Internet of Things, Artificial Intelligence and Blockchain Technology

Preface
Acknowledgements
Contents
List of Abbreviations
Chapter 1: IoT Architecture, Communication Technologies, and Its Applications
	1.1 Introduction
		1.1.1 IoT-Layered Architecture
	1.2 IoT Devices and Communication Technologies
		1.2.1 Sensor and Its Types
		1.2.2 Controller
		1.2.3 Actuator Device
		1.2.4 Device Management System
		1.2.5 Communication Between IoT Devices
			1.2.5.1 Wi-Fi Technologies
			1.2.5.2 ZigBee (802.15.4)
			1.2.5.3 Low-Power Wide-Area Networks
			1.2.5.4 Bluetooth
			1.2.5.5 Bluetooth Low Energy (BLE)
			1.2.5.6 Cellular (2G/3G/4G/5G)
			1.2.5.7 Radio Frequency Identification (RFID)
	1.3 RFID Tags with WSN Nodes
	1.4 RFID Reader with WSN Nodes
	1.5 Hybrid Integration
		1.5.1 NFC
	1.6 Applications of IoT
	1.7 Conclusion
	References
Chapter 2: A Survey on Hyperledger Frameworks, Tools, and Applications
	2.1 Introduction
		2.1.1 Framework of Basic Blockchain
	2.2 Literature Review
	2.3 Case Study and Applications
		2.3.1 Creation of Block Using Python
	2.4 Hyperledger Frameworks and Tools
		2.4.1 Hyperledger Burrow
		2.4.2 Hyperledger Fabric
		2.4.3 Hyperledger Grid
		2.4.4 Hyperledger Indy
		2.4.5 Hyperledger Iroha
		2.4.6 Hyperledger Sawtooth
		2.4.7 Hyperledger Caliper
		2.4.8 Hyperledger Cello
		2.4.9 Hyperledger Composer
		2.4.10 Hyperledger Explorer
		2.4.11 Hyperledger Quilt
		2.4.12 Hyperledger Ursa
		2.4.13 Hyperledger Aries
	2.5 Applications of Blockchain Beyond Currency
	2.6 Technical Challenges of Blockchain
	2.7 Conclusion
	References
Chapter 3: Cyber-Resilient Energy Infrastructure and IoT
	3.1 Principles of Cyber Resilience
		3.1.1 What Is Cyber Resilience?
		3.1.2 Cyber Resilience Challenges
		3.1.3 Cyber Resilience Lifecycle
			3.1.3.1 Identification of Potential Risks
			3.1.3.2 Data Protection
			3.1.3.3 Anomaly Detection
			3.1.3.4 Quick Responsive
	3.2 IoT-Enabled Smart Grid
		3.2.1 Layers in IoT-Enabled Smart Grid
	3.3 Integration of Smart Homes with Smart Grid
		3.3.1 Smart Grid Components and Their Vulnerabilities
	3.4 Security Breaches in Smart Grid
	3.5 Cyber-Secure Framework for IoT-Based Smart Home
		3.5.1 Threats to the Smart Home Environment
		3.5.2 Trust-Based Intelligent Security Systems for Smart Homes
		3.5.3 Performance Analysis
	3.6 Future of Resilient Electrical Grids
	3.7 Conclusion
	References
Chapter 4: AI, IoT, and Blockchain: Business Models, Ethical Issues, and Legal Perspectives
	4.1 Introduction
	4.2 Business Models and Perspectives for Converged AI, IoT, and Blockchain Applications
		4.2.1 Business Models for AI Applications
		4.2.2 Business Models for IoT Applications
		4.2.3 Business Models for Blockchain Applications
	4.3 Ethical Issues in AI, IoT, and Blockchain
	4.4 AI, IoT, and Blockchain from a Legal Perspective
	4.5 Conclusion
	References
Chapter 5: Examining the Legal Issues Involved in the Application of Blockchain Technology
