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 required for the design, monitoring, and management of wireless sensor networks, with particular emphasis on applications for collecting and processing energy and environmental monitoring data.

The course covers the following topics:

• Introduction to IoT networks and MATLAB.
• Spectrum estimation for finite-energy and finite-power signals. Frequency resolution in numerical spectrum estimation. Effects of windowing on spectral estimation. PAM signals.
• Signal spectrum estimation using MATLAB.
• FIR filter design using MATLAB.
• Introduction to software-defined radio (SDR) systems. Architecture of the RTL-SDR platform and its integration with MATLAB. Review of the superheterodyne receiver principle and in-phase/quadrature demodulation.
• FM demodulation from the complex envelope. Reception of FM broadcast signals using MATLAB.
• Introduction to digital packet transmission using Binary Pulse-Position Modulation (BPPM) in the 433 MHz ISM band. Demodulation using an energy detector. Symbol timing recovery using an open-loop technique.
• Implementation of a BPPM receiver and its energy detector in MATLAB. Analysis of symbol timing recovery, sampling and decision-making, and packet synchronization.
• Introduction to RFID systems and implementation of an RFID communication system using MATLAB.
• FEC and ARQ protocols.
• MAC protocols and performance analysis of contention-based MAC protocols.
• Network layer: Link-State and Distance-Vector routing protocols and algorithms. Introduction to the TCP/IP protocol.

Readings/Bibliography

• 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.

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

Teaching methods

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

The assessment of learning outcomes consists of three stages: I) laboratory activities, II) a practical test, and III) an oral examination.

I) Laboratory activities
During the course, students will carry out laboratory activities either individually or in groups. At the end of each activity, students must submit the work they have produced. This work will be assessed by the lecturer and will contribute to the final grade. All assignments must be submitted through the Virtuale platform at least one week before the examination date.

II) Practical test
The practical test consists of a discussion of one of the submitted laboratory assignments. It is designed to assess the student’s knowledge of MATLAB, understanding of the procedures adopted, and proficiency in the laboratory activities carried out during the course.

III) Oral examination
The oral examination is designed to assess the student’s knowledge and understanding of the theoretical topics covered in the course syllabus.

Students with Specific Learning Disorders (SLDs) or temporary or permanent disabilities: students are encouraged to contact the University's Office for Students with Disabilities and Specific Learning Disorders in due time (https://site.unibo.it/studenti-con-disabilita-e-dsa/it ). The Office will propose any appropriate accommodations for eligible students. Such accommodations must be submitted to the course instructor at least 15 days in advance for approval. The instructor will assess their appropriateness, taking into account the intended learning outcomes of the course.

Teaching tools

Teaching materials, lecture notes, slides, exercises, and code examples are available online (on Virtuale).

Office hours

See the website of Anna Guerra