Internet of Things (elective)*
Niveau
Master`s course
Learning outcomes of the courses/module
The graduate, the student:
* Knows basic IOT architectures
* Knows methods of data generation
* Knows basics of data transmission
* Knows options of data storage
* Knows forms of data visualization
* Understands challenges of data security
* Knows basic IOT architectures
* Knows methods of data generation
* Knows basics of data transmission
* Knows options of data storage
* Knows forms of data visualization
* Understands challenges of data security
Prerequisites for the course
none
Course content
Introduction
* IoT architecture (e.g. reference models)
* Requirements for IOT systems
* IOT data transmission protocols
* Use of IOT in an industrial context (examples)
* Basics of sensor technology
* Basics of embedded systems
Implementation
* Procedure for implementing IOT
* Prototypical implementation of IOT
* Selection of sensors
* Collection, visualization and evaluation of data
* Implementation challenges
* IoT architecture (e.g. reference models)
* Requirements for IOT systems
* IOT data transmission protocols
* Use of IOT in an industrial context (examples)
* Basics of sensor technology
* Basics of embedded systems
Implementation
* Procedure for implementing IOT
* Prototypical implementation of IOT
* Selection of sensors
* Collection, visualization and evaluation of data
* Implementation challenges
Recommended specialist literature
Perry L.; Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security; Birmingham; 2018
Sinclair B.; IoT Inc: How Your Company Can Use the Internet of Things to Win in the Outcome Economy; 2017
Thomas O., Nüttgens M., Fellmann M. (Herausgeber); Smart Service Engineering: Konzepte und Anwendungsszenarien für die digitale Transformation; Wiesbaden; 2017
Sinclair B.; IoT Inc: How Your Company Can Use the Internet of Things to Win in the Outcome Economy; 2017
Thomas O., Nüttgens M., Fellmann M. (Herausgeber); Smart Service Engineering: Konzepte und Anwendungsszenarien für die digitale Transformation; Wiesbaden; 2017
Assessment methods and criteria
Written exam
Language
German
Number of ECTS credits awarded
4
Share of e-learning in %
15
Semester hours per week
2.0
Planned teaching and learning method
Lecture, individual work with software, group work, presentation and discussion of tasks
Semester/trimester in which the course/module is offered
3
Name of lecturer
Prof. (FH) Dipl.-Ing. Thomas Schmiedinger, PhD
Academic year
Key figure of the course/module
WPF.3
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
none