Digital Twins for Smart Cities and Villages
- 1st Edition - October 17, 2024
- Imprint: Elsevier
- Editors: Sailesh Iyer, Anand Nayyar, Anand Paul, Mohd Naved
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 8 8 8 4 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 8 8 8 5 - 2
Digital Twins for Smart Cities and Villages provides a holistic view of digital twin technology and how it can be deployed to develop smart cities and smart villages. Smart manufa… Read more
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Request a sales quoteDigital Twins for Smart Cities and Villages provides a holistic view of digital twin technology and how it can be deployed to develop smart cities and smart villages. Smart manufacturing, smart healthcare, smart education, smart agriculture, smart rural solutions, and related methodologies using digital twins are discussed, including challenges in deployment, their solutions and future roadmaps. This knowledge, enriched by a variety of case studies presented in the book, may empower readers with new capabilities for new research as well as new tasks and strategies for practical implementation and real-world problem solving.
The book is thoughtfully structured, starting from the background of digital twin concepts and basic know-how to serve the needs of those new to the subject. It continues with implementation to facilitate and improve management in several urban contexts, infrastructures, and more. Global case study assessments further provide a deep characterization of the state-of-the-art in digital twin in urban and rural contexts.
The book is thoughtfully structured, starting from the background of digital twin concepts and basic know-how to serve the needs of those new to the subject. It continues with implementation to facilitate and improve management in several urban contexts, infrastructures, and more. Global case study assessments further provide a deep characterization of the state-of-the-art in digital twin in urban and rural contexts.
- Uniquely focuses on applications for smart cities and villages, including smart services for health, education, mobility, and agriculture
- Provides use cases and practical deployment of research involved in the emerging uses of digital twins
- Discusses all pertinent issues, challenges, and possible solutions instrumental in implementing digital twins smart solutions in this context
- Edited and authored by a global team of experts in their given fields
Researchers, academicians, entrepreneurs, industry, architects, urban engineers, governmental institutions, professionals from public administration, healthcare organizations, IT companies, the construction sector
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the editors
- Preface
- Chapter 1. Digital twin technology fundamentals
- 1.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 1.2 Fundamentals of digital twin technology
- 1.2.1 Key components and architecture
- 1.2.1.1 Data models
- 1.2.1.2 Sensors and IoT devices
- 1.2.1.3 Data integration and processing
- 1.2.1.4 Simulation and analytics engine
- 1.2.1.5 User interface and visualization tools
- 1.2.2 Data collection and management
- 1.2.2.1 Data collection mechanisms
- 1.2.2.2 Data integration and standardization
- 1.2.2.3 Data processing and storage
- 1.2.2.4 Data security and privacy
- 1.2.2.5 Data management strategies
- 1.2.3 Real-time simulation and analysis
- 1.2.3.1 Fundamentals of real-time simulation
- 1.2.3.2 Data-driven analysis and predictive modeling
- 1.2.3.3 Integration with IoT and sensor technology
- 1.2.3.4 Challenges in real-time simulation
- 1.2.3.5 Applications and implications
- 1.3 Applications across industries
- 1.3.1 Manufacturing and engineering
- 1.3.1.1 Enhancing product design and development
- 1.3.1.2 Optimizing production processes
- 1.3.1.3 Predictive maintenance and downtime reduction
- 1.3.1.4 Quality control and monitoring
