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

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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.

De la 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.

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.

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.

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.