Mobile and Embedded Computing

Teacher(s)

Sadre Ramin;

Language

English
> French-friendly

Prerequisites

Main themes

• Cellular networks
• Internet of things and sensor networks
• Mobile and embedded applications

Learning outcomes

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

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 INFO2.2-5 INFO5.3-5 INFO6.1, INFO6.4, INFO6.5 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 SINF2.2-5 SINF5.3-5 SINF6.1, SINF6.4, SINF6.5 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.

Content

The Internet of Things is everywhere. Many different kinds of applications, from logistics to Smart Homes to eHealth, rely on continuous data collection by small, wireless devices. In this course, we will program such devices and learn about the technologies that enable them to communicate with servers in the Internet over short and long distances (>1km). The focus will be on software and network protocols for mobile and embedded devices. The design of hardware and antenna technology is not part of this course.

• Wireless sensor networks
• Internet of Things
• Programming embedded systems with network connection
• Network protocols for resource-constrained devices
• Introduction to mobile networks (4G, 5G)

Teaching methods

• Lectures
• Scientific readings
• Practical lab sessions
• Project activities

Evaluation methods

Mode of evaluation for the June session:

• Exam (60% of the final mark)
• Project during the year (40% of the final mark)

August session: The project cannot be done or redone for the August session and the student will keep the grade obtained for it in the June session with the same weights for the final mark as indicated above. Not participating in the project will result in a zero mark for it.
Regarding the use of generative AI in the project: Students remain fully responsible for their work and must be able to explain it orally. Students are permitted to use generative AI tools to support their project work, e.g., to compare solution approaches, find errors in code, or improve the wording in reports. However, submissions, whether source code, reports, or other forms, must essentially be the students' own work. If generative AI tools have been used, their nature and scope must be described in detail in the submission (see the project instructions provided in the course) and only the original part of the student's work will be evaluated. This also applies when drawing on the work of others.
AI use considered abusive, and undermining the acquisition of knowledge targeted by the course, may be treated as a violation under Section 7, Articles 107 and following of the General Regulations of Studies and Examinations (RGEE), with all related consequences, as provided in Articles 111 and following of the RGEE. In case of suspected abusive use of AI in submitted work, or of incomplete or inaccurate reporting of AI use, the course instructors may take necessary measures in agreement with the EPL examination board coordinator.

Other information

You will need basic knowledge of IP networks, of computer systems, and of programming in C, for example from these courses:

• LINFO1252 or LINFO2241 (C and computer systems)
• LINFO1341 or LINFO2147 (IP networks)

Online resources

Moodle and/or Teams

Faculty or entity

> INFO