Big Data, Cloud Computing and IoT

✍ Scribed by Sita Rani (editor), Pankaj Bhambri (editor), Aman Kataria (editor), Alex Khang (editor)

• Publisher: Chapman and Hall/CRC
• Tongue: English
• Leaves: 259
• Category: Library

✦ Synopsis

Cloud computing, the Internet of Things (IoT), and big data are three significant technological trends affecting the world's largest corporations. This book discusses big data, cloud computing, and the IoT, with a focus on the benefits and implementation problems. In addition, it examines the many structures and applications pertinent to these disciplines. Also, big data, cloud computing, and the IoT are proposed as possible study avenues.

Features:

• Informs about cloud computing, IoT and big data, including theoretical foundations and the most recent empirical findings
• Provides essential research on the relationship between various technologies and the aggregate influence they have on solving real-world problems
• Ideal for academicians, developers, researchers, computer scientists, practitioners, information technology professionals, students, scholars, and engineers exploring research on the incorporation of technological innovations to address contemporary societal challenges

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Chapter 1: Integration of IoT, Big Data, and Cloud Computing Technologies: Trend of the Era
1.1 Introduction
1.1.1 IoT
1.1.1.1 Architecture
1.1.1.2 IoT Application Domains
1.1.2 Big Data
1.1.2.1 Big Data Applications
1.1.3 Cloud Computing
1.1.3.1 Cloud Service Models
1.1.3.2 Cloud Deployment Models
1.1.3.3 Cloud Applications Areas
1.2 Integrated Framework
1.3 Application Domains Composed of Amalgamation of Technologies
1.4 Advantages
1.4.1 Device Data Scalability
1.4.2 Scalable Infrastructural Capacity
1.4.3 Enhanced Effectiveness in Daily Activities
1.4.4 Global App Usage and Distribution Accelerated
1.4.5 Analysis and Appraisal of the Current Situation of IoT-Connected Devices
1.4.6 The Advantages of Economies of Scale
1.5 Limitations
1.6 Research Directions
1.7 Conclusion
References
Chapter 2: Cloud Environment Limitations and Challenges
2.1 Introduction
2.1.1 Need of Cloud Computing
2.1.2 Deployment Models of Cloud Computing
2.1.3 Basic Service Models of Cloud Computing
2.2 Architecture of Cloud Computing
2.2.1 Generic Architecture of Cloud Computing
2.2.2 Market-Oriented Architecture of Cloud Computing
2.3 Characteristics of Cloud Computing
2.4 Limitations and Challenges of Cloud Computing
2.5 Classification of Security Attacks in Cloud Computing
2.5.1 Security Attacks Against Cloud Consumer Side
2.5.2 ISP (Cloud Carrier) Side Attacks
2.5.3 Virtualization Attacks
2.5.4 Cloud Application, Storage, Network, DOS, and DDOS Attacks
2.6 Conclusion
References
Chapter 3: A Guide to Cloud Platform with an Investigation of Different Cloud Service Providers
3.1 Introduction
3.2 Literature Review
3.3 Cloud Computing
3.3.1 Characteristics
3.3.2 Cloud Computing Classifications
3.4 CSPs
3.4.1 Amazon Web Services
3.4.1.1 Strengths
3.4.1.2 Limitations
3.4.2 Microsoft Azure
3.4.2.1 Strengths
3.4.2.2 Limitations
3.4.3 Google Cloud
3.4.3.1 Strengths
3.4.3.2 Limitations
3.4.4 Alibaba Cloud
3.4.4.1 Strengths
3.4.4.2 Limitations
3.4.5 Oracle
3.4.5.1 Strengths
3.4.5.2 Limitations
3.4.6 IBM
3.4.6.1 Strengths
3.4.6.2 Limitations
3.5 Conclusion
References
Chapter 4: A Study on the Accuracy of IoT-Sensed Data Using Machine Learning
4.1 Introduction
4.2 Proposed Model
4.2.1 Key Factors
4.2.2 Machine Learning Algorithms
