Big Data Analysis for Green Computing: Concepts and Applications (Green Engineering and Technology)

✍ Scribed by Rohit Sharma (editor), Dilip Kumar Sharma (editor), Dhowmya Bhatt (editor), Binh Thai Pham (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 187
• Edition: 1
• Category: Library

✦ Synopsis

This book focuses on big data in business intelligence, data management, machine learning, cloud computing, and smart cities. It also provides an interdisciplinary platform to present and discuss recent innovations, trends, and concerns in the fields of big data and analytics.

Big Data Analysis for Green Computing: Concepts and Applications presents the latest technologies and covers the major challenges, issues, and advances of big data and data analytics in green computing. It explores basic as well as high-level concepts. It also includes the use of machine learning using big data and discusses advanced system implementation for smart cities.

The book is intended for business and management educators, management researchers, doctoral scholars, university professors, policymakers, and higher academic research organizations.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Multi-Criteria and Fuzzy-Based Decision Making: Applications in Environment Pollution Control for Sustainable Development
1.1 Introduction to Fuzzy
1.2 Introduction to MCDM
1.2.1 MADM
1.2.2 MODM
1.2.3 Needs of Fuzzy MCDM
1.3 Literature Survey
1.4 Fuzzy Control System
1.4.1 Fuzzy Classification
1.4.2 De-Fuzzification
1.5 Mathematical Programming Using Fuzzy Models
1.5.1 Fuzzy Linear Programming
1.5.2 Fuzzy Integer Linear Problems
1.5.3 Fuzzy Dynamic Programming
1.5.4 Mathematical Programming as a Tool for Fuzzy Rule Learning Process
1.6 MCDM with Fuzzy AHP
1.6.1 Analytical Hierarchy Process
1.6.2 Need for Fuzzy AHP
1.6.3 Case Study of Fuzzy AHP
1.6.4 Application of Fuzzy AHP: Air Pollution Control
1.7 Comparison between AHP and Fuzzy AHP
1.8 Conclusion
References
Chapter 2 Security and Privacy Requirements for IoMT-Based Smart Healthcare System: Challenges, Solutions, and Future Scope
2.1 Introduction
2.1.1 Motivation for the Chapter
2.1.2 Organization of the Chapter
2.2 Research Areas
2.2.1 Internet of Medical Things or Internet of Things and Medical Signals
2.2.2 Fusion of Medical Signals and Internet of Medical Things
2.2.3 Cloud- and Edge-Based Smart Healthcare System
2.2.4 Security and Privacy in Internet of Medical Things-Medical or Healthcare-Based System
2.3 Smart Healthcare Systems: Privacy and Security Requirements
2.3.1 Data Level
2.3.1.1 Confidentiality
2.3.1.2 Integrity
2.3.1.3 Availability
2.3.2 Sensor Level
2.3.2.1 Tamper-Proof Hardware
2.3.2.2 Localization
2.3.2.3 Self-Healing
2.3.2.4 Over the Air Programming
2.3.3 Personalized Server Level
2.3.3.1 Device Authentication
2.3.3.2 User Authentication
2.3.4 Medical Server Level
2.3.4.1 Access Control
2.3.4.2 Management of Trust or Faith
2.4 Challenges and Future Research Directions
2.5 Conclusion
References
Chapter 3 The Rise of “Big Data” on Cloud Computing
3.1 Introduction
3.2 Definition and Characteristics of Big Data
3.3 Cloud Computing
3.3.1 IaaS in Public Cloud
3.3.2 PaaS in Private Cloud
3.3.3 SaaS in Hybrid Cloud
