Towards the Integration of IoT, Cloud and Big Data: Services, Applications and Standards (Studies in Big Data, 137)

✍ Scribed by Vinay Rishiwal (editor), Pramod Kumar (editor), Anuradha Tomar (editor), Priyan Malarvizhi Kumar (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 164
• Category: Library

✦ Synopsis

This book discusses integration of internet of things (IoT), cloud computing, and big data. It presents a unique platform where IoT, cloud computing, and big data are fused together and can be foreseen as a perfect solution to many applications. Usually, IoT, cloud computing, and big data are researched separately on the basis of their properties, underlying technologies, and other open issues. Integration of IoT, cloud computing and big data is not that easy and can face key open issues like standardization of interfaces, power and energy efficiency in both data processing and transmission, security and privacy, storage mechanisms for future applications, scalability and flexibility, and QoS provisioning for end user applications. Integration of IoT, cloud computing, and big data represents the next big rise for future industry and business applications. This integration opens new exhilarating directions for research and it is discussed in this book.

✦ Table of Contents

Preface
Contents
Editors and Contributors
Introduction to Big Data Analytics
1 Introduction to Big Data
2 The Distinction Between Small and Big Data
3 Classification of Big Data
4 Characteristics of Big Data
5 Who’s Generating Big Data?
6 Why Is Big Data Important?
7 Challenges in Big-Data
8 Big Data Applications
9 How Big Data Analysis Differs from Business Intelligence Analysis?
9.1 Business Intelligence
9.2 Big Data
9.3 Differences Between Business Intelligence (BI) and Big Data
10 The Analytical Lifestyle of Big Data
10.1 Phase 1: Discovery
10.2 Phase 2: Data Preparation
10.3 Phase 3: Model Planning
10.4 Phase 4: Model Building
10.5 Phase 5: Communicate Results
10.6 Phase 6: Operationalize
11 Big Data Analysis Necessitates a Set of Skills
12 Big Data Domain
13 Introduction to Big Data Analytics
14 Overview of the Hadoop Ecosystem
14.1 HDFS
14.2 YARN
14.3 MapReduce
14.4 Spark
15 Overview of Big Data Analysis and Its Need
16 Use Cases of Big Data Analytics
17 Challenges in Analyzing Big Data
18 Big Data Quality Dimensions
19 Conclusion
References
DCD_PREDICT: Using Big Data on Prediction for Chest Diseases by Applying Machine Learning Algorithms
1 Introduction
1.1 Introduction
1.2 Background
1.3 Objective
2 Literature Survey
2.1 Summary
3 System Design
3.1 Existing System
3.2 Identification of Common Risks
3.3 Types of Heart Diseases
3.4 Problem Statement
3.5 Scope
3.6 Proposed System
4 Methodology
4.1 Supervised Learning
4.2 Symptom-Based Questionnaire
4.3 Dataset Training and Testing
5 Process and Analysis
5.1 General Process
5.2 Use Case Diagram
5.3 Data Flow Diagram
5.4 System Flow
6 Implementation and Results
6.1 Details of Algorithms
6.2 Data Set and Its Parameters
6.3 Dataset Attributes
6.4 Execution and Screenshots
7 Conclusion and Future Scope
7.1 Conclusion
7.2 Future Scope
References
Design of Energy Efficient IoMT Electrocardiogram (ECG) Machine on 28 nm FPGA
1 Introduction
2 Background
3 Environmental Settings for Energy Efficient IoMT ECG Machine
4 Power Analysis of IoMT ECG Machine
5 Conclusion
References
Automatic Smart Irrigation Method for Agriculture Data
1 Introduction
2 Motivation
3 Contribution of the Chapter
4 Organization and Roadmap of the Article
5 Related Works
6 About the Dataset and Features
7 Methodology and Applied Algorithms
7.1 Data Processing
7.2 Machine Learning
8 Result and Analysis
9 Challenges in Proposed Work
10 Conclusion and Future Work
References
Artificial Intelligence Based Plant Disease Detection
1 Introduction
2 Motivation
3 Contribution of the Chapter
4 Organization of the Chapter
5 Literature Survey
6 Issues and Challenges
7 Methodology
7.1 Advantages of Using Convolution Neural Network (CNN)
7.2 Flow of the Models
7.3 Image Preprocessing
8 Performance Metrics
9 Result Analysis
10 Conclusion and Future Scope
References
IoT Equipped Intelligent Distributed Framework for Smart Healthcare Systems
1 Introduction
1.1 Internet of Things (IoT)
1.2 Smart Healthcare
1.3 DDBMS
1.4 Artificial Intelligence (AI)
1.5 Blockchain Technology
2 Security Issues in Smart Healthcare Systems
2.1 Communication Media
2.2 Topology Issues
2.3 Scalability
2.4 Mobility and Energy Constraints
2.5 Memory Constraints
2.6 Multi-protocol Network
2.7 Tamper Devices
3 Existing Healthcare Systems
4 Proposed Model
5 Results and Discussion
6 Conclusions
References
Adaptive Particle Swarm Optimization for Energy Minimization in Cloud: A Success History Based Approach
1 Introduction
2 Background and Related Work
3 Proposed Approach
4 Results and Discussions
5 Conclusion and Future Work
Appendix
References
Field Monitoring and Automation in Agriculture Using Internet of Things (IoT)
1 Introduction
2 Related Works
3 IoT Technologies for Field Monitoring in Agriculture
3.1 Drones in Agriculture
3.2 Remote Sensing in Agriculture
3.3 Computer Imaging in Agriculture
4 Proposed Automated System Model for Agricuture
4.1 Proposed System Block Diagram
5 Work Flow of System Model
5.1 Field Quality Analysis
5.2 Irrigation System
5.3 System Design
5.4 Irrigation System
6 Hardware Setup for Proposed System Model
7 Android Mobile Application for Monitoring the Work Flow
8 Getting Alerts for Motor On/Off via Mobile Application
9 Conclusion
References