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

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ISBN 10: 0128166371 / ISBN 13: 9780128166376

Edité par Elsevier Science Publishing Co Inc, 2019

A propos de cet article

Zustand: Gut | Seiten: 396 | Sprache: Englisch | Produktart: Bücher.

N° de réf. du vendeur 33326549/3

Détails bibliographiques

• Titre: Big Data Analytics for Cyber-Physical Systems : Machine Learning for the Internet of Things
• Éditeur: Elsevier Science Publishing Co Inc
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 0128166371
• ISBN à 13 chiffres: 9780128166376
• Reliure: Couverture souple
• État de l'article: Gut

A propos de ce titre

Synopsis

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

À propos des auteurs

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.