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| موضوع: كتاب Integration of Mechanical and Manufacturing Engineering with IoT - A Digital Transformation الجمعة 16 يونيو 2023, 8:51 am | |
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أخواني في الله أحضرت لكم كتاب Integration of Mechanical and Manufacturing Engineering with IoT - A Digital Transformation Edited by R. Rajasekar C. Moganapriya P. Sathish Kumar and M. Harikrishna Kumar
و المحتوى كما يلي :
Contents Preface xvii 1 Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems 1 Vaishnavi Vadivelu, Moganapriya Chinnasamy, Manivannan Rajendran, Hari Chandrasekaran and Rajasekar Rathanasamy 1.1 Introduction 2 1.2 IoT Revolution 2 1.3 IoT 4 1.4 Fundamental Technologies 5 1.4.1 RFID and NFC 5 1.4.2 WSN 6 1.4.3 Data Storage and Analytics (DSA) 6 1.5 IoT Architecture 6 1.6 Cloud Computing (CC) and IoT 7 1.6.1 Service of CC 8 1.6.2 Integration of IoT With CC 10 1.7 Edge Computing (EC) and IoT 10 1.7.1 EC with IoT Architecture 11 1.8 Applications of IoT 12 1.8.1 Smart Mobility 12 1.8.2 Smart Grid 14 1.8.3 Smart Home System 14 1.8.4 Public Safety and Environment Monitoring 15 1.8.5 Smart Healthcare Systems 15 1.8.6 Smart Agriculture System 16 1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems 17viii Contents 1.9.1 Five-Stage Process of IoT for Design and Manufacturing System 19 1.9.2 IoT Architecture for Advanced Manufacturing Technologies 21 1.9.3 Architecture Development 22 1.10 Current Issues and Challenges in IoT 24 1.10.1 Scalability 25 1.10.2 Issue of Trust 25 1.10.3 Service Availability 26 1.10.4 Security Challenges 26 1.10.5 Mobility Issues 27 1.10.6 Architecture for IoT 27 1.11 Conclusion 28 References 29 2 Fourth Industrial Revolution: Industry 4.0 41 Maheswari Rajamanickam, Elizabeth Nirmala John Gerard Royan, Gowtham Ramaswamy, Manivannan Rajendran and Vaishnavi Vadivelu 2.1 Introduction 42 2.1.1 Global Level Adaption 42 2.2 Evolution of Industry 44 2.2.1 Industry 1.0 44 2.2.2 Industry 2.0 44 2.2.3 Industry 3.0 44 2.2.4 Industry 4.0 (or) I4.0 44 2.3 Basic IoT Concepts and the Term Glossary 45 2.4 Industrial Revolution 47 2.4.1 I4.0 Core Idea 47 2.4.2 Origin of I4.0 Concept 48 2.5 Industry 49 2.5.1 Manufacturing Phases 49 2.5.2 Existing Process Planning vs. I4.0 50 2.5.3 Software for Product Planning—A Link Between Smart Products and the Main System ERP 52 2.6 Industry Production System 4.0 (Smart Factory) 56 2.6.1 IT Support 58 2.7 I4.0 in Functional Field 60 2.7.1 I4.0 Logistics 60 2.7.2 Resource Planning 60Contents ix 2.7.3 Systems for Warehouse Management 61 2.7.4 Transportation Management Systems 61 2.7.5 Transportation Systems with Intelligence 63 2.7.6 Information Security 64 2.8 Existing Technology in I4.0 65 2.8.1 Applications of I4.0 in Existing Industries 65 2.8.2 Additive Manufacturing (AM) 66 2.8.3 Intelligent Machines 66 2.8.4 Robots that are Self-Aware 66 2.8.5 Materials that are Smart 67 2.8.6 IoT 67 2.8.7 The Internet of Things in Industry (IIoT) 67 2.8.8 Sensors that are Smart 67 2.8.9 System Using a Smart Programmable Logic Controller (PLC) 67 2.8.10 Software 68 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) 68 2.8.12 Gateway for the Internet of Things 68 2.8.13 Cloud 68 