Industrial Internet of Things (IIoT)

R. Anandan, PhD completed his degree in Computer Science and Engineering, is an IBMS/390 Mainframe professional, 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, PhD received his degree in Information Technology-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He passed away during the production of this book. Souvik Pal, PhD is an associate professor in the Department of Computer Science and Engineering at Sister Nivedita University (Techno India Group), Kolkata, India. Dr. Pal received his PhD in the field of computer science and engineering. He is the editor/author of 12 books and has been granted 3 patents. He is the recipient of a Lifetime Achievement Award in 2018. Noor Zaman, PhD completed his degree 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.

• Preface xvii1 A Look at IIoT: The Perspective of IoT Technology Applied in the Industrial Field 1Ana 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 Fernandes1.1 Introduction 21.2 Relationship Between Artificial Intelligence and IoT 51.2.1 AI Concept 61.2.2 IoT Concept 101.3 IoT Ecosystem 151.3.1 Industry 4.0 Concept 181.3.2 Industrial Internet of Things 191.4 Discussion 211.5 Trends 231.6 Conclusions 24References 262 Analysis on Security in IoT Devices—An Overview 31T. Nalini and T. Murali Krishna2.1 Introduction 322.2 Security Properties 332.3 Security Challenges of IoT 342.3.1 Classification of Security Levels 352.3.1.1 At Information Level 362.3.1.2 At Access Level 362.3.1.3 At Functional Level 362.3.2 Classification of IoT Layered Architecture 372.3.2.1 Edge Layer 372.3.2.2 Access Layer 372.3.2.3 Application Layer 372.4 IoT Security Threats 382.4.1 Physical Device Threats 392.4.1.1 Device-Threats 392.4.1.2 Resource Led Constraints 392.4.2 Network-Oriented Communication Assaults 392.4.2.1 Structure 402.4.2.2 Protocol 402.4.3 Data-Based Threats 412.4.3.1 Confidentiality 412.4.3.2 Availability 412.4.3.3 Integrity 422.5 Assaults in IoT Devices 432.5.1 Devices of IoT 432.5.2 Gateways and Networking Devices 442.5.3 Cloud Servers and Control Devices 452.6 Security Analysis of IoT Platforms 462.6.1 ARTIK 462.6.2 GiGA IoT Makers 472.6.3 AWS IoT 472.6.4 Azure IoT 472.6.5 Google Cloud IoT (GC IoT) 482.7 Future Research Approaches 492.7.1 Blockchain Technology 512.7.2 5G Technology 522.7.3 Fog Computing (FC) and Edge Computing (EC) 52References 543 Smart Automation, Smart Energy, and Grid Management Challenges 59J. Gayathri Monicka and C. Amuthadevi3.1 Introduction 603.2 Internet of Things and Smart Grids 623.2.1 Smart Grid in IoT 633.2.2 IoT Application 643.2.3 Trials and Imminent Investigation Guidelines 663.3 Conceptual Model of Smart Grid 673.4 Building Computerization 713.4.1 Smart Lighting 733.4.2 Smart Parking 733.4.3 Smart Buildings 743.4.4 Smart Grid 753.4.5 Integration IoT in SG 773.5 Challenges and Solutions 813.6 Conclusions 83References 834 Industrial Automation (IIoT) 4.0: An Insight Into Safety Management 89C. Amuthadevi and J. Gayathri Monicka4.1 Introduction 894.1.1 Fundamental Terms in IIoT 914.1.1.1 Cloud Computing 924.1.1.2 Big Data Analytics 924.1.1.3 Fog/Edge Computing 924.1.1.4 Internet of Things 934.1.1.5 Cyber-Physical-System 944.1.1.6 Artificial Intelligence 954.1.1.7 Machine Learning 954.1.1.8 Machine-to-Machine Communication 994.1.2 Intelligent Analytics 994.1.3 Predictive Maintenance 1004.1.4 Disaster Predication and Safety Management 1014.1.4.1 Natural Disasters 1014.1.4.2 Disaster Lifecycle 1024.1.4.3 Disaster Predication 1034.1.4.4 Safety Management 1044.1.5 Optimization 1054.2 Existing Technology and Its Review 1064.2.1 Survey on Predictive Analysis in Natural Disasters 1064.2.2 Survey on Safety Management and Recovery 1084.2.3 Survey on Optimizing Solutions in Natural Disasters 1094.3 Research Limitation 1104.3.1 Forward-Looking Strategic Vision (FVS) 1104.3.2 Availability of Data 1114.3.3 Load Balancing 1114.3.4 Energy Saving and Optimization 1114.3.5 Cost Benefit Analysis 1124.3.6 Misguidance of Analysis 1124.4 Finding 1134.4.1 Data Driven Reasoning 1134.4.2 Cognitive Ability 1134.4.3 Edge Intelligence 1134.4.4 Effect of ML Algorithms and Optimization 1144.4.5 Security 1144.5 Conclusion and Future Research 1144.5.1 Conclusion 1144.5.2 Future Research 114References 1155 An Industrial Perspective on Restructured Power Systems Using Soft Computing Techniques 119Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar 5.1 Introduction 1205.2 Fuzzy Logic 1215.2.1 Fuzzy Sets 1215.2.2 Fuzzy Logic Basics 1225.2.3 Fuzzy Logic and Power System 1225.2.4 Fuzzy Logic—Automatic Generation Control 1235.2.5 Fuzzy Microgrid Wind 1235.3 Genetic Algorithm 1235.3.1 Important Aspects of Genetic Algorithm 1245.3.2 Standard Genetic Algorithm 1265.3.3 Genetic Algorithm and Its Application 1275.3.4 Power System and Genetic Algorithm 1275.3.5 Economic Dispatch Using Genetic Algorithm 1285.4 Artificial Neural Network 1285.4.1 The Biological Neuron 1295.4.2 A Formal Definition of Neural Network 1305.4.3 Neural Network Models 1315.4.4 Rosenblatt’s Perceptron 1315.4.5 Feedforward and Recurrent Networks 1325.4.6 Back Propagation Algorithm 1335.4.7 Forward Propagation 1335.4.8 Algorithm 1345.4.9 Recurrent Network 1355.4.10 Examples of Neural Networks 1365.4.10.1 AND Operation 1365.4.10.2 OR Operation 1375.4.10.3 XOR Operation 1375.4.11 Key Components of an Artificial Neuron Network 1385.4.12 Neural Network Training 1415.4.13 Training Types 1425.4.13.1 Supervised Training 1425.4.13.2 Unsupervised Training 1425.4.14 Learning Rates 1425.4.15 Learning Laws 1435.4.16 Restructured Power System 1445.4.17 Advantages of Precise Forecasting of the Price 1455.5 Conclusion 145References 1466 Recent Advances in Wearable Antennas: A Survey 149Harvinder Kaur and Paras Chawla6.1 Introduction 1506.2 Types of Antennas 1536.2.1 Description of Wearable Antennas 1536.2.1.1 Microstrip Patch Antenna 1536.2.1.2 Substrate Integrated Waveguide Antenna 1536.2.1.3 Planar Inverted-F Antenna 1536.2.1.4 Monopole Antenna 1536.2.1.5 Metasurface Loaded Antenna 1546.3 Design of Wearable Antennas 1546.3.1 Effect of Substrate and Ground Geometries on Antenna Design 1546.3.1.1 Conducting Coating on Substrate 1546.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 1576.3.1.3 Partial Ground Plane 1586.3.2 Logo Antennas 1596.3.3 Embroidered Antenna 1596.3.4 Wearable Antenna Based on Electromagnetic Band Gap 1606.3.5 Wearable Reconfigurable Antenna 1616.4 Textile Antennas 1626.5 Comparison of Wearable Antenna Designs 1686.6 Fractal Antennas 1686.