| Beschreibung |
Course content:
• Introduction in Industrial IoT and Industrie4.0
• Market potential
• Industrial IoT Platforms
• Communication concepts and technologies, M2M/IoT Protocols, Wireless networks, Time-Sensitive Networking, Security
• Distributed Ledger Technologies, Block Chain and Smart Contracts
• Machine Learning and AI
• Predictive Maintenance Systems
• Examples Learning objectives:
Subject-specific competencies:
• The students have a good overview over the field of Industrial IoT.
• They know the most common IoT Platforms and their services.
• They are familiar with the most important communication technologies, IoT Proto-cols (like CoAP, MQTT, OPC UA etc.) and Wireless network technologies (like 6LoWPAN, LoRa, Sigfox, etc.) and are able to assess the advantages and disad-vantages of different technologies and select the most appropriate one for a given problem.
• They have a basic understanding of the application of Distributed Ledger Technol-ogies (e.g. Block Chain) and Smart Contracts in the context of IIoT and are able to design and assess possible fields of application.
• They know fundamental aspects of Machine learning and its industrial applications and can create possible use cases.
• They know some typical examples and use cases. Methodological competencies:
• Students are able to work independently and as a team on topics and to classify, evaluate, prepare and present them.
• Students are able to apply basic project management skills. Interdisciplinary competencies:
• Students are able to understand and evaluate business models for IIoT solutions Literature and other sources of information:
• Industrial Internet of Things : Cybermanufacturing Systems / edited by Sabina Jeschke, Christian Brecher, Houbing Song, Danda B. Rawat, Springer, 2017
• Internet of Things for Industry 4.0, edited by G. R. Kanagachidambaresan, R. Anand, E. Balasubramanian, V. Mahima, Springer, 2020
• The Era of Internet of Things, Khaled Salah Mohamed, Springer, 2019
• Machine Learning mit Python und Scikit-learn und TensorFlow, Sebastian Raschka, Vahid Mirijalili, mitp, 2. Auflage, 2018
• Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, O´Reilly, 2. Auflage, 2019 |
