Fog Computing, Deep Learning and Big Data Analytics-Research Directions / C.S.R. Prabhu.
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Full Text (via Springer) |
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| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2019.
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Table of Contents:
- 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.
- 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.
- 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.
- 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.
- 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.