عنوان

Fog Computing, Deep Learning and Big Data Analytics-Research Directions /

(Number (ISBN

9789811332098

(Number (ISBN

9789811332104

(Number (ISBN

9811332096

(Number (ISBN

981133210X

Erroneous ISBN

9789811332081

Erroneous ISBN

9811332088

Title Proper

Fog Computing, Deep Learning and Big Data Analytics-Research Directions /

General Material Designation

[Book]

First Statement of Responsibility

C.S.R. Prabhu.

Place of Publication, Distribution, etc.

Singapore :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2019.

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes bibliographical references and index.

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

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Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9789811332098

Title

Fog Computing, Deep Learning and Big Data Analytics-Research Directions.

International Standard Book Number

9789811332081

Big data.

Cloud computing.

Big data.

Cloud computing.

COMPUTERS-- Computer Literacy.

COMPUTERS-- Computer Science.

COMPUTERS-- Data Processing.

COMPUTERS-- Hardware-- General.

COMPUTERS-- Information Technology.

COMPUTERS-- Machine Theory.

COMPUTERS-- Reference.

COM-- 013000

COM-- 014000

COM-- 018000

COM-- 032000

COM-- 037000

COM-- 052000

COM-- 067000

UN

UN

Number

Edition

Class number

Prabhu, C. S. R.

Date of Transaction

20200823234629.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y