Industrial internet of things (IIoT) : intelligent analytics for predictive maintenance / edited by R. Anandan, G. Suseendran, Souvik Pal, Noor Zaman.
Contributor(s): Anandan, R [editor.] | Pal, Souvik [editor.] | Suseendran, G [editor.] | Zaman, Noor [editor.]
Language: English Series: Advances in learning analytics for intelligent cloud-IoT systems: Publisher: Hoboken, NJ : Beverly, MA : Wiley ; Scrivener Publishing, 2022Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119768777; 1119769027; 9781119769026Subject(s): Internet of things -- Industrial applicationsGenre/Form: Electronic books.Additional physical formats: Print version:: Industrial internet of things (IIoT)DDC classification: 004.678 LOC classification: TK5105.8857Online resources: Link text Full text is available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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COLLEGE LIBRARY | COLLEGE LIBRARY | 004.678 In285 2022 (Browse shelf) | Available |
Includes bibliographical references and index.
Table of Contents
Preface xvii
1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field 1
Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur, Yuzo Iano, Andrea Coimbra Segatti, Giulliano Paes Carnielli, Julio Cesar Pereira, Henri Alves de Godoy and Elder Carlos Fernandes
1.1 Introduction 2
1.2 Relationship Between Artificial Intelligence and IoT 5
1.2.1 AI Concept 6
1.2.2 IoT Concept 10
1.3 IoT Ecosystem 15
1.3.1 Industry 4.0 Concept 18
1.3.2 Industrial Internet of Things 19
1.4 Discussion 21
1.5 Trends 23
1.6 Conclusions 24
References 26
2 Analysis on Security in IoT Devices—An Overview 31
T. Nalini and T. Murali Krishna
2.1 Introduction 32
2.2 Security Properties 33
2.3 Security Challenges of IoT 34
2.3.1 Classification of Security Levels 35
2.3.1.1 At Information Level 36
2.3.1.2 At Access Level 36
2.3.1.3 At Functional Level 36
2.3.2 Classification of IoT Layered Architecture 37
2.3.2.1 Edge Layer 37
2.3.2.2 Access Layer 37
2.3.2.3 Application Layer 37
2.4 IoT Security Threats 38
2.4.1 Physical Device Threats 39
2.4.1.1 Device-Threats 39
2.4.1.2 Resource Led Constraints 39
2.4.2 Network-Oriented Communication Assaults 39
2.4.2.1 Structure 40
2.4.2.2 Protocol 40
2.4.3 Data-Based Threats 41
2.4.3.1 Confidentiality 41
2.4.3.2 Availability 41
2.4.3.3 Integrity 42
2.5 Assaults in IoT Devices 43
2.5.1 Devices of IoT 43
2.5.2 Gateways and Networking Devices 44
2.5.3 Cloud Servers and Control Devices 45
2.6 Security Analysis of IoT Platforms 46
2.6.1 ARTIK 46
2.6.2 GiGA IoT Makers 47
2.6.3 AWS IoT 47
2.6.4 Azure IoT 47
2.6.5 Google Cloud IoT (GC IoT) 48
2.7 Future Research Approaches 49
2.7.1 Blockchain Technology 51
2.7.2 5G Technology 52
2.7.3 Fog Computing (FC) and Edge Computing (EC) 52
References 54
3 Smart Automation, Smart Energy, and Grid Management Challenges 59
J. Gayathri Monicka and C. Amuthadevi
3.1 Introduction 60
3.2 Internet of Things and Smart Grids 62
3.2.1 Smart Grid in IoT 63
3.2.2 IoT Application 64
3.2.3 Trials and Imminent Investigation Guidelines 66
3.3 Conceptual Model of Smart Grid 67
3.4 Building Computerization 71
3.4.1 Smart Lighting 73
3.4.2 Smart Parking 73
3.4.3 Smart Buildings 74
3.4.4 Smart Grid 75
3.4.5 Integration IoT in SG 77
3.5 Challenges and Solutions 81
3.6 Conclusions 83
References 83
4 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89
C. Amuthadevi and J. Gayathri Monicka
4.1 Introduction 89
4.1.1 Fundamental Terms in IIoT 91
4.1.1.1 Cloud Computing 92
4.1.1.2 Big Data Analytics 92
4.1.1.3 Fog/Edge Computing 92
4.1.1.4 Internet of Things 93
4.1.1.5 Cyber-Physical-System 94
4.1.1.6 Artificial Intelligence 95
4.1.1.7 Machine Learning 95
4.1.1.8 Machine-to-Machine Communication 99
