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

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

1st Edition - January 28, 2020

Write a review

  • Editors: Pethuru Raj, Preetha Evangeline
  • eBook ISBN: 9780128187579
  • Hardcover ISBN: 9780128187562

Purchase options

Purchase options
DRM-free (PDF, Mobi, EPub)
Available
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

Description

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.

Key Features

  • Provides in-depth descriptions of digital transformation technologies and tools
  • Covers various research accomplishments in this flourishing field of relevance
  • Includes many detailed industry use cases with all the right information

Readership

Data scientists, artificial intelligence (AI) researchers, big data analytics (BDA) architects, IoT experts, IT professionals

Table of Contents

  • 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

Product details

  • No. of pages: 384
  • Language: English
  • Copyright: © Academic Press 2020
  • Published: January 28, 2020
  • Imprint: Academic Press
  • eBook ISBN: 9780128187579
  • Hardcover ISBN: 9780128187562

About the Serial Volume Editors

Pethuru Raj

Pethuru Raj
Pethuru Raj PhD has been working as a 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

Chief Architect and Vice President, Site Reliability Engineering (SRE) Division Reliance Jio Infocomm. Ltd. (RJIL), Bangalore, India

Preetha Evangeline

Preetha Evangeline
Dr. Preetha Evangeline is currently working as an Assistant Professor (Senior) in the Department of Computer science and Engineering at Vellore Institute of Technology, Vellore. She has completed her Doctoral Degree from MIT, Anna University in the field of Cloud Computing and her Masters in Engineering with specialization in CSE from Karunya Institute of Technology and Sciences. Her area of specialization are High Performance Computing, Operating Systems and Data Structures. Her future research scope lies in the area of Process Digital Twin Technology. She has published many research papers in reputed journals.

Affiliations and Expertise

Assistant Professor (Senior) in the Department of Computer science and Engineering at Vellore Institute of Technology, Vellore.

Ratings and Reviews

Write a review

There are currently no reviews for "The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases"