Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of ThingsGuido Dartmann, Houbing Herbert Song, Anke Schmeink Elsevier, 15.07.2019 - 396 Seiten 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 |
Inhalt
| 1 | |
| 25 | |
Densitybased clustering techniques for object detection and peak segmentation in expanding data fields | 49 |
Security for a regional network platform in IoT | 65 |
Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure | 91 |
Portable implementations for heterogeneous hardware platforms in autonomous driving systems | 113 |
AIbased sensor platforms for the IoT in smart cities | 145 |
Predicting energy consumption using machine learning | 167 |
Machine learningbased artificial nose on a lowcost IoThardware | 239 |
Machine Learning in future intensive careClassification of stochastic Petri Nets via continuoustime Markov | 259 |
Privacy issues in smart cities Insights into citizens perspectives toward safe mobility in urban environments | 275 |
Utility privacy tradeoff in communication systems | 293 |
IoTworkshop Blueprint for pupils education in IoT | 315 |
IoTworkshop Application examples for adult education | 345 |
| 365 | |
Back Cover | 375 |
Reinforcement learning and deep neural network for autonomous driving | 187 |
On the use of evolutionary algorithms for localization and mapping Infrastructure monitoring in smart cities | 215 |
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Big Data Analytics for Cyber-Physical Systems: Machine Learning for the ... Guido Dartmann,Houbing Herbert Song,Anke Schmeink Eingeschränkte Leseprobe - 2019 |
Häufige Begriffe und Wortgruppen
accelerators Accessed agent algorithms analysis Analytics for Cyber-Physical applications approach authentication Big Data Analytics box plot channel chapter classification cloud clustering communication Comput core point Cyber-Physical Systems daemon data points data set databases distributed driving embedded system environment evaluation example FPGA framework function hackathon heterogeneous hyperplane hyperthreaded IEEE implementation infrastructure input interface International Conference Internet of Things IoT sensor layer linear machine learning measurement methods mobile MOEA monitoring multicore neural networks NVIDIA OpenCL OpenMP optimization output parallel parameters parking lot parking space PDAF performance platforms prediction problem procedure programming regional network regression requires RNKS RWTH Aachen University scenarios sensor data server setup simulation smart cities smartphone solutions Splunk statistical support vector machine task technologies temperature tion topology traffic utility privacy trade-off variable vector wireless Ziefle