Оглавление
Группа авторов. Industrial Internet of Things (IIoT)
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
The Industrial Internet of Things (IIoT) Intelligent Analytics for Predictive Maintenance
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
8.1.3.1 Airports as Smart Cities or Airports as Platforms
8.1.3.2 Architecture of Multidrone
8.1.3.3 The Multidrone Design has the Accompanying Prerequisites
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
8.7.1.1 IoT: Game Changer for Aviation Industry
8.7.2 Applications of AI in the Aviation Industry. 8.7.2.1 Ticketing Systems
8.7.2.2 Flight Maintenance
8.7.2.3 Fuel Efficiency
8.7.2.4 Crew Management
8.7.2.5 Flight Health Checks and Maintenance
8.7.2.6 In-Flight Experience Management
8.7.2.7 Luggage Tracking
8.7.2.8 Airport Management
8.7.2.9 Just the Beginning
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
Algorithm 12.1 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
Algorithm 15.1 Interative Multistate Uplift ANN Algorithm Input: details of symptoms of the disease
15.4.5 Polynomial HMAC Algorithm
Algorithm 15.2 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
Also of Interest
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