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Industrial Internet of Things (IIoT)
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Industrial Internet of Things (IIoT)
by R. Anandan, Suseendran Gopalakrishnan, Souvik Pal, Noor Zaman
Industrial Internet of Things (IIoT)
Cover
Title Page
Copyright
Preface
1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field
2 Analysis on Security in IoT Devices—An Overview
3 Smart Automation, Smart Energy, and Grid Management Challenges
4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management
5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques
6 Recent Advances in Wearable Antennas: A Survey
7 An Overview of IoT and Its Application With Machine Learning in Data Center
8 Impact of IoT to Meet Challenges in Drone Delivery System
9 IoT-Based Water Management System for a Healthy Life
10 Fuel Cost Optimization Using IoT in Air Travel
11 Object Detection in IoT-Based Smart Refrigerators Using CNN
12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT
13 Impact of COVID-19 on IIoT
14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services
15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things
Index
Also of Interest
End User License Agreement
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Industrial Internet of Things (IIoT)
Table of Contents
Cover
Title Page
Copyright
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
1.3 IoT Ecosystem
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
2.4 IoT Security Threats
2.5 Assaults in IoT Devices
2.6 Security Analysis of IoT Platforms
2.7 Future Research Approaches
References
3 Smart Automation, Smart Energy, and Grid Management Challenges
3.1 Introduction
3.2 Internet of Things and Smart Grids
3.3 Conceptual Model of Smart Grid
3.4 Building Computerization
3.5 Challenges and Solutions
3.6 Conclusions
References
4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management
4.1 Introduction
4.2 Existing Technology and Its Review
4.3 Research Limitation
4.4 Finding
4.5 Conclusion and Future Research
References
5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques
5.1 Introduction
5.2 Fuzzy Logic
5.3 Genetic Algorithm
5.4 Artificial Neural Network
5.5 Conclusion
References
6 Recent Advances in Wearable Antennas: A Survey
6.1 Introduction
6.2 Types of Antennas
6.3 Design of Wearable Antennas
6.4 Textile Antennas
6.5 Comparison of Wearable Antenna Designs
6.6 Fractal Antennas
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
7.2 Data Center and Internet of Things
7.3 Machine Learning Models and IoT
7.4 Challenges in Data Center and IoT
7.5 Conclusion
References
8 Impact of IoT to Meet Challenges in Drone Delivery System
8.1 Introduction
8.2 Literature Survey
8.3 Smart Airport Architecture
8.4 Barriers to IoT Implementation
8.5 Current Technologies in Aviation Industry
8.6 IoT Adoption Challenges
8.7 Transforming Airline Industry With Internet of Things
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
8.11 Present and Future Scopes
8.12 Conclusion
References
9 IoT-Based Water Management System for a Healthy Life
9.1 Introduction
9.2 Water Management Using IoT
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
9.7 Sensing and Sampling of Water Treatment Using IoT
References
10 Fuel Cost Optimization Using IoT in Air Travel
10.1 Introduction
10.2 Emerging Frameworks in IoT
10.3 Applications of IoT
10.4 IoT for Smart Airports
10.5 Related Work
10.6 Existing System and Analysis
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
11.4 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
12.4 Feature Selection Techniques in Data Mining
12.5 Classification With Neural Predictive Classifier
12.6 Conclusions
References
13 Impact of COVID-19 on IIoT
13.1 Introduction
13.2 The Benefits of Industrial IoT
13.3 The Challenges of Wide-Spread IIoT Implementation
13.4 Effects of COVID-19 on Industrial Manufacturing
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
13.7 The Impact of COVID-19 on Technology in General
13.8 The Impact of COVID-19 on Specific IoT Technologies
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
14.5 Result and Discussion
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
15.5 Result and Discussion
15.6 Conclusion
References
Index
Also of Interest
End User License Agreement
Guide
Cover
Table of Contents
Title page
Copyright
Preface
Begin Reading
Index
Also of Interest
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Big data analytics illustration.
Figure 1.2 Big data illustration.