- 1.3.1.5 Customization and personalization
- 1.3.2 Healthcare and biotechnology
- 1.3.2.1 Personalized medicine and patient care
- 1.3.2.2 Surgical planning and simulation
- 1.3.2.3 Medical device design and testing
- 1.3.2.4 Drug development and pharmacological research
- 1.3.2.5 Disease modeling and epidemiology
- 1.3.3 Urban planning and smart cities
- 1.3.3.1 City-wide infrastructure planning and management
- 1.3.3.2 Traffic and transportation optimization
- 1.3.3.3 Environmental monitoring and sustainability
- 1.3.3.4 Disaster preparedness and response
- 1.3.3.5 Enhancing citizen engagement and services
- 1.4 Challenges in implementing digital twins
- 1.4.1 Data integration issues
- 1.4.2 Scalability concerns
- 1.4.3 Security and privacy challenges
- 1.5 Case studies
- 1.5.1 Manufacturing efficiency improvements
- 1.5.2 Healthcare predictive modeling
- 1.5.3 Smart city optimization
- 1.6 Integrating AI and machine learning
- 1.6.1 Enhanced predictive capabilities
- 1.6.2 Autonomous system optimization
- 1.6.3 Data-driven decision-making
- 1.7 Future directions and research opportunities
- 1.7.1 Emerging applications in sustainable energy
- 1.7.2 Advancements in virtual and augmented reality
- 1.7.3 Ethical and regulatory considerations
- 1.8 Conclusion and future scope
- Abbreviations
- Chapter 2. Research advancements in quantum computing digital twins
- 2.1 Introduction to digital twins in smart cities and villages
- 2.1.1 Understanding quantum computing
- Objectives of the chapter
- Organization of chapter
- 2.2 Digital twins in quantum computing
- 2.3 Methodology
- 2.4 State-of-the-Art in quantum computing digital twins
- 2.5 Advancements in quantum simulations and digital twin accuracy
- 2.6 Case studies and practical implementations
- 2.6.1 Quantum digital twins in energy management
- 2.6.2 Quantum computing for traffic and transportation optimization
- 2.6.3 Quantum digital twins in healthcare and public services
- 2.7 Quantum cybersecurity and resilience for digital twins
- 2.8 Quantum digital twins for energy optimization in smart cities
- 2.9 Quantum digital twins for environmental monitoring and sustainability in smart cities
- 2.10 Quantum ethics and governance in smart urban environments
- 2.10.1 Ethical considerations in quantum data usage
- 2.10.2 Quantum governance frameworks
- 2.11 Quantum computing and environmental sustainability in smart cities
- 2.11.1 Precise climate modeling
- 2.11.2 Quantum sensors for environmental monitoring
- 2.11.3 Resource optimization for sustainability
- 2.11.4 Renewable energy optimization
- 2.11.5 Efficient transportation systems
- 2.11.6 Waste management
- 2.11.7 Sustainability assessment
- 2.11.8 Environmental impact assessment
- 2.11.9 Resource allocation models
- 2.11.10 Carbon footprint reduction
- 2.12 Case studies
- 2.13 Challenges and future directions
- 2.14 Conclusion
- Chapter 3. Digital twins tools and technologies
- 3.1 Introduction
- Objectives of the chapter
- Organization of the chapter
- 3.2 Background
- 3.2.1 Evolution of digital twin technology
- 3.2.1.1 Early simulation and modeling (1960–70s)
- 3.2.1.2 NASA's “paired systems” concept (1960s)
- 3.2.1.3 Advancements in computing (1980–90s)
- 3.2.1.4 Internet of Things and sensor technology (1990–2000s)
- 3.2.1.5 Industry 4.0 and digital transformation (2010s)
- 3.2.1.6 Integration of AI and data analytics (2010s–present)
- 3.2.1.7 Expansion to various sectors (2020s–present)
- 3.2.2 The digital leap: From theory to reality
- 3.2.2.1 Advancements in computing power
- 3.2.2.2 Data storage and accessibility
- 3.2.2.3 Cloud computing and edge computing
- 3.2.2.4 Machine learning and AI
- 3.2.2.5 Real-time interactions and visualization
- 3.2.2.6 Convergence of technologies
- 3.3 Digital twin fundamentals
- 3.3.1 Defining digital twin
- 3.3.2 Core components
- 3.3.2.1 Physical entity
- 3.3.2.2 Digital counterpart
- 3.3.2.3 Connectivity