4.2.3 Linear Regression Model
4.3 Development of Machine Learning Model
4.3.1 Data Set and Data Preprocessing
4.3.2 EDA
4.3.3 Training and Testing of Data
4.4 Model Evaluation
4.4.1 Predictions from the Model
4.4.2 Evaluation Metrics
4.5 Accuracy Analysis
4.6 Conclusion
References
Chapter 5: Cloud-Based Remote Sensing:: Developments and Challenges—Research Point of View
5.1 Introduction
5.2 Motivation
5.2.1 Definition
5.3 History of Remote Sensing
5.4 Remote Sensing–Working Principle
5.4.1 Concepts of Remote Sensing
5.4.2 Sensors
5.5 Classification of Remote Sensing
5.5.1 Types of Remote Sensing
5.5.2 Passive Microwave Remote Sensing
5.5.3 Satellite Remote Sensing
5.6 Cloud-Based Remote Sensing
5.6.1 Cloud Computing
5.6.2 Cloud Service Models
5.6.3 Cloud Deployment Models
5.6.4 Cloud Service Providers
5.7 Challenges Faced by Remote Sensing
5.8 Conclusion and Future Scope
References
Chapter 6: Recent Trends in Machine Learning Techniques, Challenges and Opportunities
6.1 Introduction
6.2 ML Techniques
6.3 Conclusion
References
Chapter 7: Heart Disease Prediction Using Machine Learning and Big Data
7.1 Introduction
7.2 Machine Learning
7.2.1 Supervised Machine Learning
7.2.2 Unsupervised Machine Learning
7.2.3 Big Data
7.3 Materials and Methods
7.3.1 Data Source
7.3.2 Methods
7.3.2.1 SVM
7.3.2.2 KNN
7.3.2.3 Decision Tree
7.3.2.4 Naïve Bayes
7.3.2.5 Random Forest
7.4 Results
7.5 Conclusion and Future Plans
References
Chapter 8: Analysis of Credit Card Fraud Data Using Various Machine Learning Methods
8.1 Introduction
8.2 Algorithms
8.3 Related Works
8.4 Results
8.5 Positive Predicted Values and False Discovery Rates Results
8.6 Predicted Results
References
Chapter 9: Cloud Security Risk Management Quantifications
9.1 Introduction
9.1.1 Risk Management Characteristics
9.1.2 Background
9.1.2.1 Stationing Models
9.1.2.2 Service Model
9.1.2.2.1 Software-Based
9.1.2.2.2 Platform-Based
9.1.2.2.3 Infrastructure-Based
9.1.2.3 Mission and Applications
9.2 Fundamentals of Cloud Risk Management
9.2.1 Cloud Risk Multilayered Management
9.2.2 Cloud Surveillance Architecture
9.2.3 Shield Standard
9.3 Matter of Contention
9.3.1 Cloud Functioning
9.3.1.1 Inactivity
9.3.1.2 Disconnection Status Synchronization
9.3.1.3 Expendable Logic
9.3.1.4 Administrating Data Cache
9.3.2 Cloud Accuracy
9.3.3 Fiscal Achievements
9.3.3.1 Uncertainty of Business
9.3.3.2 Servicing Agreements
9.3.3.3 Flexibility of Workloads
9.3.3.4 Interoperability Between Cloud Operators
9.3.3.5 Adversity Restoration
9.3.4 Compliance Obligations
9.3.4.1 Deficit Perceptibility
9.3.4.2 Substantial Data Site
9.3.4.3 Sovereignty and Governance
9.3.4.4 Forensic Support
9.3.5 Security Advice
9.3.5.1 Data Acknowledgment Liability
9.3.5.2 Data Isolation
9.3.5.3 Service Principle
9.3.5.4 Diverse Holding
9.3.5.5 Gateways
9.3.5.6 Hardware Service Certainty
9.3.5.7 Executive Service
9.4 Administering Prospect in the Cloud
9.4.1 Risk Managing Policy
9.4.2 Cloud Operators’ Risk Utilization Process
9.5 Reference Guidelines
9.5.1 Administration
9.5.2 Jurisdiction
9.5.3 Safety and Authenticity
9.5.4 Virtual Engines
9.5.5 Software Utilities
9.6 Conclusion and Future Work
References
Chapter 10: Relevance of Multifactor Authentication for Secure Cloud Access
10.1 Introduction
10.2 Authentication
10.2.1 Authentication Factors
10.3 Authentication Based on Number of Factors
10.4 Need for MFA
10.5 Benefits of MFA
10.6 Application Areas of MFA
10.7 Implementation of MFA
10.7.1 Usage of a Combination of Transparent and Interactive Factors
