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Introduction to Industrial Internet of Things and Industry 4.0 [Mīkstie vāki]

, , (IIT Kharagput, INDIA)
  • Formāts: Paperback / softback, 370 pages, height x width: 234x156 mm, weight: 453 g, 180 Illustrations, black and white
  • Izdošanas datums: 15-Dec-2020
  • Izdevniecība: CRC Press
  • ISBN-10: 036789758X
  • ISBN-13: 9780367897581
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Introduction to Industrial Internet of Things and Industry 4.0Palielināt
  • Formāts: Paperback / softback, 370 pages, height x width: 234x156 mm, weight: 453 g, 180 Illustrations, black and white
  • Izdošanas datums: 15-Dec-2020
  • Izdevniecība: CRC Press
  • ISBN-10: 036789758X
  • ISBN-13: 9780367897581
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367897581.html
Keywords:
Industrial Internet of Things (IIoT) the application of Internet of Things in various industries such as manufacturing, aviation, transportation, supply chain, mining, and healthcare. This book covers the significant aspects of IIoT, including the communication, connectivity, and interoperability, as well as core concepts and business models.

Industrial IoT (IIoT) and Industry 4.0 are newly developing and fast emerging domains of interest among students, researchers, and professionals in academia and industry. Due to the popular demand of this topic, Introduction to Industrial Internet of Things and Industry 4.0 is written to serve a diverse readership from the domains of computer science and engineering, mechanical engineering, information technology, industrial engineering, electronics engineering, and other related branches of engineering. Based on the lead author’s massive open online courses (MOOCs), this book can be used as a textbook on the emerging paradigm of Industry 4.0 and IIoT, as well as a reference for professionals working in sectors of IIoT.

The book covers the significant aspects of IIoT in detail, including sensors, actuators, data transmission, and data acquisition, which form the core of IIoT. Topics and concepts are presented in a comprehensive manner, so that readers can develop expertise and knowledge. The book helps beginners to gain a basic idea of Industry 4.0 and IIoT as the first section is an overview of IoT applications, infrastructure-based protocols, cloud computing, and fog computing. The second section is designed to impart a basic knowledge of Industry 4.0 and IIoT as well as of the different phases of development in industry. Delving into more advanced areas, other sections in the book cover:

  • The business models and reference architecture of IIoT
  • The technological aspects of Industry 4.0 and IIoT
  • Predictive and prescriptive analytics applied in IIoT-based implementations
  • Applications and case studies of IIoT
  • Key enabling technologies of IIoT

To aid students and professional master IIoT and Industry 4.0, the book includes conceptual questions, exercises, and learning objectives.