- 3.3.3 Typologies of Digital Twin
- 3.3.3.1 Digital Product Twins
- 3.3.3.2 Digital Process Twins
- 3.3.3.3 Digital health twins
- 3.3.3.4 Digital City Twins (DCTs)
- 3.3.4 Transformative potential: Enabling real-time monitoring, simulation, and optimization
- 3.3.4.1 Real-time monitoring
- 3.3.4.2 Simulation for informed decision-making
- 3.3.4.3 Continuous optimization
- 3.3.4.4 Predictive analytics for optimization
- 3.4 Key technologies enabling digital twin
- 3.4.1 Internet of Things
- 3.4.1.1 Sensor integration
- 3.4.1.2 Edge computing
- 3.4.1.3 Data preprocessing
- 3.4.2 Big data analytics
- 3.4.2.1 Data storage and management
- 3.4.2.2 Data processing
- 3.4.2.3 Machine learning and Artificial Intelligence
- 3.4.3 Cloud computing
- 3.4.3.1 Scalable infrastructure
- 3.4.3.2 Data storage and retrieval
- 3.4.3.3 Data processing and analysis
- 3.4.4 Simulation and modeling
- 3.4.4.1 Physics-based models
- 3.4.4.2 Model integration
- 3.4.5 Augmented reality (AR) and virtual reality (VR)
- 3.4.5.1 3D visualization
- 3.4.5.2 Real-time data overlay
- 3.4.5.3 Interaction and control
- 3.4.6 Edge computing
- 3.4.6.1 Edge devices
- 3.4.6.2 Distributed data processing
- 3.4.7 5G connectivity
- 3.4.7.1 Low latency communication
- 3.4.7.2 Massive device connectivity
- 3.4.8 Blockchain technology
- 3.4.8.1 Distributed ledger
- 3.4.8.2 Smart contracts
- 3.4.8.3 Decentralized consensus
- 3.5 Building and deploying digital twin
- 3.5.1 Data acquisition and integration
- 3.5.1.1 Sensor deployment
- 3.5.1.2 Data collection
- 3.5.1.3 Data transformation
- 3.5.1.4 Integration with external data
- 3.5.2 Model development and calibration
- 3.5.2.1 Physics-based models
- 3.5.2.2 Data-driven models
- 3.5.2.3 Calibration
- 3.5.3 Visualization and user interfaces
- 3.5.3.1 3D representation
- 3.5.3.2 Graphical user interfaces
- 3.5.3.3 Augmented reality or virtual reality
- 3.5.4 Deployment and integration into operations
- 3.5.4.1 Cloud or edge deployment
- 3.5.4.2 Real-time connectivity
- 3.5.4.3 Integration with operational systems
- 3.5.4.4 Continuous monitoring and feedback loop
- 3.6 Applications of digital twins
- 3.7 Challenges and limitations
- 3.8 Future research directions
- 3.8.1 IoT integration and sensor fusion
- 3.8.2 AI-powered predictive analytics
- 3.8.3 Generative design and optimization
- 3.8.4 DT of complex systems
- 3.8.5 Hybrid models and simulation
- 3.8.6 Digital twin ecosystems and collaboration
- 3.8.7 Edge computing and real-time decision-making
- 3.8.8 Ethical and regulatory considerations
- 3.8.9 Quantum computing integration
- 3.8.10 Digital twin ecosystems
- 3.8.11 AI and machine learning integration
- 3.8.12 Generative design and optimization
- 3.8.13 Extended reality (XR) integration
- 3.8.14 Digital twin analytics platforms
- 3.8.15 Edge-to-cloud hybrid implementations
- 3.8.16 Autonomous decision-making and control
- 3.8.17 Ethical and regulatory considerations
- 3.9 Implications and benefits
- 3.10 Conclusion
- Abbreviations
- Chapter 4. Future trends and research challenges in digital twins
- 4.1 Introduction
- 4.2 Development of digital twins
- Objectives of the chapter
- Organization of chapter
- 4.3 AI and ML in digital twins
- 4.4 Federated digital twins
- 4.5 Digital twins into the Internet of Things space
- 4.6 Paradigm-shifting
- 4.6.1 Paradigm-shifting developments characteristics
- 4.7 Future trends
- 4.7.1 Data collection and management
- 4.7.2 Modeling and simulation
- 4.8 Challenges
- 4.8.1 Case study
- 4.8.1.1 Smart manufacturing case study: Digital twins
- 4.9 Conclusion and future scope
- 4.9.1 Future scope
- Chapter 5. Research advancements in quantum computing and digital twins
- 5.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 5.2 Background literature on the merge of quantum computing and digital twins
- 5.2.1 Foundational theories
- 5.2.2 Conceptual model of quantum computing in digital twins
- 5.2.3 Key quantum algorithms
