Big data analytics for cyber-physical systems: Machine learning for the internet of things

Authors

G. Dartmann, H. Song, A. Schmeink,

Abstract

        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.

https://www.elsevier.com/books/big-data-analytics-for-cyber-physical-systems /dartmann/978-0-12-816637-6

BibTEX Reference Entry 

@book{DaSoSc19,
	author = {Guido Dartmann and Houbing Song and Anke Schmeink},
	title = "Big data analytics for cyber-physical systems: Machine learning for the internet of things",
	pages = "1-360",
	publisher = "Elsevier",
	ISBN = "9780128166376",
	month = Jul,
	year = 2019,
	hsb = RWTH-2019-04701,
	}

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