The Industrial Internet of Things (IIoT): Intelligent Analytics for Predictive Maintenance

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field
	1.1 Introduction
	1.2 Relationship Between Artificial Intelligence and IoT
		1.2.1 AI Concept
		1.2.2 IoT Concept
	1.3 IoT Ecosystem
		1.3.1 Industry 4.0 Concept
		1.3.2 Industrial Internet of Things
	1.4 Discussion
	1.5 Trends
	1.6 Conclusions
	References
2 Analysis on Security in IoT Devices— An Overview
	2.1 Introduction
	2.2 Security Properties
	2.3 Security Challenges of IoT
		2.3.1 Classification of Security Levels
			2.3.1.1 At Information Level
			2.3.1.2 At Access Level
			2.3.1.3 At Functional Level
		2.3.2 Classification of IoT Layered Architecture
			2.3.2.1 Edge Layer
			2.3.2.2 Access Layer
			2.3.2.3 Application Layer
	2.4 IoT Security Threats
		2.4.1 Physical Device Threats
			2.4.1.1 Device-Threats
			2.4.1.2 Resource Led Constraints
		2.4.2 Network-Oriented Communication Assaults
			2.4.2.1 Structure
			2.4.2.2 Protocol
		2.4.3 Data-Based Threats
			2.4.3.1 Confidentiality
			2.4.3.2 Availability
			2.4.3.3 Integrity
	2.5 Assaults in IoT Devices
		2.5.1 Devices of IoT
		2.5.2 Gateways and Networking Devices
		2.5.3 Cloud Servers and Control Devices
	2.6 Security Analysis of IoT Platforms
		2.6.1 ARTIK
		2.6.2 GiGA IoT Makers
		2.6.3 AWS IoT
		2.6.4 Azure IoT
		2.6.5 Google Cloud IoT (GC IoT)
	2.7 Future Research Approaches
		2.7.1 Blockchain Technology
		2.7.2 5G Technology
		2.7.3 Fog Computing (FC) and Edge Computing (EC)
	References
3 Smart Automation, Smart Energy, and Grid Management Challenges
	3.1 Introduction
	3.2 Internet of Things and Smart Grids
		3.2.1 Smart Grid in IoT
		3.2.2 IoT Application
		3.2.3 Trials and Imminent Investigation Guidelines
	3.3 Conceptual Model of Smart Grid
	3.4 Building Computerization
		3.4.1 Smart Lighting
		3.4.2 Smart Parking
		3.4.3 Smart Buildings
		3.4.4 Smart Grid
		3.4.5 Integration IoT in SG
	3.5 Challenges and Solutions
	3.6 Conclusions
	References
4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management
	4.1 Introduction
		4.1.1 Fundamental Terms in IIoT
			4.1.1.1 Cloud Computing
			4.1.1.2 Big Data Analytics
			4.1.1.3 Fog/Edge Computing
			4.1.1.4 Internet of Things
			4.1.1.5 Cyber-Physical-System
			4.1.1.6 Artificial Intelligence
			4.1.1.7 Machine Learning
			4.1.1.8 Machine-to-Machine Communication
		4.1.2 Intelligent Analytics
		4.1.3 Predictive Maintenance
		4.1.4 Disaster Predication and Safety Management
			4.1.4.1 Natural Disasters
			4.1.4.2 Disaster Lifecycle
			4.1.4.3 Disaster Predication
			4.1.4.4 Safety Management
		4.1.5 Optimization
	4.2 Existing Technology and Its Review
		4.2.1 Survey on Predictive Analysis in Natural Disasters
		4.2.2 Survey on Safety Management and Recovery
		4.2.3 Survey on Optimizing Solutions in Natural Disasters
	4.3 Research Limitation
		4.3.1 Forward-Looking Strategic Vision (FVS)
		4.3.2 Availability of Data
		4.3.3 Load Balancing
		4.3.4 Energy Saving and Optimization
		4.3.5 Cost Benefit Analysis
		4.3.6 Misguidance of Analysis
	4.4 Finding
		4.4.1 Data Driven Reasoning
		4.4.2 Cognitive Ability
		4.4.3 Edge Intelligence
		4.4.4 Effect of ML Algorithms and Optimization
		4.4.5 Security
	4.5 Conclusion and Future Research
		4.5.1 Conclusion
		4.5.2 Future Research
	References
5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques
	5.1 Introduction
	5.2 Fuzzy Logic
