95639 - Industry 4.0

Learning outcomes

The general objective of this course is to provide the general conceptual and technological framework that characterise Industry 4.0, focusing in particular on Internet of Things (IoT), Industrial IoT and Computer Vision and their application in digital transformation contexts. At the end of the course, a student: - has a global understanding about the big picture related to Industry 4.0 - knows the main principles, technologies and standards about Internet of Things (IoT) and Industrial IoT, integrated with contents delivered by other courses (e.g. service oriented architectures, API, web, cloud) - knows some main state-of-the art directions in this context. Examples are Web of Things, Digital Twins - knows the main application domains and concrete case studies concerning the application of IoT and Industrial IoT - knows the main topics in the field of computer vision (e.g. object detection and classification) and their applications - knows state-of-the art approaches and technologies in the context of computer vision, with reference to both classic techniques for image representation and deep learning based solutions - is able to analyse and evaluate the application of the models and technologies, as well as to build projects and prototype technologies, given a Digital Transformation context/problem

Course contents

This course covers the fundamental concepts, technologies, drivers, trends, and implications of Industry 4.0 and industrial Digital Transformation. It addresses foundational IoT technologies (sensing, processing, communication), scalable industrial architectures and standards, AI and data-driven approaches, and advanced paradigms such as Digital Twins, Web of Things, and Industry 5.0. Beyond technical aspects, the course discusses the business, organizational, and societal implications of technology-driven transformation, supported by laboratories and a real-world industrial case study.

Overarching Learning Goal

• Students understand the history, cross-disciplinary drivers, technological foundations, and current state of Industry 4.0 and industrial Digital Transformation, and can recognize patterns, evaluate technology-driven business models, and reason about future trends.

Overarching Competency Goal

• Students are capable of ideating, designing, implementing, and presenting IoT-based solutions and prototypes in the context of Digital Transformation, Industry 4.0, and Industrial IoT.

Specific Learning Goals (Knowledge)

• Understand resource-constrainedness in Industry 4.0 systems and recognize suitable methods for sensing, identification, processing, and communication in constrained environments.

• Know the technological landscape of Industrial IoT, including embedded systems, connectivity protocols, data management, AI techniques, and interoperability standards.

• Understand reference architectures, Digital Twin concepts, Web of Things principles, and emerging Industry 5.0 and human-centered paradigms.

Specific Competency Goals (Skills)

• Design and implement functional end-to-end IoT prototypes integrating sensing, communication, data processing, and cloud/edge components.

• Evaluate architectural and technological trade-offs (e.g., edge vs. cloud, protocols, standards) in industrial IoT scenarios.

• Take informed decisions in Digital Transformation projects by relating technological choices to business needs and constraints, and effectively communicate and justify these decisions.

Readings/Bibliography

Relevant literature will be announced during the lectures.

Teaching methods

The course combines lectures, laboratory activities, and real-world case analyses to integrate theoretical, technological, and applied aspects of Industry 4.0.

• Lectures to introduce theoretical models, enabling technologies, reference architectures, and interoperability standards.

• Hands-on laboratories focused on the study and analysis of IoT prototypes.

• Case study analysis and discussion to connect technological choices with organizational and business implications.

Assessment methods

Assessment is based on a combination of written evaluation and project work, aimed at verifying both theoretical knowledge and practical competencies. The following assessment methods are available:

• Written exam with open questions, designed to assess understanding of core concepts, architectures, standards, and technological trade-offs in Industry 4.0 and Industrial IoT.

• Project Simulation based on the analysis of a case study and the simulation/design of an IoT solution addressing a specific objective. 

The specific structure and weighting of the assessment components may be adjusted depending on the number of enrolled students and will be communicated at the beginning of the course.

Teaching tools

Slides and code artifacts for labs prepared by the professor.

Office hours

See the website of Samuele Burattini