96493 - Laboratory of Internet of Things

Learning outcomes

In this course, the student will learn to design and manage a basic communication infrastructure for IoT applications. In particular, the student will acquire, through lectures in the classroom and practical experiences in the laboratory, the knowledge necessary for the configuration and implementation of telecommunications networks and wireless sensor networks, to collect data and process them remotely, e.g., for energy and environmental monitoring applications.

Course contents

The course aims to provide the fundamental practical skills for the design, monitoring and management of wireless sensor networks, with particular emphasis on applications for the collection and processing of energy and environmental monitoring data.

The course is divided into two teaching modules and presents the following topics:

Module 1

• Introduction to wireless sensor networks and their applications, with particular reference to environmental monitoring.

• Recalls on A/D and D/A conversion and numerical signal processing.

• Study and implementation of basic algorithms for numerical signal processing, modulation and demodulation.

• Design of algorithms on software defined radio (SDR) platform for wireless environmental monitoring systems.
  

Module 2

• Introduction to data collection and processing techniques from sensor networks.

• Development of web-based interfaces for the management of data generated by sensor networks for environmental monitoring.

• Data processing techniques for the detection and classification of anomalies.

Readings/Bibliography

Module 1

• A. V. Oppenheim, R. W. Schafer, Elaborazione Numerica dei Segnali, Franco Angeli, 1996.
  
  
• V. K. Ingle, J. G. Proakis, Digital Signal Processing using MATLAB, Brooks/Cole, 2000.
  
  
• J. G. Proakis, M. Salehi, Contemporary Communication Systems using MATLAB, Brooks/Cole, 2000.
  
  
• L. Calandrino, M. Chiani, Lezioni di Comunicazioni Elettriche, Pitagora Editrice, Bologna, 2013.
  
  

Module 2

• J. Watt, R. Borhani, A. K. Katsaggelos, Machine Learning Refined: Foundations, Algorithms, and Applications, Cambridge University Press, 2020.
  

Teaching methods

Modules 1 and 2

Both modules are divided into lectures and laboratory sessions. The teaching method involves: formulating the problem in terms of project specifications, mathematical formalization of the solution, laboratory simulation, laboratory implementation.Laboratory exercises on software defined radio (SDR) platform are also scheduled.

Assessment methods

Modules 1 and 2

The learning assessment for each module is divided into three phases: I) laboratory experiments, II) practical test, III) oral test.

I) During the course, laboratory experiments are planned to be carried out in groups of two people. At the end of the experiment, the groups must submit the produced material. This will be evaluated by the teacher during the course and will constitute an integrative assessment element for the subsequent final test.

II-III) The final exam consists of a practical test in the laboratory, followed by an oral test. The practical test aims to ascertain the skills acquired in solving signal processing and data transmission problems in the context of environmental monitoring. The oral test aims to verify the acquisition of theoretical knowledge provided by the course program.

The final exams of the two modules can be taken on the same day or on different days, independently.

Office hours

See the website of Enrico Testi

See the website of Elia Favarelli