3.3.4 Improved Analysis
3.3.5 Simplified Infrastructure
3.3.6 Lowering the Cost
3.3.7 Security and Privacy
3.4 Big Data in Cloud Computing
3.4.1 Big Data Storage in Cloud Computing
3.4.2 Big Data Processing in Cloud Computing
3.4.3 Big Data Analytics in Cloud Computing
3.5 Conclusion
References
Chapter 4 Effect of the Measurement on Big Data Analytics: An Evolutive Perspective with Business Intelligence
4.1 The Role of the Measurement in the Decision-Making
4.2 The Emergence of Business Intelligence: A Business Perspective
4.3 About the Data Quality and Big Data Analytics
4.4 The Impact on Scenarios and Entity States on the Measurement
4.5 Monitoring the Evolution of Entities and Scenarios
4.6 Information-Driven Decision-Making, Big Data Analytics, and the Cloud
4.7 Conclusion
References
Chapter 5 Performance Analysis for Provisioning and Energy Efficiency Distributed in Cloud Computing
5.1 Introduction: Background and Driving Forces
5.2 Efficiency of Energy Conservation in Cloud Computing
5.3 Data Security in Cloud Network
5.4 Comparative Study of Existing Techniques
5.4.1 Problem Identification
5.5 Implementation
5.6 Conclusion
References
Chapter 6 Using Internet of Things (IoT) for Smart Home Automation and Metering System
6.1 Introduction
6.2 System Architecture
6.3 The Proposed Encrypton Technal Property
6.3.1 Generate the Round Key Using Bedlam Frame
6.3.2 Encryption Process
6.4 Conclusion
References
Chapter 7 Big Data Analysis and Machine Learning for Green Computing: Concepts and Applications
7.1 Introduction to Big Data
7.2 The Emerging Technology: Big Data Analytics
7.2.1 Big Data Solutions
7.2.2 The 5Vs of Big Data
7.2.3 Stages of Big Data Lifecycle
7.2.3.1 Data Generation
7.2.3.2 Data Acquisition
7.2.3.3 Data Storage
7.2.3.4 Big Data Analysis
7.2.4 Big Data Processing Methods
7.2.5 Challenges of Big Data
7.2.6 Technologies Involved with Big Data
7.2.7 Future of Big Data
7.3 Machine Learning
7.3.1 Supervised Learning
7.3.2 Unsupervised Learning
7.3.3 Reinforcement Learning
7.4 Big Data, Machine Learning and Our Ecosystem
7.5 State-of-the-Art and Real-Time Applications of Green Computing
7.6 Discussion
7.7 Conclusion
References
Chapter 8 Fundamental Concepts and Applications of Blockchain Technology
8.1 Introduction
8.2 Blockchain Architecture
8.2.1 Blockchain Working
8.2.2 Keywords Used in Blockchain
8.2.2.1 Node
8.2.2.2 Transaction
8.2.2.3 Address
8.2.2.4 Block
8.2.2.5 Chain
8.2.2.6 Nonce
8.2.2.7 Mining
8.2.2.8 Consensus
8.2.2.9 Hard Fork
8.2.2.10 Merkle Tree
8.3 Types of Blockchain
8.3.1 Public Blockchain
8.3.2 Private Blockchain
8.3.3 Consortium Blockchain
8.4 Tiers of Blockchain Technology
8.4.1 Blockchain 1.0: Currency
8.4.2 Blockchain 2.0: Smart Contracts
8.4.3 Blockchain 3.0: DApps
8.4.4 Blockchain 4.0: Industry 4.0
8.5 Background of Blockchain
8.5.1 Bitcoin
8.5.2 Development of Bitcoin
8.5.3 Working Process of Bitcoin
8.5.4 Potential Risks in Bitcoin
8.5.4.1 Volatile
8.5.4.2 Cybertheft
8.5.4.3 Fraud
8.5.4.4 Technology Reliance
8.6 Consensus Mechanisms
8.6.1 Proof of Work
8.6.2 Proof of Stake
8.6.3 Practical Byzantine Fault Tolerance
8.6.4 Proof of Burn (PoB)
8.7 Applications of Blockchain Technology
8.7.1 Finance
8.7.2 Insurance Sector