		5.2.1 Fuzzy Sets
		5.2.2 Fuzzy Logic Basics
		5.2.3 Fuzzy Logic and Power System
		5.2.4 Fuzzy Logic—Automatic Generation Control
		5.2.5 Fuzzy Microgrid Wind
	5.3 Genetic Algorithm
		5.3.1 Important Aspects of Genetic Algorithm
		5.3.2 Standard Genetic Algorithm
		5.3.3 Genetic Algorithm and Its Application
		5.3.4 Power System and Genetic Algorithm
		5.3.5 Economic Dispatch Using Genetic Algorithm
	5.4 Artificial Neural Network
		5.4.1 The Biological Neuron
		5.4.2 A Formal Definition of Neural Network
		5.4.3 Neural Network Models
		5.4.4 Rosenblatt’s Perceptron
		5.4.5 Feedforward and Recurrent Networks
		5.4.6 Back Propagation Algorithm
		5.4.7 Forward Propagation
		5.4.8 Algorithm
		5.4.9 Recurrent Network
		5.4.10 Examples of Neural Networks
			5.4.10.1 AND Operation
			5.4.10.2 OR Operation
			5.4.10.3 XOR Operation
		5.4.11 Key Components of an Artificial Neuron Network
		5.4.12 Neural Network Training
		5.4.13 Training Types
			5.4.13.1 Supervised Training
			5.4.13.2 Unsupervised Training
		5.4.14 Learning Rates
		5.4.15 Learning Laws
		5.4.16 Restructured Power System
		5.4.17 Advantages of Precise Forecasting of the Price
	5.5 Conclusion
	References
6 Recent Advances in Wearable Antennas: A Survey
	6.1 Introduction
	6.2 Types of Antennas
		6.2.1 Description of Wearable Antennas
			6.2.1.1 Microstrip Patch Antenna
			6.2.1.2 Substrate Integrated Waveguide Antenna
			6.2.1.3 Planar Inverted-F Antenna
			6.2.1.4 Monopole Antenna
			6.2.1.5 Metasurface Loaded Antenna
	6.3 Design of Wearable Antennas
		6.3.1 Effect of Substrate and Ground Geometries on Antenna Design
			6.3.1.1 Conducting Coating on Substrate
			6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure
			6.3.1.3 Partial Ground Plane
		6.3.2 Logo Antennas
		6.3.3 Embroidered Antenna
		6.3.4 Wearable Antenna Based on Electromagnetic Band Gap
		6.3.5 Wearable Reconfigurable Antenna
	6.4 Textile Antennas
	6.5 Comparison of Wearable Antenna Designs
	6.6 Fractal Antennas
		6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas
		6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane
		6.6.3 Double-Fractal Layer Wearable Antenna
		6.6.4 Development of Embroidered Sierpinski Carpet Antenna
	6.7 Future Challenges of Wearable Antenna Designs
	6.8 Conclusion
	References
7 An Overview of IoT and Its Application With Machine Learning in Data Center
	7.1 Introduction
		7.1.1 6LoWPAN
		7.1.2 Data Protocols
			7.1.2.1 CoAP
			7.1.2.2 MQTT
			7.1.2.3 Rest APIs
		7.1.3 IoT Components
			7.1.3.1 Hardware
			7.1.3.2 Middleware
			7.1.3.3 Visualization
	7.2 Data Center and Internet of Things
		7.2.1 Modern Data Centers
		7.2.2 Data Storage
		7.2.3 Computing Process
			7.2.3.1 Fog Computing
			7.2.3.2 Edge Computing
			7.2.3.3 Cloud Computing
			7.2.3.4 Distributed Computing
			7.2.3.5 Comparison of Cloud Computing and Fog Computing
	7.3 Machine Learning Models and IoT
		7.3.1 Classifications of Machine Learning Supported in IoT
			7.3.1.1 Supervised Learning
			7.3.1.2 Unsupervised Learning
			7.3.1.3 Reinforcement Learning
			7.3.1.4 Ensemble Learning
			7.3.1.5 Neural Network
	7.4 Challenges in Data Center and IoT
		7.4.1 Major Challenges
	7.5 Conclusion
	References
8 Impact of IoT to Meet Challenges in Drone Delivery System
	8.1 Introduction
		8.1.1 IoT Components
		8.1.2 Main Division to Apply IoT in Aviation
		8.1.3 Required Field of IoT in Aviation
	8.2 Literature Survey
	8.3 Smart Airport Architecture