- 5.2.3.1 Shor's algorithm
- 5.2.3.2 Grover's algorithm
- 5.2.3.3 Quantum Fourier transform
- 5.2.3.4 Quantum machine learning algorithms
- 5.2.3.5 Variational quantum algorithms
- 5.2.4 The role of Internet of Things (IoT)
- 5.2.5 Early research efforts
- 5.2.6 Application scenarios
- 5.2.6.1 Climate modeling and environmental monitoring
- 5.2.6.2 Healthcare simulation and personalized medicine
- 5.2.6.3 Manufacturing and supply chain optimization
- 5.2.6.4 Energy sector and smart grids
- 5.2.6.5 Cybersecurity and data protection
- 5.2.6.6 Financial services and risk assessment
- 5.2.7 Limitations and challenges
- 5.3 Methodology
- 5.3.1 Database selection
- 5.3.2 Search strategy
- 5.3.3 Data extraction
- 5.3.4 Inclusion and exclusion criteria
- 5.3.5 Analytical methods
- 5.4 Results and discussion
- 5.4.1 Identification of key literature
- 5.4.2 Citation impact
- 5.4.3 Coauthorship analysis
- 5.4.4 Thematic hotspots
- 5.4.5 Research gaps and future directions
- 5.4.6 Limitations of the current research
- 5.4.7 Real-world implications
- 5.5 Future research avenues
- 5.6 Conclusion and future scope
- Abbreviations
- Chapter 6. Digital twin model design for smart village
- 6.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 6.2 Digital twin origin
- 6.2.1 Technical concept of digital twin technology
- 6.2.2 Illustration of the DT (digital twin) concept
- 6.2.3 History of digital twin technology
- 6.2.4 Types of digital twins
- 6.2.4.1 DT creation time
- 6.2.4.2 Levels of integration
- 6.3 Advantages and benefits of digital twins
- 6.3.1 Speed prototyping as well as product re-designing
- 6.3.2 Cost-effective
- 6.3.3 Predicting problems/system planning
- 6.3.4 Optimizing solutions and improved maintenance
- 6.3.5 Accessibility
- 6.3.6 Safer than the physical counterpart
- 6.3.7 Waste reduction
- 6.3.8 Documentation and communication
- 6.3.9 Training
- 6.4 Digital twin applications
- 6.4.1 Smart cities/rural smart cities
- 6.4.2 Manufacturing
- 6.4.3 Healthcare
- 6.5 Digital twin for smart villages
- 6.5.1 Why smart villages are needed?
- 6.5.2 Smart villages strategy
- 6.6 Farm management–use of digital twin in smart villages
- 6.6.1 Digital twins in farm management
- 6.6.2 Conceptual framework: Digital twins in farm management
- 6.6.2.1 Control model
- 6.6.2.2 Future research directions
- 6.6.2.3 Digital twin use case
- 6.7 Conclusion and future scope
- Chapter 7. Digital village analytics using digital twins
- 7.1 Introduction
- Objective of the chapter
- Organization of chapter
- 7.2 Theoretical background
- 7.2.1 History of digital twin (DT)
- 7.2.2 Digital twin: Definitions
- 7.2.3 Components and key characteristics of digital twin (DT)
- 7.2.4 Definition of digital village
- 7.3 Research methodology
- 7.3.1 Literature search
- 7.3.2 Selection of relevant papers
- 7.3.3 Determining the factors that drive the implementation of DVA using DT
- 7.4 Results and findings
- 7.4.1 Digital village concept
- 7.4.1.1 Definition and characteristics
- 7.4.1.2 Importance of digitalization in rural areas
- 7.4.1.3 Challenges of implementing digital twins in rural areas
- 7.4.1.4 Opportunities of implementing digital twins in rural areas
- 7.4.2 Digital twins: Foundation and applications
- 7.4.2.1 Understanding digital twins
- 7.4.2.2 Application of digital twins
- 7.4.2.3 Potential for rural development
- 7.4.3 Framework for digital analytics using digital twins
- 7.4.3.1 Digital twin creation and synchronization
- 7.4.3.2 Real-time data integration
- 7.4.3.3 Advanced analytics layers
- 7.4.4 Data collection and integration
- 7.4.5 Community engagement and participatory decision-making
- 7.4.5.1 Empowering local citizens
- 7.4.6 Privacy and security considerations
- 7.4.7 Digital village analytics potential
- 7.4.7.1 Agricultural resource management
- 7.4.7.2 Healthcare services enhancement
- 7.4.7.3 Education and skill development
- 7.4.8 Impact and benefits
