The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases

1st Edition, Volume 117 - January 28, 2020
Editors: Pethuru Raj, Preetha Evangeline David
Language: English
Hardback ISBN:
9 7 8 - 0 - 1 2 - 8 1 8 7 5 6 - 2
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 8 7 5 7 - 9

The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases, Volume 117, the latest volume in the Advances in Computers series, presents detailed coverag… Read more

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The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases, Volume 117, the latest volume in the Advances in Computers series, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters vividly illustrate how the emerging discipline of digital twin is strategically contributing to various digital transformation initiatives. Specific chapters cover Demystifying the Digital Twin Paradigm, Digital Twin Technology for "Smarter Manufacturing", The Fog Computing/ Edge Computing to leverage Digital Twin, The industry use cases for the Digital Twin idea, Enabling Digital Twin at the Edge, The Industrial Internet of Things (IIOT), and much more.

Cover image
Title page
Table of Contents
Copyright
Preface
Chapter One: Stepping into the digitally instrumented and interconnected era
Abstract
1 Introduction
2 Elucidating digitization technologies
3 Enlisting the recent happenings in the IT space
4 The connectivity and integration options
5 The promising digital intelligence methods
6 Envisioning the digital universe
7 Conclusion
Chapter Two: Digital twin technology for “smart manufacturing”
Abstract
1 Introduction
2 Digital twin, what it is?
3 Integration
4 Digital twin business values
5 Conclusion
Chapter Three: Using fog computing/edge computing to leverage Digital Twin
Abstract
1 Introduction
2 Illustrating the epoch-making IoT journey
3 The use cases of fog/edge computing
4 Fog and edge computing on digital twin
5 Facets of digital twin
6 Collaboration of fog computing
7 Collaboration with edge computing
8 Use cases of digital twin collaborated with fog
9 Benefits
10 Conclusion
Chapter Four: The industry use cases for the Digital Twin idea
Abstract
1 Manufacturing
2 Industrial IoT
3 Healthcare
4 Smart cities
5 Automobile
6 Retail
Chapter Five: Digital twin: Empowering edge devices to be intelligent
Abstract
1 Introduction
2 Transitions in IoT space
3 Data analytics at realtime and @Edge—WHY?
4 Current trend in edge computing
5 Edge computing challenges
6 Why edge cloud analytics?
7 Edge cloud and its capabilities
8 Case study—Edge analytics platform and its capabilities
9 Cook book/user manual on edge analytics platform
10 Conclusion
Chapter Six: Industry 4.0: Industrial Internet of Things (IIOT)
Abstract
1 Introduction
2 Defining Industrial Internet of Things
3 IIoT architectures
4 Applications of IIoT
5 Securing Internet of Things
6 Challenges and opportunities
7 Future of IIoT
8 Conclusion
Chapter Seven: The growing role of integrated and insightful big and real-time data analytics platforms
Abstract
1 Introduction
2 Big data architecture
3 Real-time big data analytics platforms
4 Big data to smart data to digital twin
5 Real time applications
6 Challenges in real time big data applications
7 Conclusions
Chapter Eight: Air pollution control model using machine learning and IoT techniques
Abstract
1 Introduction
2 Literature survey
3 Design
4 Implementation and results
5 Conclusion
Chapter Nine: The human body: A digital twin of the cyber physical systems
Abstract
1 Introduction
2 Cyber physical systems
3 Biological systems
4 The systems of the human body
5 Bio-plausible cyber systems
6 Applications of digital twin
7 Conclusion
Chapter Ten: Impact of cloud security in digital twin
Abstract
1 Introduction
2 Background study
3 Digital twin
4 Advantages of digital twin
5 Characteristics of digital twin
6 Cloud security techniques
7 Cloud security in digital twin
8 Impact of digital twin in aircraft
9 Use of Skyhigh
10 Applications of digital twin technology
11 Conclusion
Chapter Eleven: Digital twin in consumer choice modeling
Abstract
1 Introduction
2 Big data, machine learning and artificial intelligence in retail
3 Digital twin in retail
4 Drawbacks in traditional retail
5 Technologies to build digital twin
6 Conclusion
Chapter Twelve: Digital twin: The industry use cases
Abstract
1 Introduction
2 A recap of digital twin
3 Digital twin key drivers
4 Digital twins for the intelligent IoT era
5 The levels of digital twin (DT) maturity model
6 Digital twin industry domains
7 Enterprise-scale digital twins
8 Digital twin (DT) industry use cases
9 Digital twin applications
10 Digital twin benefits
11 A digital twin-centric approach for driver-intention prediction and traffic congestion-avoidance
12 The solution architecture description
13 Edge analytics-based virtual vehicle (VV) networks
14 The future
15 Conclusion
Chapter Thirteen: Machine learning and deep learning algorithms on the Industrial Internet of Things (IIoT)
Abstract
1 Introduction
2 IIoT analytics overview
3 Types of data
4 Challenges in IIoT
5 Need of contextual analysis in IIoT
6 Machine learning for contextual analysis
7 Role of analytics in IIoT
8 Naïve Bayes algorithm
9 Support vector machine (SVM)
10 Linear regression
11 Random forest (RF)
12 K-means clustering
13 Principal component analysis (PCA)
14 Canonical correlation analysis (CCA)
15 Neural networks
16 Monitoring
17 Behavioral control
18 Optimization
19 Self-healing
20 Summary
Chapter Fourteen: Energy-efficient edge based real-time healthcare support system
Abstract
1 Introduction
2 Edge computing: Principles and architecture
3 Deep learning
4 Edge computing for health care application
5 Deep learning at the edge: Challenges and benefits
6 Proposed system
7 Cloud-edge paradigm
8 Early exit at inference
9 Conclusion

