Data Science & Intelligent Analytics PT
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Internet of Things (elective)

level of course unit

Master's course

Learning outcomes of course unit

Students:
- know basic IOT architectures.
- know methods of data generation.
- know the basics of data transmission.
- know the options of data storage.
- know the forms of data visualization.
- understand challenges of data security.

prerequisites and co-requisites

not applicable

course contents

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
- Data collection, visualization and evaluation
- Challenges in implementation

recommended or required reading

Perry L.; Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastruc-ture, 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. (editor); Smart Service Engineering: Konzepte und Anwendungsszenarien für die digitale Transformation; Wiesbaden; 2017

assessment methods and criteria

Seminar thesis

language of instruction

German

number of ECTS credits allocated

4

eLearning quota in percent

15

course-hours-per-week (chw)

2

planned learning activities and teaching methods

Lecture, individual work with software, group work, presentation and discussion of tasks

semester/trimester when the course unit is delivered

3

name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

year of study

2. study year

recommended optional program components

not applicable

course unit code

WPF.3

type of course unit

integrated lecture

mode of delivery

Compulsory

work placement(s)

none