2.8.14 Applications of Additive Manufacturing in I4.0 68 2.8.15 Artificial Intelligence (AI) 69 2.9 Applications in Current Industries 69 2.9.1 I4.0 in Logistics 69 2.9.2 I4.0 in Manufacturing Operation 70 2.10 Future Scope of Research 73 2.10.1 Theoretical Framework of I4.0 73 2.11 Discussion and Implications 75 2.11.1 Hosting: Microsoft 75 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens 76 2.11.3 A Systematic Computational Analysis 76 2.11.4 Festo Proximity Sensor 77 2.11.5 Connectivity Hardware: HMS 77 2.11.6 IT Security: Claroty 77 2.11.7 Accenture Is a Systems Integrator 77 2.11.8 Additive Manufacturing: General Electric 78 2.11.9 Augmented and Virtual Reality: Upskill 78 2.11.10ABB Collaborative Robots 78 2.11.11Connected Vision System: Cognex 78 2.11.12Drones/UAVs: PINC 79 2.11.13Self-Driving in Vehicles: Clear Path Robotics 79x Contents 2.12 Conclusion 79 References 80 3 Interaction of Internet of Things and Sensors for Machining 85 Manivannan Rajendran, Kamesh Nagarajan, Vaishnavi Vadivelu, Harikrishna Kumar Mohankumar and Sathish Kumar Palaniappan 3.1 Introduction 86 3.2 Various Sensors Involved in Machining Process 88 3.2.1 Direct Method Sensors 89 3.2.2 Indirect Method Sensors 89 3.2.3 Dynamometer 90 3.2.4 Accelerometer 91 3.2.5 Acoustic Emission Sensor 93 3.2.6 Current Sensors 94 3.3 Other Sensors 94 3.3.1 Temperature Sensors 94 3.3.2 Optical Sensors 95 3.4 Interaction of Sensors During Machining Operation 96 3.4.1 Milling Machining 96 3.4.2 Turning Machining 97 3.4.3 Drilling Machining Operation 98 3.5 Sensor Fusion Technique 99 3.6 Interaction of Internet of Things 100 3.6.1 Identification 100 3.6.2 Sensing 101 3.6.3 Communication 101 3.6.4 Computation 101 3.6.5 Services 101 3.6.6 Semantics 101 3.7 IoT Technologies in Manufacturing Process 102 3.7.1 IoT Challenges 102 3.7.2 IoT-Based Energy Monitoring System 102 3.8 Industrial Application 104 3.8.1 Integrated Structure 104 3.8.2 Monitoring the System Related to Service Based on Internet of Things 106 3.9 Decision Making Methods 107 3.9.1 Artificial Neural Network 107 3.9.2 Fuzzy Inference System 108 3.9.3 Support Vector Mechanism 108Contents xi 3.9.4 Decision Trees and Random Forest 109 3.9.5 Convolutional Neural Network 109 3.10 Conclusion 111 References 111 4 Application of Internet of Things (IoT) in the Automotive Industry 115 Solomon Jenoris Muthiya, Shridhar Anaimuthu, Joshuva Arockia Dhanraj, Nandakumar Selvaraju, Gutha Manikanta and C. Dineshkumar 4.1 Introduction 116 4.2 Need For IoT in Automobile Field 118 4.3 Fault Diagnosis in Automobile 119 4.4 Automobile Security and Surveillance System in IoT-Based 123 4.5 A Vehicle Communications 125 4.6 The Smart Vehicle 126 4.7 Connected Vehicles 128 4.7.1 Vehicle-to-Vehicle (V2V) Communications 130 4.7.2 Vehicle-to-Infrastructure (V2I) Communications 131 4.7.3 Vehicle-to-Pedestrian (V2P) Communications 132 4.7.4 Vehicle to Network (V2N) Communication 133 4.7.5 Vehicle to Cloud (V2C) Communication 134 4.7.6 Vehicle to Device (V2D) Communication 134 4.7.7 Vehicle to Grid (V2G) Communications 135 4.8 Conclusion 135 References 136 5 IoT for Food and Beverage Manufacturing 141 Manju Sri Anbupalani, Gobinath Velu Kaliyannan and Santhosh Sivaraj 5.1 