Foreword xv
Author Biographies xix
Preface xxiii
1 Overview of Internet of Things
3(24)
1.1 Learning Outcomes
3(1)
1.2 Introduction
3(1)
1.3 IoT Architecture
4(2)
1.4 Application-based IoT Protocols
6(3)
1.4.1 Infrastructure-based protocols
7(1)
1.4.2 Data protocols
8(1)
1.4.3 Transport protocols
9(1)
1.5 Cloud Computing
9(8)
1.5.1 Types of cloud
12(1)
1.5.2 Business aspects of cloud
13(1)
1.5.3 Virtualization: Key aspect of cloud computing
14(1)
1.5.4 Mobile cloud computing
15(2)
1.6 Fog Computing
17(2)
1.6.1 Applications of Fog computing
18(1)
1.7 Sensor Cloud
19(2)
1.7.1 Applications of Sensor Cloud
20(1)
1.8 Big Data
21(6)
2 Overview of Industry 4.0 and Industrial Internet of Things
27(24)
2.1 Introduction
27(2)
2.2 Industry 4.0
29(10)
2.2.1 Industrial revolution: Phases of development
30(2)
2.2.2 Evolution of Industry 4.0
32(1)
2.2.3 Environmental impacts of industrial revolution
33(2)
2.2.4 Industrial Internet
35(3)
2.2.5 Applications of Industry 4.0
38(1)
2.3 IIoT
39(12)
2.3.1 Prerequisites of IIoT
40(2)
2.3.2 Basics of CPS
42(2)
2.3.3 CPS and IIoT
44(1)
2.3.4 Applications of IIoT
45(6)
3 Industry 4.0: Basics
51(24)
3.1 Introduction
51(3)
3.1.1 Historical context
52(1)
3.1.2 Significant changes in the industry
52(2)
3.2 Design requirements of Industry 4.0
54(1)
3.3 Drivers of Industry 4.0
55(3)
3.3.1 Megatrends
55(2)
3.3.2 Tipping points
57(1)
3.4 Sustainability Assessment of industries
58(4)
3.4.1 Globalization effects
58(2)
3.4.2 Socio-economic effects
60(2)
3.5 Smart Business Perspective
62(3)
3.5.1 Characteristics of smart business model
64(1)
3.6 Cybersecurity
65(5)
3.6.1 Various cybersecurity threats
68(1)
3.6.2 Requirements of cybersecurity
69(1)
3.7 Impacts of Industry 4.0
70(5)
3.7.1 Economy perspective
70(1)
3.7.2 Business perspective
70(1)
3.7.3 Global perspective
71(4)
4 Industrial Internet of Things: Basics
75(24)
4.1 Introduction
75(5)
4.1.1 IIoT and Industry 4.0
77(1)
4.1.2 IIC
77(3)
4.2 Industrial Internet Systems
80(5)
4.2.1 Design of industrial internet systems
80(2)
4.2.2 Impact of industrial internet
82(2)
4.2.3 Benefits of industrial internet
84(1)
4.3 Industrial sensing
85(3)
4.3.1 Traditional sensing
85(1)
4.3.2 Contemporary sensing
85(3)
4.4 Industrial Processes
88(11)
4.4.1 Features of IIoT for industrial processes
89(1)
4.4.2 Industrial plant-The future architecture
89(3)
4.4.3 Viewpoint of industrial processes
92(1)
4.4.4 Digital Enterprise
93(1)
4.4.5 Applications of Industry 4.0
94(5)
5 Business Models and Reference Architecture of IIoT
99(30)
5.1 Introduction
99(1)
5.1.1 Business models
100(1)
5.2 Definition of a business model
100(6)
5.2.1 Reference architecture
105(1)
5.3 Business Models of IoT
106(2)
5.4 Business models of IIoT
108(3)
5.4.1 Business opportunities in IIoT
109(1)
5.4.2 Categorization of business models in IIoT
110(1)
5.5 Reference architecture of IoT
111(2)
5.6 Reference Architecture of IIoT
113(4)
5.6.1 Categorization of reference architecture in IIoT
114(3)
5.7 IIRA
117(6)
5.7.1 IIRA framework: Basics
118(1)
5.7.2 Categorization of IIRA frameworks
119(4)
5.8 Key Performance Indicators for Occupational Safety and Health
123(6)
6 Key Technologies: Off-site Technologies
129(20)
6.1 Introduction
129(1)
6.2 Cloud Computing
130(11)
6.2.1 Necessity of cloud computing
130(2)
6.2.2 Cloud computing and IIoT
132(2)
6.2.3 Industrial cloud platform providers
134(4)
6.2.4 SLA for IIoT
138(2)
6.2.5 Requirements of Industry 4.0 and its solution
140(1)
6.3 Fog Computing
141(8)
6.3.1 Fog computing for IIoT
142(1)
6.3.2 Applications of fog and their solutions
143(6)
7 Key Technologies: On-site Technologies
149(32)
7.1 Introduction
149(2)
7.1.1 Need for Industry 4.0
150(1)
7.1.2 Transformations required
150(1)
7.2 Augmented Reality
151(5)
7.2.1 History of AR
152(1)
7.2.2 Categorization of AR
153(1)
7.2.3 Applications of AR
154(2)
7.3 Virtual Reality
156(3)
7.3.1 History of VR
157(1)
7.3.2 Categorization of VR
158(1)
7.3.3 Applications of VR
158(1)
7.4 Big Data and Advanced Analytics
159(8)
7.4.1 Characteristics of big data
161(2)
7.4.2 Big data sources
163(1)
7.4.3 Big data acquisition and storage
163(2)
7.4.4 Necessity of data analytics
165(1)
7.4.5 Types of analytics
166(1)
7.5 Smart factories
167(4)
7.5.1 Characteristics of smart factory
169(1)
7.5.2 Technologies used in smart factories
170(1)
7.6 Lean manufacturing system
171(10)
7.6.1 Value streams in lean production system
173(1)
7.6.2 Necessity of lean production system
174(1)
7.6.3 Implementation of lean manufacturing system
175(6)
8 Sensors
181(12)
8.1 Introduction to Sensors
181(1)
8.2 Characteristics
182(4)
8.2.1 Sensor calibration
183(1)
8.2.2 Sensor profile
183(1)
8.2.3 Sensor accuracy
184(1)
8.2.4 Sensor resolution
184(1)
8.2.5 Sensor rating
184(1)
8.2.6 Operating voltage
185(1)
8.2.7 Output
185(1)
8.3 Sensor Categories
186(7)
8.3.1 Thermal sensors
186(1)
8.3.2 Mechanical sensors
