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Subject: IOT SYSTEMS (A.A. 2024/2025)

master degree course in COMPUTER SCIENCE

Course year 2
CFU 6
Teaching units Unit IoT Systems
Information Technology (lesson)
  • TAF: Compulsory subjects, characteristic of the class SSD: ING-INF/05 CFU: 6
Teachers: Luca BEDOGNI
Exam type oral
Evaluation final vote
Teaching language Italiano
Contents download pdf download

Teachers

Luca BEDOGNI

Overview

The student will acquire knowledge regarding Internet of Things technologies, regarding low power communication protocols and their interactions.

Moreover, in this class we will also study techniques and tools to build and deploy IoT networks.

For the objectives, please read the Expected results section.

Admission requirements

Knowledge of a programming language such as C, C++, Python
Basic knowledge of network architectures

Course contents

Introduction about the class - Overview, scenario definition, examples

Data gathering - Sensing policies, Communication Protocols, Web of Things, Crowdsensing, Edge/Fog/Cloud Computing (20 hours)

Data analysis - Fuzzy systems, brief introduction about machine learning models (8 hours)

Context Aware Systems - Location Aware systems, ubiquitous computing, privacy in ubiquitous computing (14 hours)

The hours shown must be intended as tentative, and can change upon student's feedback.

Teaching methods

Lectures, with laboratory starting from mid-class. Non mandatory presence, but strongly advised. In-person class. English language.

Assessment methods

Optional short seminar (15 minutes including questions) on a scientific paper. The list of possible papers to select from will be given by week 3 of the class. Specific days will be organized, so seminars from different students are grouped together. Seminars need to be given at maximum in 2 weeks from the end of the class. The seminar must be given individually. Project work: development of a project focused on one or more of the topics of the class. A list of possible projects will be given by week 3 of the class. Project can be given individually, however for specific projects also groups of 2-3 people may be allowed, with prior allowance from the teacher. The seminar evaluation will focus on the ability of the student to present concisely, yet exhaustively, specific contributions in the IoT scenario. The student should read and analyze the content of a scientific paper, present a short presentation (8-10 slides) and give a short seminar (in Italian or English) which summarises the contribution of the article. It is also expected that the students actively contribute by making questions to other presentations. The evaluation of the seminar is in the [0:3] range, which will be eventually summed to the project evaluation. Whoever decides not to give any seminar will have an evaluation of 0. The project evaluation will focus on the achieved results, and on the ability of the student(s) to present it in detail. For group works, separate evaluations may be given. A good final project will end up with en evaluation of approximately 25. A minimum project will bring a 18, while an outstanding project may end up at 30 cum laudae.

Learning outcomes

At the end of the class the student will have basic knowledge about the IoT scenario, and specific knowledge on the topic chosen for the seminar and/or project work. The student is then able to analyze, design and develop IoT systems, with principles of ubiquitous computing and context awareness.

1) Knowledge and understanding of IoT and Context-aware systems
2) Ability to apply knowledge and understanding on the design, development and performance analysis of IoT systems
3) Autonomy of judgment to evaluate choices and possibilities when developing IoT systems
4) Communication skills on state of the art topics in IoT systems
5) Learning skills to improve the students' own capacity to learn novel technologies in the IoT domain

Readings

Book - Dustdar, Schahram, Nastic, Stefan,ˇScekic, Ognjen: Smart Cities:”The Internet of Things, People and Systems”

Book - Borcea, Christian, Talasila, Manoop, Curtmola, Reza: ”Mobile Crowdsensing”

Teacher slides (Moodle)

Code examples