Introduction 142 5.2 The Influence of IoT in a Food Industry 143 5.2.1 Management 143 5.2.2 Workers 143 5.2.3 Data 143 5.2.4 IT 143 5.3 A Brief Review of IoT’s Involvement in the Food Industry 144 5.4 Challenges to the Food Industry and Role of IoT 144 5.4.1 Handling and Sorting Complex Data 144 5.4.2 A Retiring Skilled Workforce 145 5.4.3 Alternatives for Supply Chain Management 145 5.4.4 Implementation of IoT in Food and Beverage Manufacturing 145xii Contents 5.4.5 Pilot 145 5.4.6 Plan 146 5.4.7 Proliferate 146 5.5 Applications of IoT in a Food Industry 146 5.5.1 IoT for Handling of Raw Material and Inventory Control 146 5.5.2 Factory Operations and Machine Conditions Using IoT 146 5.5.3 Quality Control With the IoT 147 5.5.4 IoT for Safety 147 5.5.5 The Internet of Things and Sustainability 147 5.5.6 IoT for Product Delivery and Packaging 147 5.5.7 IoT for Vehicle Optimization 147 5.5.8 IoT-Based Water Monitoring Architecture in the Food and Beverage Industry 148 5.6 A FW Tracking System Methodology Based on IoT 150 5.7 Designing an IoT-Based Digital FW Monitoring and Tracking System 150 5.8 The Internet of Things (IoT) Architecture for a Digitized Food Waste System 152 5.9 Hardware Design: Intelligent Scale 152 5.10 Software Design 153 References 157 6 Opportunities: Machine Learning for Industrial IoT Applications 159 Poongodi C., Sayeekumar M., Meenakshi C. and Hari Prasath K. 6.1 Introduction 160 6.2 I-IoT Applications 163 6.3 Machine Learning Algorithms for Industrial IoT 170 6.3.1 Supervised Learning 171 6.3.2 Semisupervised Learning 173 6.3.3 Unsupervised Learning 173 6.3.4 Reinforcement Learning 175 6.3.5 The Most Common and Popular Machine Learning Algorithms 176 6.4 I-IoT Data Analytics 177 6.4.1 Tools for IoT Analytics 177 6.4.2 Choosing the Right IoT Data Analytics Platforms 184 6.5 Conclusion 185 References 186Contents xiii 7 Role of IoT in Industry Predictive Maintenance 191 Gobinath Velu Kaliyannan, Manju Sri Anbupalani, Suganeswaran Kandasamy, Santhosh Sivaraj and Raja Gunasekaran 7.1 Introduction 192 7.2 Predictive Maintenance 194 7.3 IPdM Systems Framework and Few Key Methodologies 196 7.3.1 Detection and Collection of Data 196 7.3.2 Initial Processing of Collected Data 196 7.3.3 Modeling as Per Requirement 197 7.3.4 Influential Parameters 198 7.3.5 Identification of Best Working Path 198 7.3.6 Modifying Output With Respect Sensed Input 198 7.4 Economics of PdM 198 7.5 PdM for Production and Product 200 7.6 Implementation of IPdM 202 7.6.1 Manufacturing with Zero Defects 202 7.6.2 Sense of the Windsene INDSENSE 202 7.7 Case Studies 202 7.7.1 Area 1—Heavy Ash Evacuation 203 7.7.2 Area 2—Seawater Pumps 203 7.7.3 Evaporators 204 7.7.4 System Deployment Considerations in General 205 7.8 Automotive Industry—Integrated IoT 205 7.8.1 Navigation Aspect 205 7.8.2 Continual Working of Toll Booth 206 7.8.3 Theft Security System 206 7.8.4 Black Box–Enabled IoT 206 7.8.5 Regularizing Motion of Emergency Vehicle 207 7.8.6 Pollution Monitoring System 207 7.8.7 Timely Assessment of Driver’s Condition 207 7.8.8 Vehicle Performance Monitoring 207 7.9 Conclusion 208 References 208 8 Role of IoT in Product Development 215 Bhuvanesh Kumar M., Balaji N. S., Senthil S. M. and