Pethuru Raj

Pethuru Raj PhD works as chief architect and vice president of site reliability engineering (SRE) division of Reliance Jio Infocomm. Ltd. Bangalore. Previously he worked as a cloud infrastructure architect in the IBM Global Cloud Center of Excellence (CoE), Bangalore. He worked as a TOGAF-certified enterprise architecture (EA) consultant in Wipro Consulting Services (WCS) Division and as a lead architect in the corporate research (CR) division of Robert Bosch, India. He has gained more than 18 years of IT industry experience. He finished the CSIR-sponsored PhD degree in Anna University, Chennai and continued the UGC-sponsored postdoctoral research in the department of Computer Science and Automation, Indian Institute of Science, Bangalore. Thereafter, he was granted a couple of international research fellowships (JSPS and JST) to work as a research scientist for 3.5 years in two leading Japanese universities. He has authored and edited 18 books thus far and he focuses on some of the emerging technologies such as Containerized Clouds; Big, Fast, and Streaming Data Analytics; Microservices architecture (MSA); Machine and Deep Learning Algorithms; Blockchain Technology; The Internet of Things; and Edge Computing. He has published more than 30 research papers in peer-reviewed journals such as IEEE, ACM, Springer-Verlag, Inderscience, etc.

Affiliations and expertise

Reliance Jio Platforms Ltd.. (RJIL), Bangalore, India

Preetha Evangeline David

Dr. Preetha Evangeline David is currently working as an Associate Professor and Head of the Department in the Department of Artificial Intelligence and Machine Learning at Chennai Institute of Technology, Chennai, India. She holds a PhD from Anna University, Chennai in the area of Cloud Computing. She has published many research papers and Patents focusing on Artificial Intelligence, Digital Twin Technology, High Performance Computing, Computational Intelligence and Data Structures. She is currently working on Multi-disciplinary areas in collaboration with other technologies to solve socially relevant challenges and provide solutions to human problems.

Affiliations and expertise

Associate Professor and Head of the Department in the Department of Artificial Intelligence and Machine Learning at Chennai Institute of Technology, Chennai, India.

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