Academic Year 2021/2022 - 2° Year - Curriculum Data driven applications for IoT
Teaching Staff: Maurizio Palesi
Credit Value: 6
Scientific field: ING-INF/05 - Sistemi di elaborazione delle informazioni
Taught classes: 40 hours
Term / Semester:

Learning Objectives

  • Knowledge and Understanding: On completion of the course, the student shall 1) Know the key technological components underpinning IoT, 2) Understand IoT Architectures and the application of IoT in various domains, 3) Know the difference among networking protocols in the context of resource-constrained IoT devices, and 4) Know how Big Data can be exploited in the context of Smart Spaces.
  • Applying Knowledge and Understanding: On completion of the course, the student shall be able to analyze and select the appropriate technological solutions for Smart Spaces enabled by IoT and Big Data collection and analysis.
  • Making Judgements: Completing the course, the student will be able to judge the suitability, the capabilities, and the limitations of IoT based applications in the context of Smart Spaces. Further, the student will be able to identify issues, problems, or misleading results.
  • Communication Skills: On completion of the course, the student will be able to illustrate the theoretical and technical properties which characterize IoT based Smart Environments. The student will be able to interact and collaborate with peers and experts in the realization of a project or research.
  • Learning Skills: On completion of the course, the student will be able to autonomously extend the knowledge acquired during the study course by reading and understanding scientific and technical documentation.

Course Structure

The teaching will be carried out by lectures, exercises and discussion of case study.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Required Prerequisites

Basic notions in computing, database and data analytics, basics on Knowledge discovery

Attendance of Lessons

Attendance is not compulsory but strongly recommended

Detailed Course Content

Introduction to IoT Systems

  • Definition of the Internet of Things (IoT): IoT examples; IoT devices; IoT devices vs. computers.
  • Trends in the Adoption of the Internet of Things (IoT): Trends; Potentiality and pervasiveness.
  • The Importance of the Internet of Things (IoT) in Society: Societal benefits of IoT; Risk, privacy and security.

IoT Components and Protocols

  • Features and Constraints of Embedded Systems: What are embedded systems; Generic embedded systems structure; Main components overview; Specific components examples; Microcontrollers, Sensor and Actuators; Analog/Digital conversion.
  • Machine-to-Machine (M2M) communication: Technologies for WPAN (BLE, IEEE 802.15.4, etc.); Technologies for WLAN and LPWAN (LoRA and SigFox).
  • IoT application protocols: Requirements, resource constrained protocols, XMPP, CoAP, MQTT, AMQP, WebSocket, etc.

IoT Data Storage, Analytics and Platforms for System Integration

  • Architectures for IoT data storage and processing: cloud/fog/edge computing.
  • Hands-on with KNIME Data Analytics Platform

IoT Applications Domains

  • Smart space enabled application domains: examples and case studies
  • Introduction to Domain Specific Accelerators for IoT

Textbook Information

All the teaching material will be made available through the course page on Studium.

Course Planning

 SubjectsText References
1Introduction to IoT SystemsStudium 
2IoT Components and ProtocolsStudium 
3IoT Data Storage, Analytics and Platforms for System IntegrationStudium 
4IoT Applications DomainsStudium 

Learning Assessment

Learning Assessment Procedures

Exams consists of an oral examination. Grading takes into account the following criteria

  • Read the material for each lecture, ask questions, participate (5%)
  • Homeworks (15%)
  • Project presentation (40%)
  • Oral discussion (40%)

Examples of frequently asked questions and / or exercises

See material available on Studium