and technologies that can be used to exploit IoT in the industrial sector. Then, a Python web application has been developed to manage the collection, storage and visualization of data gathered from sensors positioned on an industrial machine. The real data collected by a milling machine has been used as case study.