- 7.4.8.1 Resource optimization and efficiency
- 7.4.8.2 Disaster preparedness and response
- 7.4.8.3 Bridging the digital divide in a digital village
- 7.4.9 Digital village analytics using digital twins: Challenges and ethical considerations
- 7.4.9.1 Technical and infrastructure challenges
- 7.4.9.2 Challenges related to data privacy and accessibility
- 7.4.9.3 Ethical concerns around data collection and usage
- 7.4.9.4 Socioeconomic and cultural considerations
- 7.4.9.5 Scalability and sustainability
- 7.4.10 Future directions and innovations in digital village analytics using digital twins
- 7.4.10.1 Emerging technologies and trends in digital twins and village analytics
- 7.4.10.2 Potential impact of AI, 5G, and edge computing
- 7.5 Conclusion
- Chapter 8. Toward seamless mobility: Integrating connected and autonomous vehicles in smart cities through digital twins
- 8.1 Introduction
- 8.1.1 Background and context
- 8.1.1.1 Role of CAVs in shaping their future
- Objectives of the chapter
- Organization of chapter
- 8.2 Cross-disciplinary collaboration for smart mobility
- 8.2.1 Importance of collaboration
- 8.2.2 Integrated urban planning
- Examples
- 8.3 CAV Technologies for Efficient Mobility
- 8.3.1 Sensor systems and machine learning algorithms
- 8.3.2 Real-time communication protocols
- 8.4 Enhancing user experience and acceptance
- 8.4.1 User Experience Studies
- 8.4.2 Human-Machine Interaction (HMI) Design
- 8.5 Sustainable Mobility and Environmental Impact
- 8.5.1 Energy use and efficiency
- 8.5.2 Mitigating environmental impact
- 8.6 Leveraging digital twins for smart mobility
- 8.6.1 Digital twins in urban mobility
- 8.6.2 Advancing smart mobility with digital twins
- 8.7 Conclusion
- Chapter 9. Redefining mobility: The convergence of autonomy, technology, and connected vehicles in smart cities
- 9.1 Introduction: Converging innovations
- 9.1.1 Defining Digital Twin technologies: Creating virtual counterparts of physical systems
- 9.1.2 Decoding the concept of Digital Twin
- Objectives of the chapter
- Organization of chapter
- 9.2 Enablers and barriers to the adoption of Digital Twin Technology
- 9.3 Integration of Digital Twin Technology as a catalyst for transforming urban transportation
- 9.4 Landscape of smart cities, mobility and CAVs
- 9.5 Mobility-as-a-Service (MaaS): From “Mass Transit” to “MaaS Transit”
- 9.5.1 Real-world MaaS implementation case studies: “The Whim”—Helsinki, Finland
- 9.5.2 Enhancing mobility through CAVs
- 9.6 Socio-economic impacts
- 9.7 Future directions and challenges
- 9.8 Conclusion
- Chapter 10. Planning and building digital twins for smart cities
- 10.1 Introduction
- 10.1.1 Significance of digital twins in smart cities
- 10.1.2 Benefits and challenges of implementing digital twins in smart cities
- Objectives of the chapter
- Organization of chapter
- 10.1.3 Literature review
- 10.2 Data collection and integration for digital twins
- 10.2.1 IoT devices and sensor networks for real-time data collection
- 10.2.2 Data integration across various urban systems
- 10.2.3 Data quality and management considerations
- 10.3 Advanced analytics and simulation in digital twins
- 10.3.1 Artificial intelligence and machine learning techniques
- 10.3.2 Predictive modeling and scenario simulations
- 10.3.3 Real-time data analysis for decision-making
- 10.4 Building a comprehensive digital twin of the smart city
- 10.5 Challenges and governance of digital twins in smart cities
- 10.5.1 Data privacy and security concerns
- 10.5.2 Interoperability and data standardization
- 10.5.3 Governance and policy frameworks for digital twins
- 10.6 Applications of digital twins in smart cities
- 10.6.1 Urban planning and infrastructure optimization
- 10.6.2 Disaster preparedness and emergency response
- 10.6.3 Smart mobility and transportation management
- 10.6.4 Energy efficiency and resource management
- 10.6.5 Citizen engagement and participatory decision-making
