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 |  موضوع: كتاب Integration of Mechanical and Manufacturing Engineering with IoT - A Digital Transformation الجمعة 16 يونيو 2023, 8:51 am | |
| 
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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