Analysis of data from mobile sensors/sources (Mobile crowdsensing/Participatory sensing/Urban sensing)

1.

Subject title

Analysis of data from mobile sensors/sources (Mobile crowdsensing/Participatory sensing/Urban sensing)

Анализа на податоци од мобилни сензори/извори (Mobile crowdsensing/Participatory sensing/Urban sensing)

2.

Code

m23_s_014

3.

Study program

Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Software Engineering, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, IT management, Eco-informatics, Statistics and Data Analytics, IT management, Cloud Computing, Data science in computer science and engineering, Software for embedded systems, Cloud Computing,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Билјана Ристеска Стојкоска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

This course aims to introduce students to the latest opportunities arising from Integration and analysis of sensor data from mobile sources (PR.

11.

Subject content:

Overview of existing applications (smart buildings, smart cities, transport, health care, energy efficiency, etc.). System design challenges. Extraction of sensor data from different sources (smartphones, cars, etc.). Analysis of the availability of this data. Communication of devices adjacent and exchanging sensor data via Internet of Things. Network traffic optimization through reduction (aggregation or prediction) of data. Trustworthiness confidentiality. Processing of massive data (Big data) and building cloud recommendation systems. Infrastructure to connect data to the cloud. Cloud as a common aggregate of multiple providers. Potential applications and future directions (public transport, environment, social networks and smart power networks).

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, анализа на истражувачки статии и извештаи, вежби, тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на домашни задачи и семинарски работи, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 15 + 0 + 0 + 0 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

0 hours

16.2.

Independent tasks

0 hours

16.3.

Homework

0 hours

17.

Grading method

17.1.

Tests

0 points

17.2.

Seminar work / project (presentation: written and oral)

0 points

17.3.

Activities and learning

0 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Реализирани домашни задачи

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature