Fog Computing, Deep Learning and Big Data Analytics-Research Directions / C.S.R. Prabhu.
This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smar...
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Full Text (via Skillsoft) |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2019.
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| Subjects: |
MARC
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| 100 | 1 | |a Prabhu, C. S. R. | |
| 245 | 1 | 0 | |a Fog Computing, Deep Learning and Big Data Analytics-Research Directions / |c C.S.R. Prabhu. |
| 264 | 1 | |a Singapore : |b Springer, |c 2019. | |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF file page (EBSCO, viewed January 10, 2019). | |
| 505 | 0 | |a Intro; Preface; Contents; About the Author; Abstract; 1 Introduction; 1.1 A New Economy Based on IoT Emerging from 2015; 1.1.1 Emergence of IoT; 1.1.2 Smart Cities and IoT; 1.1.3 Stages of IoT and Stakeholders; 1.1.4 Analytics; 1.1.5 Analytics from the Edge to Cloud [179]; 1.1.6 Security and Privacy Issues and Challenges in the Internet of Things (IoT); 1.1.7 Access; 1.1.8 Cost Reduction; 1.1.9 Opportunities and Business Model; 1.1.10 Content and Semantics; 1.1.11 Data-Based Business Models Coming Out of IoT; 1.1.12 Future of IoT; 1.1.13 Big Data Analytics and IoT | |
| 505 | 8 | |a 1.2 The Technological Challenges of an IoT-Driven Economy1.3 Fog Computing Paradigm as a Solution; 1.4 Definitions of Fog Computing; 1.5 Characteristics of Fog Computing; 1.6 Architectures of Fog Computing; 1.6.1 Cloudlet Architecture [11]; 1.6.2 IoX Architecture; 1.6.3 Local Grid's Fog Computing Platform; 1.6.4 ParStream; 1.6.5 ParaDrop; 1.6.6 Prismatic Vortex; 1.7 Designing a Robust Fog Computing Platform; 1.8 Present Challenges in Designing Fog Computing Platform; 1.9 Platform and Applications; 1.9.1 Components of Fog Computing Platform; 1.9.2 Applications and Case Studies | |
| 505 | 8 | |a 2 Fog Application Management2.1 Introduction; 2.2 Application Management Approaches; 2.3 Performance; 2.4 Latency-Aware Application Management; 2.5 Distributed Application Development in Fog; 2.6 Distributed Data Flow Approach; 2.6.1 Latency-Aware Fog Application Management; 2.7 Resource Coordination Approaches; 3 Fog Analytics; 3.1 Introduction; 3.2 Fog Computing; 3.3 Stream Data Processing; 3.4 Stream Data Analytics, Big Data Analytics and Fog Computing; 3.4.1 Machine Learning for Big Data, Stream Data and Fog Ecosystem; 3.4.2 Deep Learning Techniques; 3.4.3 Deep Learning and Big Data | |
| 505 | 8 | |a 3.5 Different Approaches to Fog Analytics3.6 Comparison; 3.7 Cloud Solutions for the Edge Analytics; 4 Fog Security and Privacy; 4.1 Introduction; 4.2 Authentication; 4.3 Privacy Issues; 4.4 User Behaviour Profiling; 4.5 Data Theft by Insider; 4.6 Man-in-the-Middle Attack; 4.7 Failure Recovery and Backup Mechanisms; 5 Research Directions; 5.1 Harnessing Temporal Dimension of IoT Data for Customer Relationship Management (CRM); 5.2 Adding Semantics to IoT Data; 5.3 Towards a Semantic Web of IoT; 5.4 Diversity, Interoperability and Standardization in IoT; 5.5 Data Management Issues in IoT | |
| 505 | 8 | |a 5.6 Data Provenance5.7 Data Governance and Regulation; 5.8 Context-Aware Resource and Service Provisioning; 5.9 Sustainable and Reliable Fog Computing; 5.10 Interoperability Among Fog Nodes; 5.11 Distributed Processing of Application; 5.12 Power Management Within Fog; 5.13 Multi-tenancy Support in Fog; 5.14 Programming Language and Standards for Fog; 5.15 Simulation in Fog; 5.16 Mobile Fog: Research Opportunities; 5.17 Deploying Deep Learning Integrated with Fog Nodes for Fog Analytics; 5.18 Directions of Research in Interfacing Deep Learning with Big Data Analytics; 6 Conclusion; References | |
| 520 | |a This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions. | ||
| 650 | 0 | |a Cloud computing. | |
| 650 | 0 | |a Big data. | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Cloud computing |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Prabhu, C.S.R. |t Fog Computing, Deep Learning and Big Data Analytics-Research Directions. |d Singapore : Springer, 2019 |z 9811332088 |z 9789811332081 |w (OCoLC)1056744929 |
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