Sathiya P. 8.1 Introduction 216 8.1.1 Industry 4.0 217 8.2 Need to Understand the Product Architecture 220 8.3 Product Development Process 222xiv Contents 8.3.1 Criteria to Classify the New Products 223 8.3.2 Product Configuration 224 8.3.3 Challenges in Product Development while Developing IoT Products (Data-Driven Product Development) 225 8.3.4 Role of IoT in Product Development for Industrial Applications 226 8.3.5 Impacts and Future Perspectives of IoT in Product Development 229 8.4 Conclusion 231 References 232 9 Benefits of IoT in Automated Systems 235 Adithya K. and Girimurugan R. 9.1 Introduction 235 9.2 Benefits of Automation 236 9.2.1 Improved Productivity 236 9.2.2 Efficient Operation Management 236 9.2.3 Better Use of Resources 237 9.2.4 Cost-Effective Operation 237 9.2.5 Improved Work Safety 237 9.2.6 Software Bots 237 9.2.7 Enhanced Public Sector Operations 237 9.2.8 Healthcare Benefits 238 9.3 Smart City Automation 238 9.3.1 Smart Agriculture 240 9.3.2 Smart City Services 240 9.3.3 Smart Energy 240 9.3.4 Smart Health 241 9.3.5 Smart Home 241 9.3.6 Smart Industry 242 9.3.7 Smart Infrastructure 242 9.3.8 Smart Transport 242 9.4 Smart Home Automation 243 9.5 Automation in Manufacturing 247 9.5.1 IoT Manufacturing Use Cases 249 9.5.2 Foundation for IoT in Manufacturing 251 9.6 Healthcare Automation 253 9.6.1 IoT in Healthcare Applications 254 9.6.2 Architecture for IoT-Healthcare Applications 257 9.6.3 Challenges and Solutions 258 9.7 Industrial Automation 259Contents xv 9.7.1 IoT in Industrial Automation 260 9.7.2 The Essentials of an Industrial IoT Solution 260 9.7.3 Practical Industrial IoT Examples for Daily Use 261 9.8 Automation in Air Pollution Monitoring 265 9.8.1 Methodology 266 9.8.2 Working Principle 267 9.8.3 Results 267 9.9 Irrigation Automation 268 References 269 10 Integration of IoT in Energy Management 271 Ganesh Angappan, Santhosh Sivaraj, Premkumar Bhuvaneshwaran, Mugilan Thanigachalam, Sarath Sekar and Rajasekar Rathanasamy 10.1 Introduction 272 10.2 Energy Management Integration with IoT in Industry 4.0 274 10.3 IoT in Energy Sector 276 10.3.1 Energy Generation 276 10.3.2 Smart Cities 277 10.3.3 Smart Grid 277 10.3.4 Smart Buildings 278 10.3.5 IoT in the Energy Industry 279 10.3.6 Intelligent Transportation 280 10.4 Provocations in the IoT Applications 281 10.4.1 Energy Consumption 281 10.4.2 Subsystems and IoT Integration 282 10.5 Energy Generation 284 10.5.1 Conversion of Mechanical Energy 285 10.5.2 Aeroelastic Energy Harvesting 290 10.5.3 Solar Energy Harvesting 292 10.5.4 Sound Energy Harvesting 292 10.5.5 Wind Energy Harvesting 292 10.5.6 Radiofrequency Energy Harvesting 293 10.5.7 Thermal Energy 293 10.6 Conclusion 294 References 294 11 Role of IoT in the Renewable Energy Sector 305 Veerakumar Chinnasamy and Honghyun Cho 11.1 Introduction 305 11.2 Internet of Things (IoT) 306xvi Contents 11.3 IoT in the Renewable Energy Sector 307 11.3.1 Automation of Energy Generation 307 11.3.2 Smart Grids 309 11.3.3 IoT Increases the Renewable Energy Use 312 11.3.4 Consumer Contribution 312 11.3.5 Balancing Supply and Demand 313 11.3.6 Smart Buildings 313 11.3.7 Smart Cities 314 11.3.8 Cost-Effectiveness 314 11.4 Data Analytics 314 11.4.1 Data Forecasting 