4.1.2 Intelligent Analytics 99
4.1.3 Predictive Maintenance 100
4.1.4 Disaster Predication and Safety Management 101
4.1.4.1 Natural Disasters 101
4.1.4.2 Disaster Lifecycle 102
4.1.4.3 Disaster Predication 103
4.1.4.4 Safety Management 104
4.1.5 Optimization 105
4.2 Existing Technology and Its Review 106
4.2.1 Survey on Predictive Analysis in Natural Disasters 106
4.2.2 Survey on Safety Management and Recovery 108
4.2.3 Survey on Optimizing Solutions in Natural Disasters 109
4.3 Research Limitation 110
4.3.1 Forward-Looking Strategic Vision (FVS) 110
4.3.2 Availability of Data 111
4.3.3 Load Balancing 111
4.3.4 Energy Saving and Optimization 111
4.3.5 Cost Benefit Analysis 112
4.3.6 Misguidance of Analysis 112
4.4 Finding 113
4.4.1 Data Driven Reasoning 113
4.4.2 Cognitive Ability 113
4.4.3 Edge Intelligence 113
4.4.4 Effect of ML Algorithms and Optimization 114
4.4.5 Security 114
4.5 Conclusion and Future Research 114
4.5.1 Conclusion 114
4.5.2 Future Research 114
References 115
5 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119
Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar
5.1 Introduction 120
5.2 Fuzzy Logic 121
5.2.1 Fuzzy Sets 121
5.2.2 Fuzzy Logic Basics 122
5.2.3 Fuzzy Logic and Power System 122
5.2.4 Fuzzy Logic—Automatic Generation Control 123
5.2.5 Fuzzy Microgrid Wind 123
5.3 Genetic Algorithm 123
5.3.1 Important Aspects of Genetic Algorithm 124
5.3.2 Standard Genetic Algorithm 126
5.3.3 Genetic Algorithm and Its Application 127
5.3.4 Power System and Genetic Algorithm 127
5.3.5 Economic Dispatch Using Genetic Algorithm 128
5.4 Artificial Neural Network 128
5.4.1 The Biological Neuron 129
5.4.2 A Formal Definition of Neural Network 130
5.4.3 Neural Network Models 131
5.4.4 Rosenblatt’s Perceptron 131
5.4.5 Feedforward and Recurrent Networks 132
5.4.6 Back Propagation Algorithm 133
5.4.7 Forward Propagation 133
5.4.8 Algorithm 134
5.4.9 Recurrent Network 135
5.4.10 Examples of Neural Networks 136
5.4.10.1 AND Operation 136
5.4.10.2 OR Operation 137
5.4.10.3 XOR Operation 137
5.4.11 Key Components of an Artificial Neuron Network 138
5.4.12 Neural Network Training 141
5.4.13 Training Types 142
5.4.13.1 Supervised Training 142
5.4.13.2 Unsupervised Training 142
5.4.14 Learning Rates 142
5.4.15 Learning Laws 143
5.4.16 Restructured Power System 144
5.4.17 Advantages of Precise Forecasting of the Price 145
5.5 Conclusion 145
References 146
6 Recent Advances in Wearable Antennas: A Survey 149
Harvinder Kaur and Paras Chawla
6.1 Introduction 150
6.2 Types of Antennas 153
6.2.1 Description of Wearable Antennas 153
6.2.1.1 Microstrip Patch Antenna 153
6.2.1.2 Substrate Integrated Waveguide Antenna 153
6.2.1.3 Planar Inverted-F Antenna 153
6.2.1.4 Monopole Antenna 153
6.2.1.5 Metasurface Loaded Antenna 154
6.3 Design of Wearable Antennas 154
6.3.1 Effect of Substrate and Ground Geometries on Antenna Design 154
6.3.1.1 Conducting Coating on Substrate 154
6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 157
6.3.1.3 Partial Ground Plane 158
6.3.2 Logo Antennas 159
6.3.3 Embroidered Antenna 159
6.3.4 Wearable Antenna Based on Electromagnetic Band Gap 160
6.3.5 Wearable Reconfigurable Antenna 161
6.4 Textile Antennas 162
6.5 Comparison of Wearable Antenna Designs 168
6.6 Fractal Antennas 168
6.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 171
6.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 172
6.6.3 Double-Fractal Layer Wearable Antenna 172
6.6.4 Development of Embroidered Sierpinski Carpet Antenna 172
6.7 Future Challenges of Wearable Antenna Designs 174
6.8 Conclusion 174
References 175
7 An Overview of IoT and Its Application With Machine Learning in Data Center 181
Manikandan Ramanathan and Kumar Narayanan
7.1 Introduction 181
