Fog computing, deep learning and big data analytics-research directions

목차 (Table of Contents)

• 자료제공 :
• 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
• 3.5.1.3 Clustering Methods for Big Data
• 3.4.1.4 Distributed Parallel Association Rule Mining Techniques for Big Data Scenario
• 3.4.1.5 Dynamic Association Mining
• 3.4.2 Deep Learning Techniques
• 3.4.3 Applications of Deep Learning in Big Data Analytics
• 3.4.3.1 Semantic Indexing
• 3.4.3.2 Discriminative Tasks and Semantic Tagging
• 3.4.4. Deep Learning Challenges in Big Data Analytics
• 3.4.4.1 Incremental Learning for Non-Stationary Data
• 3.4.4.2 High-Dimensional Data
• 3.4.4.3 Large-Scale Models
• 3.5 Different Approaches of Fog Analytics
• 3.6 Comparision
• 3.7 Cloud Solutions for the Edge Analytics
• 4 Fog Security and Privary
• 4.1 Introduction
• 4.2 Secure Communications in Fog Computing
• 4.3 Authentication
• 4.4 Privacy Issues
• 4.5 User Behaviour Profiling
• 4.6 Dynamic Fog Nodes and EUs
• 4.7 Malicious Attacks
• 4.8 Malicious Insider in the Cloud
• 4.9 Man in the Middle Attack
• 4.10 Secured Multi-Tenancy
• 4.11 Backup and Recovery
• 5 Research Directions
• 6 CONCLUSION
• References