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 "best practices", "winning stories" and "real-world examples" to complement innovation
Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT

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Yes, you can access Big Data Analytics for Cyber-Physical Systems by Guido Dartmann,Houbing H. Song,Anke Schmeink,Houbing Herbert Song in PDF and/or ePUB format, as well as other popular books in Law & Law Theory & Practice. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Elsevier

Year

2019

ISBN

9780128166468

Topic

Law

Subtopic

Law Theory & Practice

Index

Law

Cover image
Title page
Table of Contents
Copyright
Contributors
Foreword
Acknowledgments
Introduction
Chapter 1: Data analytics and processing platforms in CPS
Chapter 2: Fundamentals of data analysis and statistics
Chapter 3: Density-based clustering techniques for object detection and peak segmentation in expanding data fields
Chapter 4: Security for a regional network platform in IoT
Chapter 5: Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure
Chapter 6: Portable implementations for heterogeneous hardware platforms in autonomous driving systems
Chapter 7: AI-based sensor platforms for the IoT in smart cities
Chapter 8: Predicting energy consumption using machine learning
Chapter 9: Reinforcement learning and deep neural network for autonomous driving
Chapter 10: On the use of evolutionary algorithms for localization and mapping: Infrastructure monitoring in smart cities via miniaturized autonomous sensory agents
Chapter 11: Machine learning-based artificial nose on a low-cost IoT-hardware
Chapter 12: Machine Learning in future intensive care—Classification of stochastic Petri Nets via continuous-time Markov chains
Chapter 13: Privacy issues in smart cities: Insights into citizens’ perspectives toward safe mobility in urban environments
Chapter 14: Utility privacy trade-off in communication systems
Chapter 15: IoT-workshop: Blueprint for pupils education in IoT
Chapter 16: IoT-workshop: Application examples for adult education
Index