314 11.4.2 Safety and Reliability 315 11.5 Conclusion 315 References 315 Index 317 Index 3D printing, 218, 220 A FW tracking system methodology based on IoT, 150 ABB collaborative robots, 78 Abrasive water jet machining, 109 Accelerometer, 89, 91, 92, 95–97, 99 Accenture, 77, 78 Acoustic emission sensor, 89, 93, 95, 96, 99 Additive manufacturing, 47, 58, 66, 68, 75, 78 Advanced manufacturing, 21, 86 Agent-based computer aided process planning, 20, 51 Analysis of vibration, 24 Android, 223 Apache stream pipes, 182 Application layer, 6, 7, 28 Applications, 42, 60, 63–65, 67–69 Applications of IoT in a food industry, 146 factory operations and machine conditions using IoT, 146 IoT for handling of raw material and inventory control, 146 IoT for product delivery and packaging, 147 IoT for safety, 147 IoT for vehicle optimization, 147 quality control with the IoT, 147 the Internet of Things and sustainability, 147 Architecture, 219–221, 230, 231 Artificial intelligence, 41, 46, 66, 69 Artificial neural network, 176 AT&T IoT platform, 183 Augmented reality (AR), 50, 58, 68 Automation, 216–217, 219–220 Automation in air pollution monitoring, 265–266 methodology, 266–267 results, 267–268 working principle, 267 Automation in manufacturing, 247–249 foundation for IoT in manufacturing, 251–252 IoT manufacturing use cases, 249–251 Automation of energy generation, 307–308 Automation techniques, 42 Automobile security and surveillance system in IoT-based, 123 Automotive industry—integrated IoT, 205 black box–enabled IoT, 206 continual working of toll booth, 206 navigation aspect, 205 pollution monitoring system, 207 regularizing motion of emergency vehicle, 207 theft security system, 206 timely assessment of driver’s condition, 207318 Index vehicle performance monitoring, 207 AWS IoT analytics, 180 Balancing supply and demand, 313 Bella Dati, 183 Benefits of automation, 236 better use of resources, 237 cost-effective operation, 237 efficient operation management, 236 enhanced public sector operations, 237 healthcare benefits, 238 improved productivity, 236 improved work safety, 237 software bots, 237 Big data analysis, 46, 79 Bluetooth, 221 Brief review of IoT’s involvement in the food industry, 144 Case studies, 202 area 1—heavy ash evacuation, 203 area 2—seawater pumps, 203 evaporators, 204 system deployment considerations in general, 205 Challenges, 2, 10, 13, 17 Challenges to the food industry and role of IoT, 144 a retiring skilled workforce, 145 alternatives for supply chain management, 145 handling and sorting complex data, 144 implementation of IoT in food and beverage manufacturing, 145 pilot, 145 plan, 146 proliferate, 146 Challenges, 225–226, 229–232 Claroty, 77 Clear path robotics, 79 Cloud, 217–219, 221–222, 227 Cloud computing, 7, 46, 62, 65 Cognex, 78 Cognitive computing, 7, 41–42, 66 Communication, 217, 219, 221–222, 225, 228, 231–232 Connected vehicles, 128 vehicle to cloud (V2C) communication, 134 vehicle to device (V2D) communication, 134 vehicle to grid (V2G) communication, 135 vehicle to network (V2N) communication, 134 vehicle-to-infrastructure (V2I) communications, 131 vehicle-to-pedestrian (V2P) communications, 132 vehicle-to-vehicle (V2V) communications, 130 