7.1.1 6LoWPAN 183
7.1.2 Data Protocols 185
7.1.2.1 CoAP 185
7.1.2.2 MQTT 187
7.1.2.3 Rest APIs 187
7.1.3 IoT Components 189
7.1.3.1 Hardware 190
7.1.3.2 Middleware 190
7.1.3.3 Visualization 191
7.2 Data Center and Internet of Things 191
7.2.1 Modern Data Centers 191
7.2.2 Data Storage 191
7.2.3 Computing Process 192
7.2.3.1 Fog Computing 192
7.2.3.2 Edge Computing 194
7.2.3.3 Cloud Computing 194
7.2.3.4 Distributed Computing 195
7.2.3.5 Comparison of Cloud Computing and Fog Computing 196
7.3 Machine Learning Models and IoT 196
7.3.1 Classifications of Machine Learning Supported in IoT 197
7.3.1.1 Supervised Learning 197
7.3.1.2 Unsupervised Learning 198
7.3.1.3 Reinforcement Learning 198
7.3.1.4 Ensemble Learning 199
7.3.1.5 Neural Network 199
7.4 Challenges in Data Center and IoT 199
7.4.1 Major Challenges 199
7.5 Conclusion 201
References 201
8 Impact of IoT to Meet Challenges in Drone Delivery System 203
J. Ranjani, P. Kalaichelvi, V.K.G Kalaiselvi, D. Deepika Sree and K. Swathi
8.1 Introduction 204
8.1.1 IoT Components 204
8.1.2 Main Division to Apply IoT in Aviation 205
8.1.3 Required Field of IoT in Aviation 206
8.1.3.1 Airports as Smart Cities or Airports as Platforms 207
8.1.3.2 Architecture of Multidrone 208
8.1.3.3 The Multidrone Design has the Accompanying Prerequisites 208
8.2 Literature Survey 209
8.3 Smart Airport Architecture 211
8.4 Barriers to IoT Implementation 215
8.4.1 How is the Internet of Things Converting the Aviation Enterprise? 216
8.5 Current Technologies in Aviation Industry 216
8.5.1 Methodology or Research Design 217
8.6 IoT Adoption Challenges 218
8.6.1 Deployment of IoT Applications on Broad
Scale Includes the Underlying Challenges 218
8.7 Transforming Airline Industry With Internet of Things 219
8.7.1 How the IoT Is Improving the Aviation Industry 219
8.7.1.1 IoT: Game Changer for Aviation Industry 220
8.7.2 Applications of AI in the Aviation Industry 220
8.7.2.1 Ticketing Systems 220
8.7.2.2 Flight Maintenance 221
8.7.2.3 Fuel Efficiency 221
8.7.2.4 Crew Management 221
8.7.2.5 Flight Health Checks and Maintenance 221
8.7.2.6 In-Flight Experience Management 222
8.7.2.7 Luggage Tracking 222
8.7.2.8 Airport Management 222
8.7.2.9 Just the Beginning 222
8.8 Revolution of Change (Paradigm Shift) 222
8.9 The Following Diagram Shows the Design of the Application 223
8.10 Discussion, Limitations, Future Research, and Conclusion 224
8.10.1 Growth of Aviation IoT Industry 224
8.10.2 IoT Applications—Benefits 225
8.10.3 Operational Efficiency 225
8.10.4 Strategic Differentiation 225
8.10.5 New Revenue 226
8.11 Present and Future Scopes 226
8.11.1 Improving Passenger Experience 226
8.11.2 Safety 227
8.11.3 Management of Goods and Luggage 227
8.11.4 Saving 227
8.12 Conclusion 227
References 227
9 IoT-Based Water Management System for a Healthy Life 229
N. Meenakshi, V. Pandimurugan and S. Rajasoundaran
9.1 Introduction 230
9.1.1 Human Activities as a Source of Pollutants 230
9.2 Water Management Using IoT 231
9.2.1 Water Quality Management Based on IoT Framework 232
9.3 IoT Characteristics and Measurement Parameters 233
9.4 Platforms and Configurations 235
9.5 Water Quality Measuring Sensors and Data Analysis 239
9.6 Wastewater and Storm Water Monitoring Using IoT 241
9.6.1 System Initialization 241
9.6.2 Capture and Storage of Information 241
9.6.3 Information Modeling 241
9.6.4 Visualization and Management of the Information 243
9.7 Sensing and Sampling of Water Treatment Using IoT 244
References 246
10 Fuel Cost Optimization Using IoT in Air Travel 249
P. Kalaichelvi, V. Akila, J. Ranjani, S. Sowmiya and C. Divya
10.1 Introduction 250
10.1.1 Introduction to IoT 250
10.1.2 Processing IoT Data 250
10.1.3 Advantages of IoT 251