	5.1 Introduction
		5.1.1 Objectives
	5.2 Literature Review
		5.2.1 Can Blockchain Systems Really Be Safe and Legally Sound Without Intermediaries?
	5.3 Application of Blockchain Technology to Law
		5.3.1 Smart Contracts
		5.3.2 Public Blockchain – Land Registration
		5.3.3 Securing Financial Transactions
		5.3.4 Digital Assets
		5.3.5 Intellectual Property
		5.3.6 Process Automation, Workflow Integration, and Security
		5.3.7 Enforcement of Contracts or Judgments
		5.3.8 Security
	5.4 Blockchain and its Potential Usage in Nigeria
		5.4.1 Enforceability of Smart Contracts
		5.4.2 Privacy
		5.4.3 Jurisdiction
		5.4.4 Taxation
	5.5 Future Trends and Conclusion
		5.5.1 Future Trends
		5.5.2 Conclusion
	References
Chapter 6: IoT-Based Biomedical Sensors for Pervasive and Personalized Healthcare
	6.1 Introduction
	6.2 Mobile and Pervasive Healthcare
	6.3 Connected Healthcare
	6.4 Pervasive Healthcare and Telemedicine
		6.4.1 Telemedicine
		6.4.2 Pervasive Healthcare
	6.5 Challenges of Traditional Healthcare
	6.6 IoT Healthcare Services
	6.7 IoT-Based Smart Healthcare System
	6.8 Different Healthcare Sensors
		6.8.1 Other Sensors Used in Medical Care Units
		6.8.2 Different Fitness Devices
	6.9 Conclusion
	References
Chapter 7: The Herculean Coalescence AIoT – A Congruence or Convergence?
	7.1 Introduction
		7.1.1 The Framework of AI
		7.1.2 The Framework of IoT
		7.1.3 Chapter Organization
	7.2 Evolution of AIoT
		7.2.1 Generations of AIoT
			7.2.1.1 First Generation
			7.2.1.2 Second Generation
			7.2.1.3 Third Generation
			7.2.1.4 Fourth Generation
		7.2.2 Few Applications of AIoT in Strong Force Existence
	7.3 Case Discussions of Various Applications
		7.3.1 Smart City
		7.3.2 Retail Industry
			7.3.2.1 Retail Industry in India
			7.3.2.2 Online Retailing and Retail During Pandemic
			7.3.2.3 Future of Retailing
			7.3.2.4 Problems Faced by Retail Industry
		7.3.3 Pharmaceutical Industry
			7.3.3.1 Manufacturing and Operations
			7.3.3.2 Inventory Management
			7.3.3.3 Order Management
			7.3.3.4 Major Benefits of AIoT
			7.3.3.5 Challenges in the Pharmaceutical Industry
		7.3.4 Oil and Gas Industry
			7.3.4.1 Preventive Maintenance
			7.3.4.2 AI-IoT Solutions
				SparkCognition Uses Four Different Modules Given Below
				Softweb Solutions Use IoT Connect System
				Telit Uses IoT Solutions
				Foghorn Uses Lightning Edge Intelligence
			7.3.4.3 Challenges in Oil and Gas Industry
		7.3.5 Privacy and Governance
			7.3.5.1 Standardization
			7.3.5.2 Challenges in Privacy and Governance
		7.3.6 IELTS and PTE Examinations
			7.3.6.1 Challenges in IELTS and PTE
	7.4 Summative Challenges Faced in Convergence of AIoT
	7.5 Future Trends and Conclusion
	References
Chapter 8: Impact of Internet of Things, Artificial Intelligence, and Blockchain Technology in Industry 4.0
	8.1 Introduction
	8.2 Recent Trends in IoT
		8.2.1 Trends and Characteristics
		8.2.2 Enabling Technologies for IoT
	8.3 Importance of Artificial Intelligence
		8.3.1 Role of AI in Industry 4.0
		8.3.2 Applications of Artificial Intelligence
		8.3.3 Important Observations
		8.3.4 Categories of Artificial Intelligence
		8.3.5 AI: Thinking about Data
		8.3.6 Machine Learning in Action
	8.4 Machine Learning
		8.4.1 Some Machine Learning Methods
		8.4.2 Importance of Machine Learning Concepts in Industry 4.0
	8.5 Deep Learning
		8.5.1 Important Observations
		8.5.2 Deep Learning Vs. Machine Learning
		8.5.3 Impact of Deep Learning in Industry 4.0
	8.6 Role of Blockchain in Present Era
	8.7 Impact of IoT, AI, and Blockchain Technology in Industries
	8.8 Challenges in the Implementation of Industry 4.0
		8.8.1 Challenges Faced by Developed Nations
		8.8.2 Challenges Faced by Developing Nations
	8.9 Conclusion
	References
Chapter 9: Electronic Health Record Maintenance (EHRM) Using Blockchain Technology
