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MOBILE AND SOCIAL SENSING SYSTEMS
MARCO AVVENUTI
Academic year2021/22
CourseARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Code885II
Credits6
PeriodSemester 2
LanguageEnglish

ModulesAreaTypeHoursTeacher(s)
MOBILE AND SOCIAL SENSING SYSTEMSING-INF/05LEZIONI60
MARCO AVVENUTI unimap
ALESSIO VECCHIO unimap
Syllabus not available in selected language
Learning outcomes
Knowledge

The course is aimed at providing students with an overview of issues, solutions, methods and technologies related to mobile, wearable and social sensing systems. Key principles and techniques are discussed covering the collection, filtering and analysis of information from both mobile and social platforms, with a specific focus on data from physical and human sensors.

Assessment criteria of knowledge

During the oral exam the student must be able to demonstrate his/her knowledge of the course material and be able to discuss the reading matter thoughtfully and with propriety of expression. - The student must demonstrate the ability to put into practice and to execute, with critical awareness, the activities illustrated or carried out under the guidance of the teacher during the course.

Methods:

Final oral exam

  • Project (1/2 people per project)
Skills

At the end of the course students are expected to:

  • acquire hands-on experience with mobile computing, sensor-based systems and technologies, and social sensing applications
  • develop the skills required to design and implement mobile and wireless sensing applications
Assessment criteria of skills

The practical aspects concerning the design and implementation of pervasive applications will be assessed through a project.

The theoretical aspects underlying pervasive and mobile computing will be assessed through oral exam.

Behaviors

The student will be able to critically evaluate the most recent advancements in the area of pervasive and mobile computing.

Assessment criteria of behaviors

Students will be required to read a research article concerning pervasive and mobile computing. The paper will be assigned by teachers during class hours and will be presented by students during class hours.

Prerequisites

Fundamentals of distributed programming.

Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures
  • participation in seminar
  • individual study
  • group work
  • laboratory work

Attendance: Advised

Teaching methods:

  • lectures
  • seminars
  • laboratory
  • project work
Syllabus

Design and development of smartphone applications:

  • application model
  • graphical user interfaces
  • using sensors
  • context-aware computing
  • voice-based interaction

Principles and technologies in mobile/wearable computing:

  • energy efficiency
  • Bluetooth Low Energy
  • non-GPS-based localization techniques
  • human activity monitoring/recognition
  • Publish/Subscribe, Distributed Hash Tables

Principles and technologies in social sensing:

  • Social Media Sources and Networks
  • Social Bot Detection
  • Humans-as-sensors paradigm
Bibliography

Material and recommended reading indicated by the teacher.

Assessment methods

Presentation of a research article (10 minutes).

Project presentation and discussion (20-30 minutes).

Oral exam (approximately 30 minutes).

 

Temporary instructions due to the COVID-19 pandemic:

the exam will be carried out using Microsoft Teams using this channel

https://teams.microsoft.com/l/channel/19%3a25446b457da94556a79e3c1339478e69%40thread.tacv2/Generale?groupId=ec743325-b124-4ab6-9ab0-4cb0d11b13b4&tenantId=c7456b31-a220-47f5-be52-473828670aa1

Updated: 10/09/2021 10:04