10.1.4 Disadvantages of IoT 251
10.1.5 IoT Standards 251
10.1.6 Lite Operating System (Lite OS) 251
10.1.7 Low Range Wide Area Network (LoRaWAN) 252
10.2 Emerging Frameworks in IoT 252
10.2.1 Amazon Web Service (AWS) 252
10.2.2 Azure 252
10.2.3 Brillo/Weave Statement 252
10.2.4 Calvin 252
10.3 Applications of IoT 253
10.3.1 Healthcare in IoT 253
10.3.2 Smart Construction and Smart Vehicles 254
10.3.3 IoT in Agriculture 254
10.3.4 IoT in Baggage Tracking 254
10.3.5 Luggage Logbook 254
10.3.6 Electrical Airline Logbook 254
10.4 IoT for Smart Airports 255
10.4.1 IoT in Smart Operation in Airline Industries 257
10.4.2 Fuel Emissions on Fly 258
10.4.3 Important Things in Findings 258
10.5 Related Work 258
10.6 Existing System and Analysis 264
10.6.1 Technology Used in the System 265
10.7 Proposed System 268
10.8 Components in Fuel Reduction 276
10.9 Conclusion 276
10.10 Future Enhancements 277
References 277
11 Object Detection in IoT-Based Smart Refrigerators Using CNN 281
Ashwathan R., Asnath Victy Phamila Y., Geetha S. and Kalaivani K.
11.1 Introduction 282
11.2 Literature Survey 283
11.3 Materials and Methods 287
11.3.1 Image Processing 292
11.3.2 Product Sensing 292
11.3.3 Quality Detection 293
11.3.4 Android Application 293
11.4 Results and Discussion 294
11.5 Conclusion 299
References 299
12 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT 301
Latha Parthiban, Maithili Devi Reddy and A. Kumaravel
12.1 Introduction 302
12.2 Literature Review 302
12.3 Data Mining Tasks 304
12.3.1 Classification 305
12.3.2 Regression 306
12.3.3 Clustering 306
12.3.4 Summarization 306
12.3.5 Dependency Modeling 306
12.3.6 Association Rule Discovery 307
12.3.7 Outlier Detection 307
12.3.8 Prediction 307
12.4 Feature Selection Techniques in Data Mining 308
12.4.1 GAs for Feature Selection 308
12.4.2 GP for Feature Selection 309
12.4.3 PSO for Feature Selection 310
12.4.4 ACO for Feature Selection 311
12.5 Classification With Neural Predictive Classifier 312
12.5.1 Neural Predictive Classifier 313
12.5.2 MapReduce Function on Neural Class 317
12.6 Conclusions 319
References 319
13 Impact of COVID-19 on IIoT 321
K. Priyadarsini, S. Karthik, K. Malathi and M.V.V Rama Rao
13.1 Introduction 321
13.1.1 The Use of IoT During COVID-19 321
13.1.2 Consumer IoT 322
13.1.3 Commercial IoT 322
13.1.4 Industrial Internet of Things (IIoT) 322
13.1.5 Infrastructure IoT 322
13.1.6 Role of IoT in COVID-19 Response 323
13.1.7 Telehealth Consultations 323
13.1.8 Digital Diagnostics 323
13.1.9 Remote Monitoring 323
13.1.10 Robot Assistance 323
13.2 The Benefits of Industrial IoT 326
13.2.1 How IIoT is Being Used 327
13.2.2 Remote Monitoring 327
13.2.3 Predictive Maintenance 328
13.3 The Challenges of Wide-Spread IIoT Implementation 329
13.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring 330
13.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency 330
13.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses 331
13.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work 332
13.3.5 Building on the Lessons of 2020 332
13.4 Effects of COVID-19 on Industrial Manufacturing 332
13.4.1 New Challenges for Industrial Manufacturing 333
13.4.2 Smarter Manufacturing for Actionable Insights 333
13.4.3 A Promising Future for IIoT Adoption 334
13.5 Winners and Losers—The Impact on IoT/Connected Applications and Digital Transformation due to
COVID-19 Impact 335
13.6 The Impact of COVID-19 on IoT Applications 337
13.6.1 Decreased Interest in Consumer IoT Devices 338
13.6.2 Remote Asset Access Becomes Important 338
13.6.3 Digital Twins Help With Scenario Planning 339
13.6.4 New Uses for Drones 339
13.6.5 Specific IoT Health Applications Surge 340
13.6.6 Track and Trace Solutions Get Used More Extensively 340