Figure 1.3 AI and data science illustration.
Figure 1.4 Internet of Things.
Figure 1.5 IoT illustration.
Figure 1.6 Connection to the car’s illustration.
Figure 1.7 IoT devices.
Figure 1.8 IoT and blockchain illustration.
Figure 1.9 Maintenance IoT vehicle illustration.
Chapter 3
Figure 3.1 Connections and linked objects of IoT.
Figure 3.2 Autonomous robots with Industry 4.0.
Figure 3.3 Architecture of SCADA.
Figure 3.4 Smart grid conceptual model.
Figure 3.5 Smart grid security.
Figure 3.6 Classification of IoT-aided smart grid.
Figure 3.7 Advanced metering infrastructure.
Chapter 4
Figure 4.1 Working of IIoT.
Figure 4.2 Big data analytics with IoT.
Figure 4.3 Types of ML.
Figure 4.4 Supervised and unsupervised learning.
Figure 4.5 Reinforcement learning.
Figure 4.6 Disaster lifecycle.
Chapter 5
Figure 5.1 Construction of biological neuron.
Figure 5.2 McCulloch-Pitts model.
Figure 5.3 Multi-layer perceptron structure.
Figure 5.4 A two-layered network.
Figure 5.5 A recurrent network.
Figure 5.6 AND network.
Figure 5.7 OR network.
Figure 5.8 XOR network.
Chapter 6
Figure 6.1 Schematic of single patch on textile substrate [34].
Figure 6.2 Side view of the five-layer model [34].
Figure 6.3 The antenna geometry with MTM cells [36].
Figure 6.4 Antenna design with partial ground: (a) front view; (b) back view.
Figure 6.5 Logo-based tracking system [38].
Figure 6.6 Antenna with EBG structure [41].
Figure 6.7 Reconfigurable Antenna of O-shape: (a) OFF; (b) ON [43].
Figure 6.8 UHF RFID tag antenna design [45].
Figure 6.9 UHF RFID tag read range measurements vs. frequency [45].
Figure 6.10 Gain, efficiency vs. frequency for Zelt antenna [46].
Figure 6.11 Wearable fractal antenna with different iterations: (a) 0th; (b) 1st...
Figure 6.12 Wearable fractal antenna with iterations: (a) 0th; (b) 1st; and (c) ...
Figure 6.13 The embroidered Sierpinski carpet antenna: (a) substrate material; (...
Chapter 7
Figure 7.1 IoT protocol with OSI standard.
Figure 7.2 IoT layers and protocols.
Figure 7.3 IoT packet headers.
Figure 7.4 Confirmable message passing mechanism.
Figure 7.5 Non-confirmable message passing mechanism.
Figure 7.6 MQTT subscriber-publisher model.
Figure 7.7 IoT components.
Figure 7.8 IoT communication architecture.
Figure 7.9 Data center architecture.
Figure 7.10 Fog computing.
Figure 7.11 Edge computing.
Figure 7.12 Stages of machine learning.
Figure 7.13 Classification of machine learning that can be supported in IoT.
Figure 7.14 Challenges in IoT and data center.
Chapter 8
Figure 8.1 Multidrone architecture for autonomous cinematography.
Figure 8.2 Current technologies in aviation industry.
Chapter 9
Figure 9.1 Water quality factors.
Figure 9.2 Water quality management framework.
Figure 9.3 Intelligent IoT-based water quality management framework.
Figure 9.4 Smart wastewater quality monitoring system.
Figure 9.5 Containments of sensing in water.
Chapter 10
Figure 10.1 Aircraft monitoring system.
Figure 10.2 IoT information chain to collect aviation data.
Figure 10.3 High-end engine in jet plane.
Figure 10.4 Fuel emissions from jet airplane.
Figure 10.5 IoT real-time connectivity in airline industries.
Figure 10.6 Smart luggage IoT connected tracking system.
Figure 10.7 Evaluation and performance improvement.
Figure 10.8 Steps involved in fuel consumption.