8.7.2.1 Benefits Offered by Blockchain Technology in Insurance
8.7.3 Music Industry
8.7.3.1 The Glitches with the Present Structure of the Music Industry
8.7.4 Identity Management
8.7.4.1 Difficulties in the Existing Identity Management System
8.7.5 Supply Chain
8.7.5.1 Problems in the Old Supply Chain Management
8.7.5.2 Benefits of Supply Chain on Decentralization Platform
8.8 Challenges in Blockchain Technology
8.8.1 Scalability
8.8.1.1 Improving the Storage of Blockchain
8.8.1.2 Redesigning Blockchain
8.8.2 Privacy Leakage
8.8.3 Selfish Mining
8.9 Future Enhancement
8.10 Conclusions
References
Chapter 9 Mental Disorder Detection Using Machine Learning
9.1 Introduction
9.2 A Review of Mental Disorders and Diseases
9.3 Machine Learning for Disorder Diagnosis and Prognosis
9.3.1 Supervised Learning Approach
9.3.2 Unsupervised Learning Approach
9.3.3 Reinforcement Learning Approach
9.4 Possible Causes of Bipolar Disorder and Detection Codes
9.5 A Meta-analysis of Depression
9.6 Diagnosis and Prognosis of Parkinson’s Disease
9.7 A Meta-Analysis of Anxiety
9.8 Schizophrenia and Post-Traumatic Stress Disorder
9.8.1 Support Vector Machine for Prediction
9.9 Conclusion
References
Chapter 10 Blockchain Technology for Industry 4.0 Applications: Issues, Challenges and Future Research Directions
10.1 Introduction
10.1.1 Interoperability
10.1.2 Device Reliability and Durability
10.1.3 Security and Privacy Issues
10.1.4 Emerging Technologies and Skills of Staff
10.1.5 Standardization
10.2 Utilization of Blockchain for Industry 4.0 Applications
10.2.1 Demand for Blockchain Technology in Industry 4.0 Application
10.2.2 Versatile Nature of Blockchains in Industry
10.2.2.1 Depending on Access Regulation
10.2.2.2 Depending on Permissions
10.2.2.3 Depending on the Operation Mode
10.2.2.4 Depending on the Sort of Inducement
10.2.3 Role of Smart Contracts in Industry 4.0 Factories
10.2.4 Superiority of Adopting Blockchain Technology in Industry 4.0 Technologies
10.3 Industry 4.0 Applications Based on Blockchain Technology
10.3.1 IIoT
10.3.2 Vertical and Horizontal Integration Systems
10.3.3 ICPS
10.3.4 Role of Big Data and Data Analytics
10.3.5 Industrial Augmented and Virtual Reality
10.3.6 Autonomous Robotics and Vehicles
10.3.6.1 Cobots
10.3.6.2 Robots
10.3.6.3 AGV (Autonomous Ground Vehicle)
10.3.7 Cloud and Edge Computing
10.3.8 Additive Manufacturing (3D Printing)
10.3.9 Cybersecurity
10.3.10 Simulation Software
10.4 Open Issues in Blockchain Technology
10.4.1 Marketability
10.4.2 Security and Privacy
10.4.3 Resource Constraints
10.4.4 Regulations
10.5 Major Challenges of Implementing Blockchain Technology into Industry 4.0
10.5.1 Scalability
10.5.2 Cryptosystems for Resource-Constrained Devices
10.5.3 Consensus Algorithm Selection
10.5.4 Security and Privacy
10.5.5 Energy and Cost Efficiency
10.5.5.1 PoS (Proof-of-Stake)
10.5.5.2 DPoS (Delighted Proof-of-Stake)
10.5.5.3 PoT (Proof-of-Trust)
10.5.6 Storage
10.5.7 Dearth of Accomplishment
10.5.8 Legal and Complaisance
10.5.9 Naming and Discovery
10.5.10 Required Infrastructure
10.5.11 Interoperability and Standardization
10.5.12 Regulatory and Legal Aspects
10.5.13 Management of Multichains
10.6 Conclusion
References
Index