Big Data, Cloud Computing and IoT : Tools and Applications

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 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 "Cloud computing, the Internet of Things (IoT), & big data are three significant technological trends affecting the world's largest corporations. This book discusses Big Data, Cloud Computing, and 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 IoT are proposed as possible study avenues. Features: Informs about cloud computing, IoT & 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, IT professionals, students, scholars, & engineers exploring research on the incorporation of technological innovations to address contemporary societal challenges"-- Provided by publisher