Mobile and Embedded Computing

lingi2146  2019-2020  Louvain-la-Neuve

Mobile and Embedded Computing
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 15.0 h
Q2
Teacher(s)
Sadre Ramin;
Language
English
Prerequisites
LINGI1341 : Computer networks
Main themes
  • Cellular networks
  • Internet of Things (IoT) and wireless sensor networks (WSN)
  • Mobile and embedded applications
  • Operating systems for IoT and WSN devices
  • Network protocols for WSN
Aims

At the end of this learning unit, the student is able to :

1 Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • INFO1.1-3
  • INFO2.4-5
  • INFO5.2-5
  • INFO6.1, INFO6.3
Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • SINF1.M1
  • SINF2.4-5
  • SINF5.2-5
  • SINF6.1, SINF6.3
Students completing this course successfully will be able to
  • Explain how in mobile cellular and sensor networks operate
  • Describe the key problems that affect these environments and identify their impact on the mobile and embedded systems
  • Integrate and combine the above concepts in order to solve complex mobile computing problems.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Evaluation methods
  • Project (40% of the final mark)
  • Final exam (60% of the final mark)
Other information
Background:
  • LSINF1252 
  • LINGI1341
Faculty or entity
INFO


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Computer Science and Engineering
INFO2M
5
Master [120] in Computer Science
SINF2M
5
Master [120] in Electrical Engineering
ELEC2M
5
Master [120] in Data Science: Information Technology
DATI2M
5