Big Data Analytics for Cyber-Physical Systems (eBook)

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

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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 implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational 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. Bridges the gap between IoT, CPS, and mathematical modelling Features numerous use cases that discuss how concepts are applied in different domains and applications Provides "e;best practices"e;, "e;winning stories"e; and "e;real-world examples"e; to complement innovation Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT