Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things - Softcover

Softcover

ISBN 10: 0128166371 ISBN 13: 9780128166376

Verlag: Elsevier, 2019

Inhaltsangabe

Big Data Analytics for Cyber-Physical Systems editado por Elsevier uk

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Reseña del editor

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

Contraportada

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things

Edited by Guido Dartmann, Houbing Song and Anke Schmeink

Cyber-physical systems (CPS) and the Internet of Things (IoT) are rapidly developing technologies that are transforming our society. The disruptive transformation of the economy and society is expected due to the data collected by these systems, rather than the technological aspects of such as networks, embedded systems, and cloud technology. However, to create value out of the data, it must be transformed into information and therefore, expertise in data analytics and machine learning is the key component of future smart systems in cities and other applications.

Big Data Analytics in Cyber-Physical Systems

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 to evaluate the extracted data of those systems. Each chapter provides different tools and applications in order to present a broad list of data analytics and machine learning tools in multiple IoT applications. Additionally, this volume addresses the education 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.

Key Features:

Fills the gap between IoT, CPS, and mathematical modeling

Numerous use cases that discuss how concepts are applied in different domains and applications.

Provides "best practices," "real developments", and "winning stories" to complement technical information.

Uniquely covers contents within the context of mathematical foundations of signal processing and machine learning in CPS and IoT.

About the Editors:

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.

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.

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.

Details zur Ausgabe

• Verlag: Elsevier
• Erscheinungsdatum: 2019
• ISBN 10: 0128166371
• ISBN 13: 9780128166376
• Einband: Tapa blanda
• Sprache: Englisch
• Anzahl der Seiten: 396
• Herausgeber: Dartmann Guido, Song Houbing, Schmeink Anke