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 1716.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 1726.6.3 Double-Fractal Layer Wearable Antenna 1726.6.4 Development of Embroidered Sierpinski Carpet Antenna 1726.7 Future Challenges of Wearable Antenna Designs 1746.8 Conclusion 174References 1757 An Overview of IoT and Its Application With Machine Learning in Data Center 181Manikandan Ramanathan and Kumar Narayanan7.1 Introduction 1817.1.1 6LoWPAN 1837.1.2 Data Protocols 1857.1.2.1 CoAP 1857.1.2.2 MQTT 1877.1.2.3 Rest APIs 1877.1.3 IoT Components 1897.1.3.1 Hardware 1907.1.3.2 Middleware 1907.1.3.3 Visualization 1917.2 Data Center and Internet of Things 1917.2.1 Modern Data Centers 1917.2.2 Data Storage 1917.2.3 Computing Process 1927.2.3.1 Fog Computing 1927.2.3.2 Edge Computing 1947.2.3.3 Cloud Computing 1947.2.3.4 Distributed Computing 1957.2.3.5 Comparison of Cloud Computing and Fog Computing 1967.3 Machine Learning Models and IoT 1967.3.1 Classifications of Machine Learning Supported in IoT 1977.3.1.1 Supervised Learning 1977.3.1.2 Unsupervised Learning 1987.3.1.3 Reinforcement Learning 1987.3.1.4 Ensemble Learning 1997.3.1.5 Neural Network 1997.4 Challenges in Data Center and IoT 1997.4.1 Major Challenges 1997.5 Conclusion 201References 2018 Impact of IoT to Meet Challenges in Drone Delivery System 203J. Ranjani, P. Kalaichelvi, V.K.G Kalaiselvi, D. Deepika Sree and K. Swathi8.1 Introduction 2048.1.1 IoT Components 2048.1.2 Main Division to Apply IoT in Aviation 2058.1.3 Required Field of IoT in Aviation 2068.1.3.1 Airports as Smart Cities or Airports as Platforms 2078.1.3.2 Architecture of Multidrone 2088.1.3.3 The Multidrone Design has the Accompanying Prerequisites 2088.2 Literature Survey 2098.3 Smart Airport Architecture 2118.4 Barriers to IoT Implementation 2158.4.1 How is the Internet of Things Converting the Aviation Enterprise? 2168.5 Current Technologies in Aviation Industry 2168.5.1 Methodology or Research Design 2178.6 IoT Adoption Challenges 2188.6.1 Deployment of IoT Applications on BroadScale Includes the Underlying Challenges 2188.7 Transforming Airline Industry With Internet of Things 2198.7.1 How the IoT Is Improving the Aviation Industry 2198.7.1.1 IoT: Game Changer for Aviation Industry 2208.7.2 Applications of AI in the Aviation Industry 2208.7.2.1 Ticketing Systems 2208.7.2.2 Flight Maintenance 2218.7.2.3 Fuel Efficiency 2218.7.2.4 Crew Management 2218.7.2.5 Flight Health Checks and Maintenance 2218.7.2.6 In-Flight Experience Management 2228.7.2.7 Luggage Tracking 2228.7.2.8 Airport Management 2228.7.2.9 Just the Beginning 2228.8 Revolution of Change (Paradigm Shift) 2228.9 The Following Diagram Shows the Design of the Application 2238.10 Discussion, Limitations, Future Research, and Conclusion 2248.10.1 Growth of Aviation IoT Industry 2248.10.2 IoT Applications—Benefits 2258.10.3 Operational Efficiency 2258.10.4 Strategic Differentiation 2258.10.5 New Revenue 2268.11 Present and Future Scopes 2268.11.1 Improving Passenger Experience 2268.11.2 Safety 2278.11.3 Management of Goods and Luggage 2278.11.4 Saving 2278.12 Conclusion 227References 2279 IoT-Based Water Management System for a Healthy