187(1)
8.3.3 Electrical sensors
188(1)
8.3.4 Chemical sensors
189(1)
8.3.5 Optical sensors
190(1)
8.3.6 Acoustic sensors
190(3)
9 Actuators
193(8)
9.1 Introduction
193(1)
9.2 Thermal Actuators
194(2)
9.2.1 Takeaways
195(1)
9.3 Hydraulic Actuators
196(1)
9.3.1 Takeaways
196(1)
9.4 Pneumatic Actuators
197(1)
9.4.1 Takeaways
198(1)
9.5 Electromechanical Actuators
198(3)
9.5.1 Takeaways
199(2)
10 Industrial Data Transmission
201(30)
10.1 Introduction
201(1)
10.2 Foundation Fieldbus
202(3)
10.2.1 Features
202(2)
10.2.2 Components
204(1)
10.3 Profibus
205(2)
10.3.1 Features
205(1)
10.3.2 Components
206(1)
10.4 HART
207(1)
10.4.1 Features
207(1)
10.4.2 Components
208(1)
10.5 Interbus
208(2)
10.5.1 Features
209(1)
10.5.2 Components
209(1)
10.6 Bitbus
210(2)
10.6.1 Features
211(1)
10.6.2 Components
211(1)
10.7 CC-Link
212(2)
10.7.1 Features
212(2)
10.7.2 Components
214(1)
10.8 Modbus
214(1)
10.8.1 Features
214(1)
10.8.2 Components
214(1)
10.9 Batibus
215(2)
10.9.1 Features
215(1)
10.9.2 Components
216(1)
10.10 DigitalSTROM
217(1)
10.10.1 Features
217(1)
10.10.2 Components
217(1)
10.11 Controller Area Network
218(2)
10.11.1 Features
218(1)
10.11.2 Components
219(1)
10.12 DeviceNet
220(1)
10.12.1 Features
220(1)
10.12.2 Components
221(1)
10.13 LonWorks
221(2)
10.13.1 Features
221(1)
10.13.2 Components
222(1)
10.14 ISA 100.11a
223(1)
10.14.1 Features
223(1)
10.14.2 Components
224(1)
10.15 Wireless HART
224(2)
10.15.1 Features
225(1)
10.15.2 Components
226(1)
10.16 LoRa and LoRaWAN
226(1)
10.16.1 Features
227(1)
10.16.2 Components
227(1)
10.17 Recent and Upcoming Technologies
227(4)
10.17.1 NB-IoT
228(1)
10.17.2 IEEE 802.11AH
228(3)
11 Industrial Data Acquisition
231(10)
11.1 Introduction
231(1)
11.2 Distributed Control System
232(1)
11.2.1 Components
232(1)
11.3 PLC
233(2)
11.3.1 Components
234(1)
11.4 SCADA
235(6)
11.4.1 Components
235(6)
12 Introduction to IIoT Analytics
241(12)
12.1 Introduction
241(2)
12.1.1 Necessity of analytics
242(1)
12.2 IIoT Analytics
243(10)
12.2.1 Categorization of analytics: IIoT and Industry 4.0 context
243(1)
12.2.2 Usefulness of IIoT analytics
244(1)
12.2.3 Challenges of analytics in industries
245(1)
12.2.4 Mapping of analytics with the IIRA architecture
246(2)
12.2.5 Deployment of analytics
248(1)
12.2.6 Artificial intelligence
249(1)
12.2.7 Applications of analytics across value chain
250(3)
13 Machine Learning and Data Science in Industries
253(26)
13.1 Introduction
253(1)
13.2 Machine Learning
254(2)
13.3 Categorization of ML
256(6)
13.4 Applications of ML in Industries
262(2)
13.5 Data Science in Industries
264(2)
13.6 Deep Learning
266(2)
13.7 Application of Deep Learning in Industries
268(11)
14 Healthcare Applications in Industries
279(12)
14.1 Introduction
279(2)
14.1.1 Major challenges associated with healthcare
280(1)
14.1.2 Coping with increase in diseases
281(1)
14.2 Applications of Healthcare in Industries
281(10)
14.2.1 Smart devices
284(3)
14.2.2 Advanced technologies used in healthcare
287(1)
14.2.3 Open research issues to be addressed
288(3)
15 Inventory Management and Quality Control
291(12)
15.1 Introduction
291(1)
15.2 Inventory Management
292(4)
15.2.1 Inventory
293(2)
15.2.2 Types of inventory management
295(1)
15.3 Inventory Management and IIoT
296(3)
15.3.1 Benefits of IIoT applications in inventory management
298(1)
15.4 Quality Control
299(4)
16 Plant Safety and Security
303(12)
16.1 Introduction
303(1)
16.2 Plant Safety
304(3)
16.2.1 IIoT applications for undertaking safety measures in plant
306(1)
16.3 Plant Security
307(8)
16.3.1 Software security
308(2)
16.3.2 Network security
310(1)
16.3.3 Mobile device security
311(4)
17 Case Studies
315(6)
17.1 Introduction
315(1)
17.2 Manufacturing Industry
316(1)
17.2.1 Background of the industry
316(1)
17.2.2 Challenges
316(1)
17.2.3 Industrial IoT as a solution
316(1)
17.2.4 Benefits
316(1)
17.3 Automotive Industry
317(1)
17.3.1 Background of the industry
317(1)
17.3.2 Challenges
317(1)
17.3.3 Industrial IoT as a solution
317(1)
17.3.4 Benefits
318(1)
17.4 Mining Industry
318(3)
17.4.1 Background of the industry
318(1)
17.4.2 Challenges
318(1)
17.4.3 Industrial IoT as a solution
318(1)
17.4.4 Benefits
319(2)
18 Test Your Understanding
321(14)
References 335(16)
Index 351
Sudip Misra is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur. He is also the Abdul Kalam Technology Innovation National Fellow. He earned a Ph.D. in Computer Science at Carleton University, Ottawa, Canada. His current research interests include algorithm design for emerging communication networks. He is the author of over 300 scholarly research papers, more than 120 of which were published by reputable organizations such as IEEE and ACM. He is elected as Fellow of the National Academy of Science India (NASI). He is also the IEEE Communications Society Distinguished Lecturer for 2020-21.



