Fog Computing, Deep Learning and Big Data Analytics-Research Directions. (2019)

• Record Type: Book
• Further Information: Note: C.S.R. Prabhu.
• Authors: Prabhu, C. S. R
• Contents: 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.
• Publisher Details: Singapore : Springer
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource (71 pages)
• Subjects: Computer science; Big data; Data structures (Computer scienc); Information systems; Computers -- Data Modeling & Design; Computers -- Data Processing; Algorithms & data structures; Public administration; Computers -- Database Management -- General; Databases
• Languages: English
• ISBNs: 9789811332098
• Related ISBNs: 9789811332081
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.