13.6.7 Smart City Data Platforms Become Key 340
13.7 The Impact of COVID-19 on Technology in General 341
13.7.1 Ongoing Projects Are Paused 341
13.7.2 Some Enterprise Technologies Take Off 341
13.7.3 Declining Demand for New Projects/Devices/ Services 342
13.7.4 Many Digitalization Initiatives Get Accelerated or Intensified 342
13.7.5 The Digital Divide Widens 343
13.8 The Impact of COVID-19 on Specific IoT Technologies 343
13.8.1 IoT Networks Largely Unaffected 343
13.8.2 Technology Roadmaps Get Delayed 344
13.9 Coronavirus With IoT, Can Coronavirus Be Restrained? 344
13.10 The Potential of IoT in Coronavirus Like Disease Control 345
13.11 Conclusion 346
References 346
14 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services 349
Sumanta Chatterjee, Pabitra Kumar Bhunia, Poulami Mondal, Aishwarya Sadhu and Anusua Biswas
14.1 Introduction 350
14.2 Literature Review 353
14.3 Design of Smart Ambulance Booking System Through App 356
14.4 Smart Ambulance Booking 359
14.4.1 Welcome Page 360
14.4.2 Sign Up 360
14.4.3 Home Page 361
14.4.4 Ambulance Section 361
14.4.5 Ambulance Selection Page 362
14.4.6 Confirmation of Booking and Tracking 363
14.5 Result and Discussion 363
14.5.1 How It Works? 365
14.6 Conclusion 365
14.7 Future Scope 366
References 366
15 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things 369
Resmi G. Nair and N. Kumar
15.1 Introduction 370
15.2 Literature Survey 371
15.3 Problem Statement 372
15.4 Proposed Methodology 373
15.4.1 Design a Smart Wearable Device 373
15.4.2 Normalization 374
15.4.3 Feature Extraction 377
15.4.4 Classification 378
15.4.5 Polynomial HMAC Algorithm 379
15.5 Result and Discussion 382
15.5.1 Accuracy 382
15.5.2 Positive Predictive Value 382
15.5.3 Sensitivity 383
15.5.4 Specificity 383
15.5.5 False Out 383
15.5.6 False Discovery Rate 383
15.5.7 Miss Rate 383
15.5.8 F-Score 383
15.6 Conclusion 390
References 390
Index 393
Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach 73.5 billion dollars in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case studies in the areas of the IIoT, robotic and intelligent systems, and web-based applications which will be of interest to working professionals and those in education and research involved in a broad cross-section of technical disciplines.
About the Author
R. Anandan completed his PhD in Computer Science and Engineering, is IBMS/390 Mainframe professional and is recognized as a Chartered Engineer from the Institution of Engineers in India and received a fellowship from Bose Science Society, India. He is a Professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 110 research papers in various international journals, authored 9 books in the computer science and engineering disciplines and has received 13 awards.
G. Suseendran received his PhD in Information Technoslogy-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He is currently working as assistant professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 75 research papers in various referred journals, authored 11 books and received 6 awards.
Dr. Souvik Pal is an associate professor and Head of the Computer Science and Engineering Department, Global Institute of Management and Technology, West Bengal, India. Dr. Pal received his PhD in the field of computer science and engineering. His editor/author of 12 books and has been granted 3 patents. He is the recipient of one Lifetime Achievement Awards in 2018.
Noor Zaman completed his PhD in IT from University Technology Petronas (UTP) Malaysia. He has authored many research papers in WoS/ISI indexed and impact factor research journals and edited 12 books in computer science.
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