	9.1 Introduction
		9.1.1 Why Blockchain?
		9.1.2 Types of Blockchain
			9.1.2.1 Public Blockchain
			9.1.2.2 Private Blockchain
			9.1.2.3 Consortium Blockchain
			9.1.2.4 Hybrid Blockchain
	9.2 Literature Survey
	9.3 Materials and Methods
		9.3.1 Blockchain Complexity
		9.3.2 High-Energy Consumption
		9.3.3 Scalability Challenges
		9.3.4 Brain Drain for Blockchain
		9.3.5 Blockchain Components in Healthcare
			9.3.5.1 Blockchain in Health
			9.3.5.2 Securing Patient Data
			9.3.5.3 Healthcare Data Management
			9.3.5.4 Challenges in Healthcare Data Management
	9.4 Having a Proper Strategy
	9.5 A Common Database to be Maintained like a Repository
	9.6 The Database Must Have Genuine Data
	9.7 Case Study and Applications
		9.7.1 Methodology
		9.7.2 Blockchain-Based Projects for Healthcare Data Management
			9.7.2.1 Pharmaceutical Sector
			9.7.2.2 Drug Discovery and Pharmaceutical Research
			9.7.2.3 Supply Chain and Counterfeit Drug Detection
			9.7.2.4 Prescription Management
			9.7.2.5 Precision Tracking
			9.7.2.6 Advantages
			9.7.2.7 Accessing and Sharing Health Data
			9.7.2.8 Data to Empower Patients
			9.7.2.9 Malpractice Concerns
			9.7.2.10 Institutional and Interpersonal Competition
		9.7.3 Ethics and Dissemination
		9.7.4 Analytics
			9.7.4.1 Predictive Analytics
			9.7.4.2 Telemedicine
			9.7.4.3 Analytics with Centralized Server
		9.7.5 Blockchain to the Rescue
		9.7.6 Blockchain as a Service (BaaS)
			9.7.6.1 How Does BaaS Work?
	9.8 Conclusion
	References
Chapter 10: Blockchain Security for Artificial Intelligence-Based Clinical Decision Support Tool