	8.4 Barriers to IoT Implementation
		8.4.1 How is the Internet of Things Converting the Aviation Enterprise?
	8.5 Current Technologies in Aviation Industry
		8.5.1 Methodology or Research Design
	8.6 IoT Adoption Challenges
		8.6.1 Deployment of IoT Applications on Broad Scale Includes the Underlying Challenges
	8.7 Transforming Airline Industry With Internet of Things
		8.7.1 How the IoT Is Improving the Aviation Industry
		8.7.2 Applications of AI in the Aviation Industry
	8.8 Revolution of Change (Paradigm Shift)
	8.9 The Following Diagram Shows the Design of the Application
	8.10 Discussion, Limitations, Future Research, and Conclusion
		8.10.1 Growth of Aviation IoT Industry
		8.10.2 IoT Applications—Benefits
		8.10.3 Operational Efficiency
		8.10.4 Strategic Differentiation
		8.10.5 New Revenue
	8.11 Present and Future Scopes
		8.11.1 Improving Passenger Experience
		8.11.2 Safety
		8.11.3 Management of Goods and Luggage
		8.11.4 Saving
	8.12 Conclusion
	References
9 IoT-Based Water Management System for a Healthy Life
	9.1 Introduction
		9.1.1 Human Activities as a Source of Pollutants
	9.2 Water Management Using IoT
		9.2.1 Water Quality Management Based on IoT Framework
	9.3 IoT Characteristics and Measurement Parameters
	9.4 Platforms and Configurations
	9.5 Water Quality Measuring Sensors and Data Analysis
	9.6 Wastewater and Storm Water Monitoring Using IoT
		9.6.1 System Initialization
		9.6.2 Capture and Storage of Information
		9.6.3 Information Modeling
		9.6.4 Visualization and Management of the Information
	9.7 Sensing and Sampling of Water Treatment Using IoT
	References
10 Fuel Cost Optimization Using IoT in Air Travel
	10.1 Introduction
		10.1.1 Introduction to IoT
		10.1.2 Processing IoT Data
		10.1.3 Advantages of IoT
		10.1.4 Disadvantages of IoT
		10.1.5 IoT Standards
		10.1.6 Lite Operating System (Lite OS)
		10.1.7 Low Range Wide Area Network (LoRaWAN)
	10.2 Emerging Frameworks in IoT
		10.2.1 Amazon Web Service (AWS)
		10.2.2 Azure
		10.2.3 Brillo/Weave Statement
		10.2.4 Calvin
	10.3 Applications of IoT
		10.3.1 Healthcare in IoT
		10.3.2 Smart Construction and Smart Vehicles
		10.3.3 IoT in Agriculture
		10.3.4 IoT in Baggage Tracking
		10.3.5 Luggage Logbook
		10.3.6 Electrical Airline Logbook
	10.4 IoT for Smart Airports
		10.4.1 IoT in Smart Operation in Airline Industries
		10.4.2 Fuel Emissions on Fly
		10.4.3 Important Things in Findings
	10.5 Related Work
	10.6 Existing System and Analysis
		10.6.1 Technology Used in the System
	10.7 Proposed System
	10.8 Components in Fuel Reduction
	10.9 Conclusion
	10.10 Future Enhancements
	References
11 Object Detection in IoT-Based Smart Refrigerators Using CNN
	11.1 Introduction
	11.2 Literature Survey
	11.3 Materials and Methods
		11.3.1 Image Processing
		11.3.2 Product Sensing
		11.3.3 Quality Detection
		11.3.4 Android Application
	11.4 Results and Discussion
	11.5 Conclusion
	References
12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT
	12.1 Introduction
	12.2 Literature Review
	12.3 Data Mining Tasks
		12.3.1 Classification
		12.3.2 Regression
		12.3.3 Clustering
		12.3.4 Summarization
		12.3.5 Dependency Modeling
		12.3.6 Association Rule Discovery
		12.3.7 Outlier Detection
		12.3.8 Prediction
	12.4 Feature Selection Techniques in Data Mining
		12.4.1 GAs for Feature Selection
		12.4.2 GP for Feature Selection
		12.4.3 PSO for Feature Selection
