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{ "item_title" : "Fog Computing, Deep Learning and Big Data Analytics-Research Directions", "item_author" : [" C. S. R. Prabhu "], "item_description" : "1Introduction1.1.A new economy based on IOT emerging by 20151.1.1Emergence of IOT1.1.2Smart Cities and IOT1.1.3Stages of IOT and Stakeholders1.1.3.1Stages of IOT1.1.3.2Stakeholders1.1.3.3Practical Down Scaling1.1.4Analytics1.1.5Analytics from the Edge to Cloud [179]1.1.6Security and Privacy Issues and Challenges in Internet of Things (IOT)1.1.7Access1.1.8Cost Reduction1.1.9Opportunities and Business Model1.1.10Content and Semantics1.1.11Data based Business models coming out of IOT1.1.12Future of IOT1.1.12.1Technology Drivers1.1.12.2Future possibilities1.1.12.3Challenges and Concerns1.1.13Big Data Analytics and IOT1.1.13.1Infrastructure for integration of Big Date with IOT1.2The Technological challenges of an IOT driven Economy1.3Fog Computing Paradigm as a solution1.4Definitions of Fog Computing1.5Characteristics of Fog computing1.6Architectures of Fog computing1.6.1Cloudlet Architecture1.6.2IoX Architecture1.6.3Local Grid's Fog Computing platform1.6.4Parstream1.6.5Para Drop1.6.6Prismatic Vortex1.7Designing a robust Fog computing platform 1.8Present challenges in designing Fog Computing Platform1.9Platform and Applications1.9.1Components of Fog Computing Platform1.9.2Applications and case studies1.9.2.1Health data management and Health care1.9.2.2Smart village health care1.9.2.3Smart home1.9.2.4Smart vehicle and vehicular fog computing1.9.2.5Augmented Reality applications2.Fog Application management2.1Introduction2.2Application Management Approaches2.3Performance2.4Latency Aware Application Management2.5Distributed Application Development in Fog2.6Distributed Data flow approach2.7Resource Coordination Approaches3Fog Analytics3.1Introduction3.2Fog Computing3.3Stream data processing3.4Stream Data Analytics and Fog computing3.4.1Machine Learning for Big Data Stream data and Fog Analytics3.4.1.1Supervised Learning3.4.1.2Distributed Decision Trees", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/81/133/208/9811332088_b.jpg", "price_data" : { "retail_price" : "159.99", "online_price" : "159.99", "our_price" : "159.99", "club_price" : "159.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Fog Computing, Deep Learning and Big Data Analytics-Research Directions|C. S. R. Prabhu

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

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Overview

1Introduction
1.1.A new economy based on IOT emerging by 2015
1.1.1Emergence of IOT
1.1.2Smart Cities and IOT
1.1.3Stages of IOT and Stakeholders
1.1.3.1Stages of IOT
1.1.3.2Stakeholders
1.1.3.3Practical Down Scaling
1.1.4Analytics
1.1.5Analytics from the Edge to Cloud [179]
1.1.6Security and Privacy Issues and Challenges in Internet of Things (IOT)
1.1.7Access
1.1.8Cost Reduction
1.1.9Opportunities and Business Model
1.1.10Content and Semantics
1.1.11Data based Business models coming out of IOT
1.1.12Future of IOT
1.1.12.1Technology Drivers
1.1.12.2Future possibilities
1.1.12.3Challenges and Concerns
1.1.13Big Data Analytics and IOT
1.1.13.1Infrastructure for integration of Big Date with IOT
1.2The Technological challenges of an IOT driven Economy
1.3Fog Computing Paradigm as a solution
1.4Definitions of Fog Computing
1.5Characteristics of Fog computing
1.6Architectures of Fog computing
1.6.1Cloudlet Architecture
1.6.2IoX Architecture
1.6.3Local Grid's Fog Computing platform
1.6.4Parstream
1.6.5Para Drop
1.6.6Prismatic Vortex
1.7Designing a robust Fog computing platform

1.8Present challenges in designing Fog Computing Platform
1.9Platform and Applications
1.9.1Components of Fog Computing Platform
1.9.2Applications and case studies
1.9.2.1Health data management and Health care
1.9.2.2Smart village health care
1.9.2.3Smart home
1.9.2.4Smart vehicle and vehicular fog computing
1.9.2.5Augmented Reality applications
2.Fog Application management
2.1Introduction
2.2Application Management Approaches
2.3Performance
2.4Latency Aware Application Management
2.5Distributed Application Development in Fog
2.6Distributed Data flow approach
2.7Resource Coordination Approaches
3Fog Analytics
3.1Introduction
3.2Fog Computing
3.3Stream data processing
3.4Stream Data Analytics and Fog computing
3.4.1Machine Learning for Big Data Stream data and Fog Analytics
3.4.1.1Supervised Learning
3.4.1.2Distributed Decision Trees

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Details

  • ISBN-13: 9789811332081
  • ISBN-10: 9811332088
  • Publisher: Springer
  • Publish Date: January 2019
  • Dimensions: 9.21 x 6.14 x 0.25 inches
  • Shipping Weight: 0.67 pounds
  • Page Count: 71

Related Categories

    1. Books
    2. Computers
    3. Database Administration & Management
    1. Books
    2. Computers
    3. Data Science - Data Modeling & Design
    1. Books
    2. Computers
    3. Computer Science

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