Chandana Roy is a senior researcher in the Department of Industrial and Systems Engineering at the Indian Institute of Technology, Kharagpur, India (IIT Kharagpur). Her research interests include Internet of Things (IoT), Industrial Internet of Things (IIoT), Sensor Cloud, and Wireless Body Area Network (WBAN). She co-led the planning, organization and delivery of the massively popular NPTEL MOOC course on Industrial Internet of Things and Industry 4.0. She is an active member of the Smart Wireless Applications and Networking (SWAN) Lab at IIT Kharagpur. Additionally, she worked as an organizing committee member in various short-term courses and workshops organized at IIT Kharagpur. Chandana received fellowships from the Ministry of Human Resources and Development (MHRD), India, and the Defence Research and Development Organization (DRDO), India. She was earlier an Assistant Professor in the Department of Electrical Engineering at Aryabhatta Institute of Engineering and Management (AIEMD), Durgapur. Chandanas works are published in reputed conferences such as IEEE ICC,WCNC, and GLOBECOM, and journals. She serves as a peer reviewer in reputed IEEE, Wiley, and Elsevier journals/transactions.



Anandarup Mukherjee is a senior researcher in the Department of Computer Science and Engineering at Indian Institute of Technology, Kharagpur (IIT Kharagpur). He concurrently serves as the Founder and Director of Sensordrops Networks Pvt. Ltd., a govt. recognized startup incubated at the Science and Technology Entrepreneurs Park at IIT Kharagpur. His research interests include, but are not limited to, networked robots, unmanned aerial vehicle swarms and enabling deep learning for these platforms for controls and communications. These solutions are mainly aimed at intelligent and autonomous control and coordination between ground stations and unmanned aerial vehicles (UAVs) as well as between multiple UAVs in flight.