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fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
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Dartmann / Song / Schmeink

Big Data Analytics for Cyber-Physical Systems

Machine Learning for the Internet of Things

Medium: Buch
ISBN: 978-0-12-816637-6
Verlag: Elsevier Science Publishing Co Inc
Erscheinungstermin: 16.07.2019
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Autoren/Hrsg.

Herausgeber

Prof. Dr.-Ing. Guido Dartmann is a professor and research group leader at Trier University of Applied Sciences, Germany. Dr. Dartmann also serves as a co-lead of the German IoT expert group of national Digital Summit. His research interests include distributed systems, data analytics, signal processing, optimization of technical systems, cyber-physical systems, wireless communication, cyber-security, internet of things, and traffic and mobility.

Houbing Song, Security and Optimization for Networked Globe Laboratory, University of Maryland, Baltimore County (UMBC), Baltimore, USA. His research interests include cyber-physical systems, cybersecurity and privacy, IoT, big data analytics, connected vehicles, smart health, wireless communications, and networking. Dr. Song has edited and authored several books in the field, including Cyber-Physical Systems: Foundations, Principles and Applications.

Prof. Dr.-Ing. Anke Schmeink, is a professor leading the ISEK research and teaching area at RWTH Aachen University, Germany. Her research interests include information theory and network optimization.

1. Data analytics and processing platforms in CPS2. Fundamentals of data analysis and statistics3. Density-based clustering techniques for object detection and peak segmentation in expanding data fields4. Security for a regional network platform in IoT5. Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure6. Portable implementations for heterogeneous hardware platforms in autonomous driving systems7. AI-based sensor platforms for the IoT in smart cities8. Predicting energy consumption using machine learning9. Reinforcement learning and deep neural network for autonomous driving10. On the use of evolutionary algorithms for localization and mapping: Infrastructure monitoring in smart cities via miniaturized autonomous11. Machine learning-based artificial nose on a low-cost IoT-hardware12. Machine Learning in future intensive care-Classification of stochastic Petri Nets via continuous-time Markov chains13. Privacy issues in smart cities: Insights into citizens' perspectives toward safe mobility in urban environments14. Utility privacy trade-off in communication systems15. IoT-workshop: Blueprint for pupils education in IoT16. IoT-workshop: Application examples for adult education