Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things

Cyber-physical systems (CPS) and the Internet of Things (IoT) are rapidly developing technologies that are transforming our society. The disruptive transformation of the economy and society is expected due to the data collected by these systems, rather than the technological aspects of such as networks, embedded systems, and cloud technology. However, to create value out of the data, it must be transformed into information and therefore, expertise in data analytics and machine learning is the key component of future smart systems in cities and other applications. Big Data Analytics in Cyber-Physical Systems 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 to evaluate the extracted data of those systems. Each chapter provides different tools and applications in order to present a broad list of data analytics and machine learning tools in multiple IoT applications. Additionally, this volume addresses the education 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. Fills the gap between IoT, CPS, and mathematical modelingNumerous use cases that discuss how concepts are applied in different domains and applicationsProvides best practices, real developments, and winning stories to complement technical informationUniquely covers contents within the context of mathematical foundations of signal processing and machine learning in CPS and IoT INDICE: 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 of 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 the heating energy consumption of a building for several subsequent days using machine learning methods 9. Reinforcement Learning and Deep Neural Network for Autonomous Driving 10. On the Use of Evolutionary Algorithms for Localization and Mapping of Miniaturized Autonomous Sensory Agents for Infrastructure Monitoring in Smart Cities 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 towards 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

• ISBN: 978-0-12-816637-6
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 360
• Fecha Publicación: 01/07/2019
• Nº Volúmenes: 1
• Idioma: Inglés