10.7.2 Usage of Client-Side Authentication
10.7.3 Usage of Out-of-Band Authentication
10.7.4 Usage of a Decentralised Architecture
10.8 Challenges of MFA
10.9 Future of MFA
10.10 Conclusion
References
Chapter 11: LBMMS:: Load Balancing with Max-Min of Summation in Cloud Computing
11.1 Introduction
11.2 Background
11.2.1 Deployment Models
11.3 Load Balancing
11.4 Problem Definition
11.4.1 Virtualization
11.4.2 Implementation
11.4.3 Scheduling
11.5 The Motivation for Load Balancing
11.5.1 Meta-Task About Machines
11.5.2 System Manager
11.5.3 Problem Definitions
11.5.4 Machine Specification
11.5.5 Job Specification
11.5.6 The Execution Time of the Jobs of Each Machine
11.5.7 Required Algorithms
11.6 Flow Chart of the Load-Balancing Algorithm
11.7 Proposed Method
11.7.1 Load Balancing with Max-Min of Summation
11.7.2 Method
11.7.3 Flow Chart
11.8 Illustration of an Example
11.9 Comparison Among Other Load-Balancing Algorithms
11.10 Conclusion
References
Chapter 12: Convergence Time Aware Network Comprehensive Switch Migration Algorithm Using Machine Learning for SDN Cloud Datacenter
12.1 Introduction
12.2 Literature Survey
12.3 System Architecture
12.4 Implementation
12.5 Results and Discussion
12.6 Conclusion
References
Chapter 13: IoT Network Used in Fog and Cloud Computing
13.1 Introduction
13.2 IoT
13.2.1 Communication Protocols of IoT
13.2.1.1 IEEE 802.15.4
13.2.1.2 Zigbee
13.2.1.3 6LoWPAN
13.2.1.4 WirelessHART
13.2.1.5 Z-Wave
13.2.1.6 Other Protocols
13.2.2 Networking in IoT
13.3 Cloud Computing
13.3.1 Services
13.3.2 Deployment Model
13.3.3 End Users
13.3.4 Architecture
13.3.5 Cloud Computing in IIoT
13.3.6 Cloud Computing for Device Management
13.4 Fog Computing
13.4.1 Architecture of Fog Computing
13.4.2 Fog-Enabled IoT
13.5 Case Studies
13.5.1 Factories and Assembly Lines
13.5.2 Other Areas
13.6 Conclusion
References
Chapter 14: Smart Waste Management System Using a Convolutional Neural Network Model
14.1 Introduction
14.2 Literature Review
14.3 Hardware and Software Requirements
14.3.1 Anaconda Navigator
14.3.2 Tensor Flow
14.3.3 Keras
14.3.4 Flask
14.3.5 Python Packages
14.4 Experimental Investigations
14.5 Proposed Methodology
14.5.1 Classifier Module
14.5.2 Algorithm Module
14.5.3 Convolution Module
14.5.4 Website Module
14.6 Results and Discussion
14.7 Conclusion and Future Work
References
Chapter 15: An IoT-Based Emotion Analysis and Music Therapy
15.1 Introduction
15.1.1 Emotions
15.1.2 Music Therapy
15.1.2.1 Music Therapy on Brain Disorders
15.1.2.2 Music Therapy on Traumatic Patients
15.1.2.3 Music Therapy on Cancer
15.1.2.4 Music Therapy on Developmental Deficits
15.2 Machine Learning Architecture for Healthcare Applications
15.2.1 Emotion Analysis Using Machine Learning
15.2.2 Music Therapy Using Machine Learning
15.3 IoT Solutions in Healthcare
15.3.1 Machine Learning–Enabled IoT
15.3.2 IoT for Emotion Analysis and Music Therapy
15.4 Proposed Architecture
References
Chapter 16: Complete Low-Cost IoT Framework for the Indian Agriculture Sector
16.1 Introduction
16.2 Proposed Model
16.2.1 Elements of the Proposed Model
16.3 LPWAN Techniques
16.3.1 LoRa/LoRaWAN Technology
16.3.1.1 LoRaWAN Specification
16.3.1.2 LoRa Protocol Stack
16.4 Computer Vision Technology
16.4.1 Image Acquisition
16.4.2 Image Processing
16.4.2.1 Edge Detection
16.4.2.2 Segmentation
16.4.2.3 Image Classification
16.4.3 Analyzing and Understanding
16.5 Conclusion and Future Scope
References
Electronic Journal/Conference
Online Documents/Web
Index