Life 229N. Meenakshi, V. Pandimurugan and S. Rajasoundaran9.1 Introduction 2309.1.1 Human Activities as a Source of Pollutants 2309.2 Water Management Using IoT 2319.2.1 Water Quality Management Based on IoT Framework 2329.3 IoT Characteristics and Measurement Parameters 2339.4 Platforms and Configurations 2359.5 Water Quality Measuring Sensors and Data Analysis 2399.6 Wastewater and Storm Water Monitoring Using IoT 2419.6.1 System Initialization 2419.6.2 Capture and Storage of Information 2419.6.3 Information Modeling 2419.6.4 Visualization and Management of the Information 2439.7 Sensing and Sampling of Water Treatment Using IoT 244References 24610 Fuel Cost Optimization Using IoT in Air Travel 249P. Kalaichelvi, V. Akila, J. Ranjani, S. Sowmiya and C. Divya10.1 Introduction 25010.1.1 Introduction to IoT 25010.1.2 Processing IoT Data 25010.1.3 Advantages of IoT 25110.1.4 Disadvantages of IoT 25110.1.5 IoT Standards 25110.1.6 Lite Operating System (Lite OS) 25110.1.7 Low Range Wide Area Network (LoRaWAN) 25210.2 Emerging Frameworks in IoT 25210.2.1 Amazon Web Service (AWS) 25210.2.2 Azure 25210.2.3 Brillo/Weave Statement 25210.2.4 Calvin 25210.3 Applications of IoT 25310.3.1 Healthcare in IoT 25310.3.2 Smart Construction and Smart Vehicles 25410.3.3 IoT in Agriculture 25410.3.4 IoT in Baggage Tracking 25410.3.5 Luggage Logbook 25410.3.6 Electrical Airline Logbook 25410.4 IoT for Smart Airports 25510.4.1 IoT in Smart Operation in Airline Industries 25710.4.2 Fuel Emissions on Fly 25810.4.3 Important Things in Findings 25810.5 Related Work 25810.6 Existing System and Analysis 26410.6.1 Technology Used in the System 26510.7 Proposed System 26810.8 Components in Fuel Reduction 27610.9 Conclusion 27610.10 Future Enhancements 277References 27711 Object Detection in IoT-Based Smart Refrigerators Using CNN 281Ashwathan R., Asnath Victy Phamila Y., Geetha S. and Kalaivani K.11.1 Introduction 28211.2 Literature Survey 28311.3 Materials and Methods 28711.3.1 Image Processing 29211.3.2 Product Sensing 29211.3.3 Quality Detection 29311.3.4 Android Application 29311.4 Results and Discussion 29411.5 Conclusion 299References 29912 Effective Methodologies in Pharmacovigilance for Identifying Adverse Drug Reactions Using IoT 301Latha Parthiban, Maithili Devi Reddy and A. Kumaravel12.1 Introduction 30212.2 Literature Review 30212.3 Data Mining Tasks 30412.3.1 Classification 30512.3.2 Regression 30612.3.3 Clustering 30612.3.4 Summarization 30612.3.5 Dependency Modeling 30612.3.6 Association Rule Discovery 30712.3.7 Outlier Detection 30712.3.8 Prediction 30712.4 Feature Selection Techniques in Data Mining 30812.4.1 GAs for Feature Selection 30812.4.2 GP for Feature Selection 30912.4.3 PSO for Feature Selection 31012.4.4 ACO for Feature Selection 31112.5 Classification With Neural Predictive Classifier 31212.5.1 Neural Predictive Classifier 31312.5.2 MapReduce Function on Neural Class 31712.6 Conclusions 319References 31913 Impact of COVID-19 on IIoT 321K. Priyadarsini, S. Karthik, K. Malathi and M.V.V Rama Rao13.1 Introduction 32113.1.1 The Use of IoT During COVID-19 32113.1.2 Consumer IoT 32213.1.3 Commercial IoT 32213.1.4 Industrial Internet of Things (IIoT) 32213.1.5 Infrastructure IoT 32213.1.6 Role of IoT in COVID-19 Response 32313.1.7 Telehealth Consultations 32313.1.8 Digital Diagnostics 32313.1.9 Remote Monitoring 32313.1.10 Robot Assistance 32313.2 The Benefits of Industrial IoT 32613.2.1 How IIoT is Being Used 32713.2.2 Remote Monitoring 32713.2.3 Predictive Maintenance 32813.3 The Challenges of Wide-Spread IIoT Implementation 32913.