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

Prabhu, C.S.R.

Brossura

ISBN 10: 981133210X ISBN 13: 9789811332104

Casa editrice: Springer, 2019

Riassunto

1

Introduction

1.1.

A new economy based on IOT emerging by 2015

1.1.1

Emergence of IOT

1.1.2

Smart Cities and IOT

1.1.3

Stages of IOT and Stakeholders

1.1.3.1

Stages of IOT

1.1.3.2

Stakeholders

1.1.3.3

Practical Down Scaling

1.1.4

Analytics

1.1.5

Analytics from the Edge to Cloud [179]

1.1.6

Security and Privacy Issues and Challenges in 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

1.1.12.1

Technology Drivers

1.1.12.2

Future possibilities

1.1.12.3

Challenges and Concerns

1.1.13

Big Data Analytics and IOT

1.1.13.1

Infrastructure for integration of Big Date with IOT

1.2

The Technological challenges of an IOT driven Economy

1.3

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

1.6.2

IoX Architecture

1.6.3

Local Grid's Fog Computing platform

1.6.4

Parstream

1.6.5

Para Drop

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

1.9.2.1

Health data management and Health care

1.9.2.2

Smart village health care

1.9.2.3

Smart home

1.9.2.4

Smart vehicle and vehicular fog computing

1.9.2.5

Augmented Reality applications

2.

Fog Application management

2.1

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

2.7

Resource Coordination Approaches

3

Fog Analytics

3.1

Introduction

3.2

Fog Computing

3.3

Stream data processing

3.4

Stream Data Analytics and Fog computing

3.4.1

Machine Learning for Big Data Stream data and Fog Analytics

3.4.1.1

Supervised Learning

3.4.1.2

Distributed Decision Trees

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2019
• ISBN 10: 981133210X
• ISBN 13: 9789811332104
• Rilegatura: Paperback
• Lingua: Inglese
• Contatto del produttore: non disponibile