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Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science.
- Bridges the gap between IoT, CPS, and mathematical modelling.
- Features numerous use cases that discuss how concepts are applied in different domains and applications.
- Provides "best practices", "winning stories" and "real-world examples" to complement innovation.
- Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT.
Professors, researchers, graduate & upper level undergraduate students, and industry practitioners in smart cities, autonomous systems, Internet of Things, CPS, machine learning, and data analytics
1. Data analytics and processing platforms in CPS
2. Fundamentals of data analysis and statistics
3. Density-based clustering techniques for object detection and peak segmentation in expanding data fields
4. Security for a regional network platform in IoT
5. Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure
6. Portable implementations for heterogeneous hardware platforms in autonomous driving systems
7. AI-based sensor platforms for the IoT in smart cities
8. Predicting energy consumption using machine learning
9. Reinforcement learning and deep neural network for autonomous driving
10. On the use of evolutionary algorithms for localization and mapping: Infrastructure monitoring in smart cities via miniaturized autonomous
11. Machine learning-based artificial nose on a low-cost IoT-hardware
12. Machine Learning in future intensive care—Classification of stochastic Petri Nets via continuous-time Markov chains
13. Privacy issues in smart cities: Insights into citizens’ perspectives toward safe mobility in urban environments
14. Utility privacy trade-off in communication systems
15. IoT-workshop: Blueprint for pupils education in IoT
16. IoT-workshop: Application examples for adult education
- No. of pages:
- © Elsevier 2019
- 16th July 2019
- Paperback ISBN:
- eBook ISBN:
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.
Professor and Research Group Leader, Trier University of Applied Sciences, Co-lead of the German Internet of Things expert group of National Digital Summit and Visiting Scholar (Lehrauftrag), ICE institute, RWTH Aachen University, Germany
Houbing Song, PhD, is an assistant professor of Electrical Engineering and Computer Science and the director of the Security and Optimization for Networked Globe Laboratory (SONG Lab) at the Embry-Riddle Aeronautical University, Florida, United States. 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 published by Elsevier.
Assistant Professor of Electrical Engineering and Computer Science, Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab), Embry-Riddle Aeronautical University, Florida, United States
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.
Professor and Group Leader, Institute for Theoretical Information Technology, RWTH Aachen University, Germany
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