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Edge Computing and Internet of Things

7,5 credits

The course is intended to develop the student’s knowledge and abilities of how edge computing and Internet of Things (IoT) can be used as a way to meet application demands in intelligent IoT systems. This includes an understanding and use of the IoT architecture with its entities and protocols, from the IoT devices, via middle layers like edge and fog, up to the cloud. It also includes the understanding of the computing and communication technologies used for IoT, as well as the analysis of their constraints, as e.g. performance, power efficiency, memory size, and communication bandwidth. The course also includes the security and privacy issues related to the area of edge computing, IoT, and big data.

Further, it is intended to provide the possibility for the student to, from the basis of relevant literature, reflect over and discuss current research and development in regard to highly demanding streaming applications, like advanced sensing or machine learning, at the edge of an IoT system. The student should be able under supervision to implement an edge and IoT systems.




The course is divided into a lecture part, a laboratory part including a small project, and a seminar part.

The lecture part initially gives a motivation for IoT and edge/cloud computing, based on application requirements and resource restrictions. Then it introduces the architectures, entities and protocols used for IoT and edge computing. Example applications and IoT architectures are presented and discussed. This part will also discuss various limitations, such as computing, memory, communication, power, and energy limitations, that will influence future edge and IoT developments. The course will also address relevant security and privacy issues in the area.

The laboratory part provides hands-on experience of edge computing and IoT systems and architectures for the development and use of intelligent IoT systems.

In the seminar part of the course, course participants conduct detailed studies of various subareas and lead seminars in these. The university’s research projects are included in these special studies.

Spring 2022 (Campus based, Halmstad, 50%)

Level:

Advanced level

Application code:

X3103

Entry requirements:

Courses in computer science, computer engineering or electrical engineering of at least 90 higher education credits, including thesis. Algorithms, Data Structures and Problem Solving 7.5 credits or equivalent.

Selection rules:

Available for exchange students. Limited numbers of seats.

Start week:

week: 03

Instructional time:

Daytime

Language of instruction:

Teaching is in English.

Show education info
Spring 2023 (Campus based, Halmstad, 50%)

Level:

Advanced level

Application code:

X3103

Entry requirements:

Courses in computer science, computer engineering or electrical engineering of at least 90 higher education credits, including thesis. Algorithms, Data Structures and Problem Solving 7.5 credits or equivalent.

Selection rules:

Available for exchange students. Limited numbers of seats.

Start week:

week: 03

Instructional time:

Daytime

Language of instruction:

Teaching is in English.

Show education info

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