	10.1 Introduction
	10.2 Blockchain in Healthcare
	10.3 Critical Key Components of Blockchain Technology
		10.3.1 Immutability
		10.3.2 Decentralized
		10.3.3 Security
		10.3.4 Faster and Cheaper Transaction
		10.3.5 Consensus
	10.4 Role of Artificial Intelligence in Healthcare
		10.4.1 Developing Radiological Tools
		10.4.2 Reduce EHR Clinical Burdens
		10.4.3 Artificial Intelligence in Breast Cancer
		10.4.4 Role of Artificial Intelligence in Dementia
		10.4.5 Role of Artificial Intelligence in Diabetic
		10.4.6 Role of Artificial Intelligence in Cardiovascular Disease
	10.5 Clinical Decision-Making System
		10.5.1 Clinical Decision Support System
			10.5.1.1 Introduction
			10.5.1.2 Why CDS Is Important?
		10.5.2 Different Types of the CDS System
			10.5.2.1 An AI-Enabled Knowledge-Based System (KBS)
			10.5.2.2 Non-knowledge-Based CDS System
		10.5.3 Benefits and Risk
			10.5.3.1 Benefits
				Patient Safety
				Hospital Organization Management
				Patient Decision Support
			10.5.3.2 Risk
	10.6 CDS Tools
		10.6.1 RAMPmedical
		10.6.2 Medical Algorithm Company: Documentation Forms and Templates
		10.6.3 Cohesic
		10.6.4 Hera-MI
			10.6.4.1 Hera-MI Product: Breast-SlimView
		10.6.5 Tapa Healthcare
	10.7 Artificial Intelligence in the Clinical Decision Support Tool
		10.7.1 AI-Based Breast Imaging Tool
			10.7.1.1 Quantitative Insights
			10.7.1.2 ScreenPoint Medical
			10.7.1.3 CureMetrix
		10.7.2 AI-Based Medical Imaging General
		10.7.3 Lunit
		10.7.4 ChironX
		10.7.5 4Quant
	10.8 Blockchain-Based Techniques in the Clinical Decision Support System
		10.8.1 FHIRChain
		10.8.2 BlocHIE
	10.9 Conclusion
	References
Chapter 11: Decision Support Mechanism to Improve a Secured System for Clinical Process Using Blockchain Technique
	11.1 Introduction
		11.1.1 Overview of Blockchain Techniques
		11.1.2 Challenges and Techniques Involved in Blockchain Process
	11.2 Literature Survey on Healthcare Processing Using Blockchain
	11.3 Related Works
		11.3.1 Working Principle of Blockchain with an Intelligent System
		11.3.2 Case Study on the Decision-Making System Using Expert Mechanism Through Intelligence Algorithm
		11.3.3 Case Study on How the Patient’s Intelligence Will Get Transacted to Clinical Processing Mechanism Using Smart Contract
	11.4 Conclusion
	11.5 Future Trends on Blockchain
	References
Chapter 12: Bi-GRU Model with Stacked Embedding for Sentiment Analysis: A Case Study
	12.1 Introduction
	12.2 Literature Review
		12.2.1 Sentiment Lexicon-Based Sentiment Analysis
		12.2.2 Machine Learning-Based Sentiment Analysis
		12.2.3 Deep Learning-Based Sentiment Analysis
	12.3 Methodology
		12.3.1 Polarity-Based Mechanism
		12.3.2 GRU Combined with CNN
		12.3.3 Bi-GRU Layer
		12.3.4 Long Short-Term Memory
	12.4 Case Study
		12.4.1 Dataset
		12.4.2 Data Preprocessing
		12.4.3 Stacked Embedding
	12.5 Results
	12.6 Conclusion
	References
Chapter 13: A Systematic Framework for Heart Disease Prediction Using Big Data Analytics
	13.1 Introduction
		13.1.1 Big Data
		13.1.2 Heart Diseases
		13.1.3 Predictive Analytics of Heart Diseases
	13.2 Data-Driven Approach of Medicinal Data Using Big Data
		13.2.1 The New Science
		13.2.2 Big Data Challenges in Medicinal Data
		13.2.3 Effective Decision-Making in Data-Driven Approach
		13.2.4 Data Reduction, Visualization, and Analytics
	13.3 Challenges of Data-Driven Healthcare
		13.3.1 Data Complexity
		13.3.2 Data Variety
		13.3.3 Data Quality
		13.3.4 Statistical Rigor
		13.3.5 Selection Bias
	13.4 Big Data Analytics Tools
		13.4.1 Zoho Analytics
		13.4.2 Cloudera
		13.4.3 Power BI
		13.4.4 Tableau
	13.5 Case Study: Heart Disease Prediction Using Big Data Analytics
		13.5.1 Big Data and the Promise of Population Health
		13.5.2 Heart Disease Prediction from Patient Data Using Visualization Method
		13.5.3 Dataset Description
		13.5.4 Performances Metrics Analysis
		13.5.5 Training Machine Learning for Clinical Applications
	13.6 Conclusion and Future Scope
	References
Chapter 14: Artificial Intelligence and the Future of Law Practice in Nigeria
	14.1 Introduction
		14.1.1 General
		14.1.2 Objectives
	14.2 Literature Review and Data Analysis
		14.2.1 The Development of AI in Legal Practice
		14.2.2 AI and Its Impact on Data Analysis
		14.2.3 AI’s Growing Influence in Legal Practice
		14.2.4 Ruling and Judicial Decision Making with AI
	14.3 Application of AI in Practice
		14.3.1 Artificial Intelligence
		14.3.2 Legal Automation
		14.3.3 Administration of Justice
		14.3.4 Provision of Legal Services
	14.4 AI and Its Adoption in Legal Practice in Nigeria
		14.4.1 The Current Scope of AI and Law in Nigeria
		14.4.2 Potential AI Scenarios in Nigeria
			14.4.2.1 Administration of Criminal Justice
			14.4.2.2 Administration of Civil Matters
			14.4.2.3 AI-Powered Lawyers and Judges: Can They Be Trusted?
		14.4.3 Related Instances of AI Usage in Nigeria
	14.5 Comparative Table of Scholarly Views on AI and Its Impact on Law
	14.6 Future Perspectives and Conclusion
		14.6.1 Future Perspectives
		14.6.2 Conclusion
	References
		Books, Articles and Journals
		Websites
Index