		12.4.4 ACO for Feature Selection
	12.5 Classification With Neural Predictive Classifier
		12.5.1 Neural Predictive Classifier
		12.5.2 MapReduce Function on Neural Class
	12.6 Conclusions
	References
13 Impact of COVID-19 on IIoT
	13.1 Introduction
		13.1.1 The Use of IoT During COVID-19
		13.1.2 Consumer IoT
		13.1.3 Commercial IoT
		13.1.4 Industrial Internet of Things (IIoT)
		13.1.5 Infrastructure IoT
		13.1.6 Role of IoT in COVID-19 Response
		13.1.7 Telehealth Consultations
		13.1.8 Digital Diagnostics
		13.1.9 Remote Monitoring
		13.1.10 Robot Assistance
	13.2 The Benefits of Industrial IoT
		13.2.1 How IIoT is Being Used
		13.2.2 Remote Monitoring
		13.2.3 Predictive Maintenance
	13.3 The Challenges of Wide-Spread IIoT Implementation
		13.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring
		13.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency
		13.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses
		13.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work
		13.3.5 Building on the Lessons of 2020
	13.4 Effects of COVID-19 on Industrial Manufacturing
		13.4.1 New Challenges for Industrial Manufacturing
		13.4.2 Smarter Manufacturing for Actionable Insights
		13.4.3 A Promising Future for IIoT Adoption
	13.5 Winners and Losers—The Impact on IoT/ Connected Applications and Digital Transformation due to COVID-19 Impact
	13.6 The Impact of COVID-19 on IoT Applications
		13.6.1 Decreased Interest in Consumer IoT Devices
		13.6.2 Remote Asset Access Becomes Important
		13.6.3 Digital Twins Help With Scenario Planning
		13.6.4 New Uses for Drones
		13.6.5 Specific IoT Health Applications Surge
		13.6.6 Track and Trace Solutions Get Used More Extensively
		13.6.7 Smart City Data Platforms Become Key
	13.7 The Impact of COVID-19 on Technology in General
		13.7.1 Ongoing Projects Are Paused
		13.7.2 Some Enterprise Technologies Take Off
		13.7.3 Declining Demand for New Projects/Devices/Services
		13.7.4 Many Digitalization Initiatives Get Accelerated or Intensified
		13.7.5 The Digital Divide Widens
	13.8 The Impact of COVID-19 on Specific IoT Technologies
		13.8.1 IoT Networks Largely Unaffected
		13.8.2 Technology Roadmaps Get Delayed
	13.9 Coronavirus With IoT, Can Coronavirus Be Restrained?
	13.10 The Potential of IoT in Coronavirus Like Disease Control
	13.11 Conclusion
	References
14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services
	14.1 Introduction
	14.2 Literature Review
	14.3 Design of Smart Ambulance Booking System Through App
	14.4 Smart Ambulance Booking
		14.4.1 Welcome Page
		14.4.2 Sign Up
		14.4.3 Home Page
		14.4.4 Ambulance Section
		14.4.5 Ambulance Selection Page
		14.4.6 Confirmation of Booking and Tracking
	14.5 Result and Discussion
		14.5.1 How It Works?
	14.6 Conclusion
	14.7 Future Scope
	References
15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things
	15.1 Introduction
	15.2 Literature Survey
	15.3 Problem Statement
	15.4 Proposed Methodology
		15.4.1 Design a Smart Wearable Device
		15.4.2 Normalization
		15.4.3 Feature Extraction
		15.4.4 Classification
		15.4.5 Polynomial HMAC Algorithm
	15.5 Result and Discussion
		15.5.1 Accuracy
		15.5.2 Positive Predictive Value
		15.5.3 Sensitivity
		15.5.4 Specificity
		15.5.5 False Out
		15.5.6 False Discovery Rate
		15.5.7 Miss Rate
		15.5.8 F-Score
	15.6 Conclusion
	References
Index
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