Connectivity hardware, 77 Consumer contribution, 312 Cost effectiveness, 314 Costs, development cost, 226 production cost, 223 Current sensor, 89, 94, 95, 106, 107 Customization, 220–221, 225 Cyber-physical systems (CPS), 41–43, 218–219 Data, 217, 219–222, 224–225, 227, 229–232 Data analytics, 177, 314 Data forecasting, 314–315 Data handling technique, 85 Data storage and analytics, 6 Datadog, 183 Decision making methods, artificial neural network, 107, 108 convolutional neural network, 107, 109 decision trees and random forest, 107, 109Index 319 fuzzy inference system, 107, 108 support vector mechanism, 108 Decision trees, 176 Designing an IoT-based digital FW monitoring and tracking system, 150 Digitalization, 218, 220, 221, 230–231 Digitization, 42, 46, 51, 60 Direct method, 88, 89 Drilling, 88, 98, 99, 108 Dynamometer, 89–92, 95–99 Economics of PdM, 198 Ecosystem, 43, 46, 69 Edge computing, 10, 11, 182, 183 Energy generation, 284 aeroelastic energy harvesting, 291 conversion of mechanical energy, 285 radiofrequency energy harvesting, 293 solar energy harvesting, 292 sound energy harvesting, 292 thermal energy, 293 wind energy harvesting, 292 Energy management integration with IoT in industry 4.0, 274 Energy monitoring system, application layer, 104, 106 data acquisition layer, 102 data processing layer, 103, 104 data transmission layer, 103 Enterprise resource planning, 45, 52, 54, 58–59, 80 Environment monitoring, 9, 15 Fault diagnosis in automobile, 119 Festo proximity sensor, 77 Google cloud IoT store, 180 Grinding, 109 Hardware design: intelligent scale, 152 Healthcare automation, 253–254 architecture for IoT-healthcare applications, 257–258 challenges and solutions, 258–259 IoT in healthcare applications, 254–257 Implementation of IPdM, 202 manufacturing with zero defects, 202 sense of the windsene INDSENSE, 202 Indirect method, 88, 89 Industrial application, 104 Industrial automation, 259–260 essentials of an industrial IoT solution, 260–261 IoT in industrial automation, 260 practical industrial IoT examples for daily use, 261–265 Industrial Internet of Things, 46, 67, 161 Industry 1.0, 44 Industry 2.0, 44 Industry 3.0, 44 Industry 4.0, 17, 41–44, 216–221, 274 Information security, 64–65 Infrastructure as a service, 8, 9 Integration of IoT, 10 Intelligent machines, 66 Interaction technique, communication, 86, 100, 101, 106 computation, 88, 100, 101 identification, 100 semantics, 100, 101 sensing, 97, 100, 101, 111 services, 100, 101 Internet of Things (IoT), 41–47, 49, 62, 64–65, 67–68, 76, 79, 85, 100, 106, 160, 306–307 IoT architecture, 6, 7, 11 IoT in energy sector, 276 energy generation, 276 intelligent transportation, 280 IoT in the energy industry, 279320 Index smart buildings, 278 smart cities, 277 smart grid, 277 IoT in renewable energy sector, 307 IoT increases the renewable energy use, 313 IoT-based water monitoring architecture in the food and beverage industry, 148 IPdM systems framework and few key methodologies, 196 detection and collection of data, 196 identification of best working path, 198 influential parameters, 198 initial processing of collected data, 196 modeling as per requirement, 197 modifying output with respect sensed input, 198 Irrigation