Big Data Analytics for Cyber-Physical Systems
Machine Learning for the Internet of Things
Published on 15. July 2019
396 pages
978-0-12-816646-8 (ISBN)
Description
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
More details
Other editions
Additional editions
Guido Dartmann | Houbing Herbert Song | Anke Schmeink
Big Data Analytics for Cyber-Physical Systems
Machine Learning for the Internet of Things
Book
07/2019
Elsevier
€114.50
Shipment within 15-20 days
Content
1. Data analytics and processing platforms in CPS2. Fundamentals of data analysis and statistics3. Density-based clustering techniques for object detection and peak segmentation in expanding data fields4. Security for a regional network platform in IoT5. Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure6. Portable implementations for heterogeneous hardware platforms in autonomous driving systems7. AI-based sensor platforms for the IoT in smart cities8. Predicting energy consumption using machine learning9. Reinforcement learning and deep neural network for autonomous driving10. On the use of evolutionary algorithms for localization and mapping: Infrastructure monitoring in smart cities via miniaturized autonomous11. Machine learning-based artificial nose on a low-cost IoT-hardware12. Machine Learning in future intensive care-Classification of stochastic Petri Nets via continuous-time Markov chains13. Privacy issues in smart cities: Insights into citizens' perspectives toward safe mobility in urban environments14. Utility privacy trade-off in communication systems15. IoT-workshop: Blueprint for pupils education in IoT16. IoT-workshop: Application examples for adult education
