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.
1. Cover image - p. page_i 2. Title page - p. page_i 3. Table of Contents - p. page_i 4. Copyright - p. page_iv 5. Preface - p. page_xi 6. Chapter One: Stepping into the digitally instrumented and interconnected era - p. page_xv 7. Chapter Two: Digital twin technology for “smart manufacturing” - p. page_34 8. Chapter Three: Using fog computing/edge computing to leverage Digital Twin - p. page_49 9. Chapter Four: The industry use cases for the Digital Twin idea - p. page_77 10. Chapter Five: Digital twin: Empowering edge devices to be intelligent - p. page_105 11. Chapter Six: Industry 4.0: Industrial Internet of Things (IIOT) - p. page_127 12. Chapter Seven: The growing role of integrated and insightful big and real-time data analytics platforms - p. page_164 13. Chapter Eight: Air pollution control model using machine learning and IoT techniques - p. page_186 14. Chapter Nine: The human body: A digital twin of the cyber physical systems - p. page_218 15. Chapter Ten: Impact of cloud security in digital twin - p. page_246 16. Chapter Eleven: Digital twin in consumer choice modeling - p. page_263 17. Chapter Twelve: Digital twin: The industry use cases - p. page_284 18. Chapter Thirteen: Machine learning and deep learning algorithms on the Industrial Internet of Things (IIoT) - p. page_320 19. Chapter Fourteen: Energy-efficient edge based real-time healthcare support system - p. page_338
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.
1. Cover image - p. page_i 2. Title page - p. page_i 3. Table of Contents - p. page_i 4. Copyright - p. page_iv 5. Preface - p. page_xi 6. Chapter One: Stepping into the digitally instrumented and interconnected era - p. page_xv 7. Chapter Two: Digital twin technology for “smart manufacturing” - p. page_34 8. Chapter Three: Using fog computing/edge computing to leverage Digital Twin - p. page_49 9. Chapter Four: The industry use cases for the Digital Twin idea - p. page_77 10. Chapter Five: Digital twin: Empowering edge devices to be intelligent - p. page_105 11. Chapter Six: Industry 4.0: Industrial Internet of Things (IIOT) - p. page_127 12. Chapter Seven: The growing role of integrated and insightful big and real-time data analytics platforms - p. page_164 13. Chapter Eight: Air pollution control model using machine learning and IoT techniques - p. page_186 14. Chapter Nine: The human body: A digital twin of the cyber physical systems - p. page_218 15. Chapter Ten: Impact of cloud security in digital twin - p. page_246 16. Chapter Eleven: Digital twin in consumer choice modeling - p. page_263 17. Chapter Twelve: Digital twin: The industry use cases - p. page_284 18. Chapter Thirteen: Machine learning and deep learning algorithms on the Industrial Internet of Things (IIoT) - p. page_320 19. Chapter Fourteen: Energy-efficient edge based real-time healthcare support system - p. page_338
