Big data analytics for cyber-physical systems : machine learning for the internet of things
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 interacti...
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
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Amsterdam
Elsevier
2019
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| Vydání: | 1 |
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| ISBN: | 9780128166376, 0128166371 |
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| 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. . |
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| AbstractList | 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. . |
| Author | Song, Houbing Schmeink, Anke Dartmann, Guido |
| Author_xml | – sequence: 1 fullname: Dartmann, Guido – sequence: 2 fullname: Song, Houbing – sequence: 3 fullname: Schmeink, Anke |
| BackLink | https://cir.nii.ac.jp/crid/1130566852121582464$$DView record in CiNii |
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| DOI | 10.1016/C2018-0-00208-X |
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| Discipline | Engineering Computer Science |
| EISBN | 0128166460 9780128166468 |
| Edition | 1 |
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| Notes | Includes bibliographical references and index |
| OCLC | 1108871637 |
| PQID | EBC5829827 |
| PageCount | 398 |
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| PublicationDate | c2019 2019 2019-07-15 |
| PublicationDateYYYYMMDD | 2019-01-01 2019-07-15 |
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| PublicationPlace | Amsterdam |
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| Publisher | Elsevier |
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| Snippet | Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and... |
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| SubjectTerms | Big data Big data -- Data processing Embedded computer systems Internet of things Machine learning |
| TableOfContents | 6.1. Preprocessing and inference chain -- 6.2. Data selection and evaluation approach -- 7. Evaluation and results -- 7.1. Parameter tuning with cross-validation (zone 1) -- 7.2. Performance evaluation (zone 2) -- 7.3. Discussion -- 8. Conclusion and future work -- References -- Chapter 6: Portable implementations for heterogeneous hardware platforms in autonomous driving systems -- 1. Advanced driver-assistance systems -- 2. Programming challenges -- 2.1. Understanding scaling-behavioral details -- 2.1.1. Metrics -- 2.1.2. Case study -- 2.1.3. Characteristics -- 2.2. Migration and architecture mapping -- 3. Parallel programming approaches -- 3.1. Basic multicore parallelization -- 3.1.1. SIMD vectorization -- 3.1.2. Automated parallelization tools -- 3.2. Programming heterogeneous architectures -- 3.2.1. Programming accelerators -- 3.2.2. Architecture mapping -- 4. Unification -- 4.1. Unifying heterogeneous software-code -- 4.2. Coherent shared memory -- 4.3. Abstracting middleware layers -- 5. Summary -- References -- Chapter 7: AI-based sensor platforms for the IoT in smart cities -- 1. Introduction -- 2. Function units of an IoT sensor -- 3. More than one sensor element -- 4. The communication interface -- 5. Embedded O/S requirements -- 6. Artificial intelligence embedded -- 7. Classification and regression using machine learning algorithms -- 8. Learning process required -- 9. AI-based IoT sensor system -- 10. Decentralized intelligence -- 11. Conclusions -- References -- Chapter 8: Predicting energy consumption using machine learning -- 1. Introduction -- 2. Data profiling -- 2.1. Data integration -- 2.2. Outlier detection -- 2.3. Feature selection -- 3. Learning from data -- 3.1. Multiple regression -- 3.2. Artificial neural networks -- 3.3. Valuation metrics -- 4. Related work -- 4.1. Data preprocessing -- 4.2. Procedure models Front Cover -- Big Data Analytics forCyber-Physical Systems: Machine Learning for the Internet of Things -- Copyright -- Contents -- Contributors -- Foreword -- Acknowledgments -- Introduction -- Chapter 1: Data analytics and processing platforms in CPS -- 1. Open source versus proprietary software -- 2. Data types -- 3. Easy data visualization using code -- 4. Statistical measurements in CPS data -- 5. Statistical methods, models, and techniques: Brief introduction -- 6. Analytics and statistics versus ML techniques -- 7. Data charts -- 8. Machine logs analysis and dashboarding -- 9. Conclusion -- References -- Chapter 2: Fundamentals of data analysis and statistics -- 1. Introduction -- 2. Useful software tools -- 2.1. Software for statistical computations -- 2.2. Software for interactive graphical data representation -- 3. Fundamentals of statistics -- 3.1. Features and scalings -- 3.1.1. Nominal, ordinal, and metric features -- 3.1.2. Discrete and continuous features -- 3.2. Characterization of univariate distributions -- 3.2.1. Nominal features -- 3.2.2. Ordinal features and quantiles -- 3.2.3. Metric features-Grapical representation -- 3.2.4. Metric features-Characteristic numbers -- 3.3. Characterization of multivariate distributions -- 4. Regression: Fitting functional models to the data -- 4.1. Linear regression -- 4.1.1. Linear least-squares regression -- 4.1.2. Linear total least-squares regression -- 4.2. Nonlinear regression -- 4.2.1. Polynomials -- 4.2.2. Population growth-Logistic function -- 4.3. Logistic regression -- 5. Minimizing redundancy: Factor analysis and principle component analysis -- 6. Explore unknown data: Cluster analysis -- 6.1. k-Means -- 6.2. Hierarchical clustering -- 6.3. Density-based clustering -- 6.4. Outlier detection -- 7. Conclusion -- References 5.4. Results -- 