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 the implementation of machine learning algorithms in embedded systems, focusing on the interaction between IoT technology and the mathematical tools to evaluate the extracted data of those systems. Chapters provide different tools and applications on a broad list of data analytics and machine learning tools. Additionally, the book addresses how to incorporate these technologies into our society by examining new platforms for IoT in schools and new and necessary courses.
As cyber-physical systems (CPS) and the Internet of Things (IoT) are rapidly developing technologies that are transforming our society, this book provides a timely update for both practitioners and interested researchers.
- Fills the gap between IoT, CPS and mathematical modeling
- Includes numerous use cases that discuss how concepts are applied in different domains and applications
- Provides "best practices," "real developments" and "winning stories" that complement technical information
- Uniquely covers concepts 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 of 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 the heating energy consumption of a building for several subsequent days using machine learning methods
9. Reinforcement Learning and Deep Neural Network for Autonomous Driving
10. On the Use of Evolutionary Algorithms for Localization and Mapping of Miniaturized Autonomous Sensory Agents for Infrastructure Monitoring in Smart Cities
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 towards 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
- 1st July 2019
- Paperback 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 Internet of Things 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. His research interests include cyber-physical systems, cybersecurity and privacy, internet of things, 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.
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, USA
Prof. Dr.-Ing. Anke Schmeink, is a Professor and Group Leader for the Institute for Theoretical Information Technology 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