Course syllabus - Reliable and Real-Time Networks, 7.5 credits

Information about the course

Course code: FOID010
Third-cycle subject: Computer Science
School: School of Innovation, Design and Engineering
Responsible department: NES
Valid from: 2025-08-29
Established by: Dean of School
Decision date: 2025-08-29
Last modified: 2025-08-25
Level of education: Third cycle level

Course objective

The course will target basics and fundamentals of real-time communication in industrial setup, such as in automotive and automation industries. Moreover, it covers various network technologies that are commonly used in the industrial setups. The technologies that are covered in this course includes fieldbus (CAN, CAN FD, CAN XL), Ethernet (Ethernet IP, Real-time Ethernet, AVB, TSN), and techniques for reliability analysis of the networks. The objective of the course is to give students a deep understanding of requirements from industry in terms of timing, reliability, and resource utilization for designing and analysing industrial networks.

Course content

Basic and fundamentals of real-time networks
Timing and reliability requirements in designing communicationinfrastructure for industry
Fieldbus in industry specifically various versions of CAN (ControllerArea Networks)
Details of real-time Ethernet, including Audio Video Bridging, Time-Sensitive Networking.
Timing analysis of different Ethernet and CAN communication protocols
Reliability analysis of real-time networks

Intented learning outcomes

Can describe the main timing and reliability requirements of industrial networks
Can analyse the timing properties of different network technologies, including CAN, CAN FD, CAN XL, and AVB/TSN
Can analyse reliability requirements of real-time networks
Can model the networks in high-level using software and system modelling techniques

The intended qualitative targets in relation to the Higher Education Ordinance, appendix 2.

Knowledge and understanding

For the Degree of Doctor, the doctoral student shall demonstrate:

A1: broad knowledge and systematic understanding of the research field as well as advanced and up-to-date specialised knowledge in a limited area of this field,

Competence and skills

For the Degree of Doctor, the doctoral student shall demonstrate

B1: the capacity for scholarly analysis and synthesis as well as to review and assess new and complex phenomena, issues, and situations autonomously and critically,
B2: the ability to identify and formulate issues with scholarly precision critically, autonomously, and creatively, and to plan and use appropriate methods to undertake research and other qualified tasks within predetermined time frames and to review and evaluate such work,
B3: through a dissertation the ability to make a significant contribution to the formation of knowledge through his or her own research,
B5: the ability to identify the need for further knowledge,

Teaching formats

The course contains 5 full-day workshops including lectures and hands-on exercises. Moreover, the course has 1 full-day presentation/seminar to present final results of a small project.

Examination

SEM1: 3.0 credits (connected to Learning Outcome 1)

GRU1: 3.5 credits (connected to Learning Outcome 2, 3, 4)

OBN1: 1.0 credits (connected to Learning Outcome 1)

Grade

Examinations included in the course are assessed according to a two-grade scale, fail or pass.

Grades are to be decided by a teacher specially appointed by the university.

A person who has not passed the regular examination shall be given the opportunity to retake the test.

Requirements

To participate in the course and the examinations included in the course, the applicant must be admitted to doctoral studies at Mälardalen University.

Specific entry requirements

Basic knowledge of real-time embedded systems is required

Selection criteria

Selection of applicants will be made in accordance with the ranking below.

Doctoral students in Computer Science
Doctoral students at Mälardalen University