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring 33013.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency 33013.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses 33113.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work 33213.3.5 Building on the Lessons of 2020 33213.4 Effects of COVID-19 on Industrial Manufacturing 33213.4.1 New Challenges for Industrial Manufacturing 33313.4.2 Smarter Manufacturing for Actionable Insights 33313.4.3 A Promising Future for IIoT Adoption 33413.5 Winners and Losers—The Impact on IoT/Connected Applications and Digital Transformation due toCOVID-19 Impact 33513.6 The Impact of COVID-19 on IoT Applications 33713.6.1 Decreased Interest in Consumer IoT Devices 33813.6.2 Remote Asset Access Becomes Important 33813.6.3 Digital Twins Help With Scenario Planning 33913.6.4 New Uses for Drones 33913.6.5 Specific IoT Health Applications Surge 34013.6.6 Track and Trace Solutions Get Used More Extensively 34013.6.7 Smart City Data Platforms Become Key 34013.7 The Impact of COVID-19 on Technology in General 34113.7.1 Ongoing Projects Are Paused 34113.7.2 Some Enterprise Technologies Take Off 34113.7.3 Declining Demand for New Projects/Devices/ Services 34213.7.4 Many Digitalization Initiatives Get Accelerated or Intensified 34213.7.5 The Digital Divide Widens 34313.8 The Impact of COVID-19 on Specific IoT Technologies 34313.8.1 IoT Networks Largely Unaffected 34313.8.2 Technology Roadmaps Get Delayed 34413.9 Coronavirus With IoT, Can Coronavirus Be Restrained? 34413.10 The Potential of IoT in Coronavirus Like Disease Control 34513.11 Conclusion 346References 34614 A Comprehensive Composite of Smart Ambulance Booking and Tracking Systems Using IoT for Digital Services 349Sumanta Chatterjee, Pabitra Kumar Bhunia, Poulami Mondal, Aishwarya Sadhu and Anusua Biswas14.1 Introduction 35014.2 Literature Review 35314.3 Design of Smart Ambulance Booking System Through App 35614.4 Smart Ambulance Booking 35914.4.1 Welcome Page 36014.4.2 Sign Up 36014.4.3 Home Page 36114.4.4 Ambulance Section 36114.4.5 Ambulance Selection Page 36214.4.6 Confirmation of Booking and Tracking 36314.5 Result and Discussion 36314.5.1 How It Works? 36514.6 Conclusion 36514.7 Future Scope 366References 36615 An Efficient Elderly Disease Prediction and Privacy Preservation Using Internet of Things 369Resmi G. Nair and N. Kumar15.1 Introduction 37015.2 Literature Survey 37115.3 Problem Statement 37215.4 Proposed Methodology 37315.4.1 Design a Smart Wearable Device 37315.4.2 Normalization 37415.4.3 Feature Extraction 37715.4.4 Classification 37815.4.5 Polynomial HMAC Algorithm 37915.5 Result and Discussion 38215.5.1 Accuracy 38215.5.2 Positive Predictive Value 38215.5.3 Sensitivity 38315.5.4 Specificity 38315.5.5 False Out 38315.5.6 False Discovery Rate 38315.5.7 Miss Rate 38315.5.8 F-Score 38315.6 Conclusion 390References 390Index 393