Figure 10.9 Optimization of aircraft fuel.
Figure 10.10 Overall idea for fuel cost optimization.
Figure 10.11 High-end platform in travel route comparison for optimization.
Chapter 11
Figure 11.1 Flow diagram of the proposed system.
Figure 11.2 Architecture of the proposed system.
Figure 11.3 YOLO architecture.
Figure 11.4 Pages in the Android app.
Figure 11.5 Experimental setup of the proposed system.
Figure 11.6 (a) Annotating images in VoTT (b) Annotating images in VoTT.
Figure 11.7 Object detection using YOLO model trained with 100 (a) vs. 200 (b) v...
Figure 11.8 Object detection using YOLO model trained with 100 (a) vs. 200 (b) v...
Figure 11.9 Graph of epoch vs. loss during training.
Figure 11.10 Output from NodeMCU in serial monitor.
Chapter 12
Figure 12.1 Knowledge discovery in databases.
Figure 12.2 Data mining in Pharmacovigilance.
Figure 12.3 Semi-Convergent Matrix construction for improving search accuracy.
Figure 12.4 Structure of neural predictive classifier for classification.
Figure 12.5 Processing diagram of activation function for classification.
Figure 12.6 Neural predictive classifier.
Chapter 13
Figure 13.1 IIoT infrastructure.
Figure 13.2 Applications of IIoT.
Figure 13.3 Impact of COVID-19 on the connected applications software market.
Figure 13.4 Examples of “winners” in industrial IoT solutions.
Figure 13.5 Impact of COVID-19 on IoT.
Figure 13.6 Download rank.
Figure 13.7 Usage of Librestram’s Onsight.
Figure 13.8 Download Rank of the app “Kinsa Health”.
Figure 13.9 Impact of COVID-19 on technology.
Figure 13.10 Zoom usage analysis.
Chapter 14
Figure 14.1 Use-case diagram.
Figure 14.2 Welcome page.
Figure 14.3 Signup page.
Figure 14.4 Home page.
Figure 14.5 Ambulance section.
Figure 14.6 Ambulance selection page.
Figure 14.7 Confirmation of booking.
Figure 14.8 Ambulance booking app.
Figure 14.9 Booking system.
Chapter 15
Figure 15.1 Schematic representation of the suggested methodology.
Figure 15.2 Hardware prototype.
Figure 15.3 Health data vs. specificity.
Figure 15.4 Health data vs. sensitivity.
Figure 15.5 Health data vs. false out.
Figure 15.6 Health data vs. positive predictive value.
Figure 15.7 Health data vs. false discovery rate.
Figure 15.8 Health data vs. miss rate.
Figure 15.9 Health data vs. F-score.
Figure 15.10 Health data vs. accuracy.
Figure 15.11 Time taken for encryption.
Figure 15.12 Time taken for decryption.
List of Tables
Chapter 4
Table 4.1 Types of assets needs safety [50].
Chapter 6
Table 6.1 Chronology for the wearable antennas.
Table 6.2 SAR values of the antenna with varying distance [35].
Table 6.3 Max average SAR values at different distances from human body [36].
Table 6.4 Impedance characteristics of proposed denim antenna [37].
Table 6.5 Different textile materials with their dielectric constant values.
Table 6.6 Description of textile materials.
Table 6.7 Comparison of wearable antenna designs.
Chapter 7
Table 7.1 IoT protocols.
Table 7.2 Comparison of CoAP and MQTT.
Table 7.3 Pros and cons of fog computing.
Table 7.4 Comparison of cloud and fog computing.
Chapter 9
Table 9.1 Arduino specifications.
Table 9.2 Raspberry Pi and Arduino.
Table 9.3 Water sensors.
Chapter 11
Table 11.1 Materials used in the proposed system.
Table 11.2 Uses and purpose of sensors.
Table 11.3 Analysis of the model (10 testing images - 40 items).
Chapter 15
Table 15.1 Comparison of encryption time.
Table 15.2 Comparison of decryption time.
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