Institute for Theoretical Information Technology

Big Data Analytics for Cyber-Physical Systems

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 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.

Key Features

• 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.

Table of Contents

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

BibT_EX Reference Entry 

@book{DaSoSc19b,
	author = {Guido Dartmann and Houbing Song and Anke Schmeink},
	title = "Big Data Analytics for Cyber-Physical Systems",
	pages = "396",
	publisher = "Elsevier",
	series = "Machine Learning for the Internet of Things",
	ISBN = "978-0-12-816637-6",
	month = Jul,
	year = 2019,
	hsb = RWTH-2019-07876,
	}

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