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

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

C.S.R. Prabhu

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71 pages, parution le 15/01/2019
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Résumé

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.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
3.5.1.3Clustering Methods for Big Data
3.4.1.4Distributed Parallel Association Rule Mining Techniques for Big Data Scenario
3.4.1.5Dynamic Association Mining
3.4.2Deep Learning Techniques
3.4.3Applications of Deep Learning in Big Data Analytics
3.4.3.1Semantic Indexing
3.4.3.2Discriminative Tasks and Semantic Tagging
3.4.4.Deep Learning Challenges in Big Data Analytics
3.4.4.1Incremental Learning for Non-Stationary Data
3.4.4.2High-Dimensional Data
3.4.4.3Large-Scale Models
3.5Different Approaches of Fog Analytics
3.6Comparision

3.7Cloud Solutions for the Edge Analytics
4Fog Security and Privary
4.1Introduction
4.2Secure Communications in Fog Computing
4.3Authentication
4.4Privacy Issues
4.5User Behaviour Profiling
4.6Dynamic Fog Nodes and EUs
4.7Malicious Attacks
4.8Malicious Insider in the Cloud
4.9Man in the Middle Attack
4.10Secured Multi-Tenancy
4.11Backup and Recovery
5Research Directions
6CONCLUSION
References
 Dr. Chivukula Sree Rama Prabhu has held prestigious positions with Government of India and various Institutions. He retired as the Director General of National Informatics Centre (NIC) Ministry of Electronics and Information Technology Government of India, New Delhi, and has worked in various capacities at Tata Consultancy Services (TCS), CMC, TES and TELCO (now Tata Motors). He was also an international resource faculty for the Programs of APO (Asian Productivity Organization), and represented India on the International Panel at Venture 2004 held by APO at Osaka, Japan. He taught and researched at the University of Central Florida, Orlando and also had a brief stint as a Consultant to NASA Cape Canaveral.
Mr. Prabhu was unanimously elected and served as the Chairman of Computer Society of India (CSI), Hyderabad Chapter. He is presently working as an Advisor at KL University, Vijayawada, Andhra Pradesh and as a Director, Research and Innovation at Keshav Memorial Institute of Technology (KMIT), Hyderabad. He obtained his master's degree in Electrical Engineering with specialization in Computer Science from the Indian Institute of Technology, Bombay after a bachelor's degree in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Hyderabad in 1976. He has guided a large number of student research projects at master's level and has published several papers.

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Éditeur(s) Apress
Auteur(s) C.S.R. Prabhu
Parution 15/01/2019
Nb. de pages 71
EAN13 9789811332081

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