6. Conclusion -- References -- Chapter 11: Machine learning-based artificial nose on a low-cost IoT-hardware -- 1. Introduction -- 2. Related work -- 3. Temperature-modulated gas sensing -- 3.1. Principle component analysis -- 4. Support vector machine -- 4.1. Separable case -- 4.2. Nonseparable case -- 4.3. ECOC model -- 5. Feature selection -- 5.1. Wrappers -- 6. Technical implementation -- 6.1. Equipment -- 6.1.1. Hardware IoT device -- 6.1.2. Semiconductor gas sensor BME680 -- 6.1.3. Cloud computer -- 6.2. Measurement process -- 7. Experimental setup and results -- 8. Conclusion -- References -- Glossary -- Chapter 12: Machine Learning in future intensive care-Classification of stochastic Petri Nets via continuous-time Markov ... -- 1. Introduction -- 1.1. Digitalization of the intensive care unit -- 1.2. Petri nets and data analytics for clinical data -- 1.3. Our contribution -- 2. Background -- 2.1. Stochastic Petri nets -- 2.2. Continuous-time Markov chains -- 3. Methodology -- 3.1. Quantization -- 3.2. EM-algorithm for CTMC parameter learning -- 3.3. SPN topology estimation -- 3.4. Support vector machine -- 4. Simulation setup -- 4.1. SPN structure -- 4.2. Training procedure -- 5. Results -- 6. Conclusion and future work -- References -- Chapter 13: Privacy issues in smart cities: Insights into citizens perspectives toward safe mobility in urban environments -- 1. Introduction -- 2. Automated driving and privacy issues -- 2.1. Willingness to share data in automated driving, data types, and storage -- 2.2. Acceptance of cameras on public traffic routes -- 3. Crime surveillance and privacy issues -- 3.1. Acceptance of surveillance technologies at different locations -- 3.2. Trade-off between safety and privacy regarding acceptance of surveillance technologies -- 4. Conclusions, outlook, and future research directions Chapter 3: Density-based clustering techniques for object detection and peak segmentation in expanding data fields -- 1. Introduction -- 2. Related work -- 3. A brief introduction to density-based clustering -- 4. Formal extensions of density-based clustering -- 4.1. Operation on lattice-based data -- 5. Clustering strategy for time-expandable data sets -- 5.1. Legacy expansion approach -- 5.2. Horizons of processing areas with incomplete data -- 5.3. Algorithmic strategy for data with expansion -- 6. Evaluation and results -- 7. Conclusion -- References -- Chapter 4: Security for a regional network platform in IoT -- 1. Introduction -- 2. Regional network security -- 2.1. Regional network -- 2.2. Threats and design requirements -- 3. Proactive distributed authentication framework for a regional network -- 3.1. Automatic trustworthy registration -- 3.2. Distributed access authentication -- 3.3. Mutual authentication procedure -- 3.4. Temporary registration and authentication in the event of a disaster -- 3.5. Handover authentication procedure -- 3.6. Blacklist sharing procedure -- 4. Discussion -- 5. Function implementations -- 6. Network setup and performance evaluations -- 6.1. Network setup -- 6.2. Metrics and experimental setup -- 6.3. Experimental results -- 7. Conclusions -- References -- Further reading -- Chapter 5: Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure -- 1. Introduction -- 2. Related work -- 3. Fundamentals and background -- 3.1. Introduction to support vector machines -- 3.2. Introduction to principal component analysis -- 4. System setup and architecture -- 4.1. Platform and embedded system concept -- 4.2. Ultrasonic sensing properties and synchronization -- 4.3. Real-world evaluation scenario -- 5. Data annotation and trainging methodoloy -- 6. Proposed method References 4.3. Prognosis -- 4.4. Evaluation -- 4.5. Conclusion -- 5. Further thoughts -- Acknowledgments -- References -- Chapter 9: Reinforcement learning and deep neural network for autonomous driving -- 1. Introduction -- 1.1. Motivation -- 1.2. Objective -- 1.3. Related work -- 2. Signal model -- 2.1. Scenario -- 2.2. Sensor signal model -- 3. Machine learning -- 3.1. Deep learning and neural networks -- 3.1.1. Feed-forward network -- 3.1.2. Recurrent neural network -- 3.1.3. Loss function -- 3.2. Reinforcement learning -- 3.2.1. Markov decision process -- 3.2.2. Reward -- 3.3. Policy -- 3.3.1. Value functions -- 3.3.2. Temporal difference learning -- 3.3.3. Policy gradient method -- 3.3.4. Actor-critic algorithm -- 3.3.5. On-policy and off-policy -- 3.3.6. Exploration vs exploitation -- 3.4. Deep deterministic policy gradients -- 3.4.1. Experience replay -- 3.4.2. Separate target network -- 3.4.3. Update rule for weights of the neural network -- 4. Simulation -- 4.1. Developing a scenario -- 4.2. Dynamic environment -- 4.3. Testbed -- 5. Conclusion and future work -- References -- Chapter 10: On the use of evolutionary algorithms for localization and mapping: Infrastructure monitoring in smart cities ... -- 1. Introduction -- 1.1. Monitoring infrastructure -- 1.2. State-of-the-art -- 1.3. Chapter organization -- 2. Centralized offline localization and mapping -- 3. Evolutionary localization and mapping -- 3.1. EVOLAM overview -- 3.2. MOEA -- 3.3. Simulation -- 3.4. Localization -- 3.4.1. Measurement model -- 3.4.2. Motion model -- 3.4.3. Maximum a posteriori optimal localization -- 3.5. Environment mapping -- 4. Multiobjective evolutionary algorithms -- 4.1. Dominance-based MOEA -- 4.2. Indicator-based MOEA -- 4.3. Other approaches -- 5. Case-study -- 5.1. Problem statement -- 5.2. Objective functions -- 5.3. Adopted MOEA -- 5.3.1. Simulation |
| Title | Big data analytics for cyber-physical systems : machine learning for the internet of things |
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