automation, 268 K means, 176 Linear regression, 176 Logistic regression, 176 Logistics, 54, 60–63, 69 Machine learning, 46, 56, 66, 171 Machining operations, 85, 87, 88, 94, 104, 106, 109, 111 Manufacturing phases, 49 Manufacturing system, 1, 19, 28 Manufacturing systems, 85 Materials, 45, 54, 58, 61, 66, 67, 69, 73 Microsoft, 75–77 Milling, 87, 88, 91, 96, 97, 106–108 Mobility issues, 27 Modern, 218, 221, 228 Monitoring system, 88, 92, 93, 102, 104, 105 Naïve Bayes classifier, 176 Nearfield communication, 2 Need for IoT in automobile field, 118 Network, 217–225, 231 Network layer, 6, 7, 148, 152 Neurofuzzy models, 108 Optical sensor, 89, 95 Oracle Internet of Things cloud, 183 PdM for production and product, 200 Perception layer, 6, 7, 28, 146 Platform as a service, 8, 9 Predictive maintenance, 194 Privacy, 225 Process parameters, 90, 100, 102, 109 Process planning, 50, 52, 79 Product configuration, 224 Product development, concept generation, 216, 222 customer need, 216, 220 new product, 216, 222–223 product design, 216, 221, 222, 228 smart product, 218, 220–221, 231 Product life cycle, 218, 228–229 Product planning, 52, 54–56, 79–80 Product tracking system, 23 Production planning, 221, 225 Production system, 43, 47–48, 55–58, 66 Prototype, 216, 220, 222 Provocations in the IoT applications, 281 energy consumption, 281 subsystems and IoT integration, 282 Public safety, 15, 207, 237 Radiofrequency identification, 2, 116, 148 Random forest, 177 Real-time data processing, 46 Reinforcement, 175Index 321 Resource planning, 45, 60 Revolution, 2, 41 Risk factor, 222 Robotics, 218, 220, 222 Robots, 47, 50, 54, 56–57, 65–66, 69–70, 76, 78–79 Safety and reliability, 315 Scalability, 10, 25 Security, 225 Security challenges, 26, 184 Semisupervised, 173 Sensor fusion technique, 99 Sensors, 45, 50, 56, 61, 62, 67, 70, 85–99, 101–103, 105–107, 111 Service availability, 26 Siemens, 75–77 Sigfox, 221 Smart buildings, 313–314 Smart cities, 314 Smart city automation, 238–240 smart agriculture, 240 smart city services, 240 smart energy, 240-241 smart health, 241 smart home, 241 smart industry, 242 smart infrastructure, 242 smart transport, 242–243 Smart factory, 41–43, 46, 56, 58–59, 219, 232 Smart grids, 2, 12, 13, 309–312 Smart healthcare system, 15 Smart home automation, 243–247 Smart home system, 14, 15, 243 Smart mobility, 12, 13 Smart product, 43, 51–52, 60, 67, 73 Smart programmable logic controller, 67–68 Smart vehicles, 126 Software as a service, 8, 9 Software design, 153 Supervised, 171 Supply chain, 218, 230 Support vector machine, 176 System architecture, 23 Temperature sensor, 89, 94, 106 The influence of IoT in the food industry, 143 data, 143 IT, 143 management, 143 workers, 143 The Internet of Things (IoT) architecture for a digitized food waste system, 151 ThingsBoard, 181 ThingSpeak, 181 Tool wear, 89, 93, 94, 97, 99, 100, 107, 108 Transportation management systems, 61 Transportation systems, 60, 62–63 Trust, 25, 28 Turning, 88, 91, 97, 98, 108 Unsupervised, 173 Upskill, 78 Vehicle communications, 125 Virtual reality (VR), 51, 68, 78 Warehouse management, 60–61 Wireless sensor networks, 2, 9, 16 WSO2 IoT server, 182
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