- 10.7 Future trends and innovations in digital twins for smart cities
- 10.7.1 Evolving role of digital twins in smart city development
- 10.7.2 Digital twin modeling and simulation in smart cities
- 10.7.3 Optimizing resource allocation and efficiency in smart cities
- 10.7.4 Enhancing mobility and transportation systems with digital twins
- 10.7.5 Leveraging digital twins for sustainable energy management
- 10.8 Top 10 case studies and best practices in digital twin implementation for smart cities
- 10.9 Conclusion and future scope
- Chapter 11. A dashboard framework for decision support in smart cities
- 11.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 11.2 Smart cities
- 11.2.1 Necessity of decision support in smart cities
- 11.2.2 Various sources of data in smart cities
- 11.3 Unified dashboard
- 11.3.1 Strategies for integrating diverse data streams into a unified dashboard
- 11.3.1.1 Follow the rules
- 11.3.2 Importance of processing real-time data to provide up-to-date information to decision-makers
- 11.4 Real-time data processing in smart cities
- 11.4.1 Technologies and techniques used for real-time data processing
- 11.4.2 Significance of data quality and accuracy in decision support
- 11.4.3 Methods for data validation, cleansing and ensuring data accuracy
- 11.5 Urban data
- 11.5.1 Privacy concerns associated with collecting and using sensitive urban data
- 11.5.2 Security measures to protect data and prevent unauthorized access
- 11.5.3 Analytics and machine learning are used to extract valuable insights from large datasets
- 11.5.4 Examples of predictive analytics and anomaly detection in smart cities
- 11.6 Effective data visualization to users
- 11.6.1 Effective data visualization methods for presenting complex urban data to users
- 11.6.2 Dashboard design principles to enhance user experience
- 11.6.2.1 Advantages
- 11.7 Conclusion and future scope
- Future scope
- Chapter 12. Immersive learning trends using digital twins
- 12.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 12.2 Literature review
- 12.2.1 The concept of immersive learning
- 12.2.2 Understanding digital twins
- 12.2.3 Integration of digital twins in education
- 12.2.4 Current state of immersive learning technologies
- 12.2.5 Role of digital twins in immersive learning
- 12.3 Methodology
- 12.3.1 Literature search
- 12.3.2 Screening
- 12.3.3 Data extraction
- 12.3.4 Data analysis
- 12.3.5 Synthesis
- 12.4 Emerging trends in immersive learning
- 12.4.1 Overview of immersive learning trends
- 12.4.2 Growth of online education
- 12.5 Current landscape and future prospects
- 12.5.1 Limitations of traditional E-learning
- 12.5.2 Rise of experiential and interactive learning
- 12.5.3 The metaverse and its implications
- 12.5.4 Immersive learning experiences
- 12.5.5 Access to real-world experiences
- 12.5.6 Collaborative learning environments
- 12.5.7 Personalized learning
- 12.5.8 Socialization
- 12.6 Key themes and trends
- 12.6.1 Synthesized findings from literature review
- 12.6.2 Patterns in digital twin-enabled immersive learning
- 12.6.3 Common challenges and solutions
- 12.7 Future directions
- 12.7.1 For educators and instructors
- 12.7.2 For institutions and educational leaders
- 12.7.3 For technology providers and developers
- 12.7.4 For students and learners
- 12.7.5 Implications for practice in digital twin-driven immersive learning
- 12.7.6 Recommendations for educators and institutions
- 12.7.7 Pedagogical approaches and design considerations
- 12.8 Conclusion and future scope
- Chapter 13. Digital Twin and Virtual Reality, Augmented Reality, and Mixed Reality
- 13.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 13.2 Literature review
- 13.2.1 Digital Twin technology: Concepts and evolution
- 13.2.2 Virtual Reality (VR): Definitions and applications
- 13.2.3 Augmented Reality (AR): Fundamentals and use cases
- 13.2.4 Mixed Reality (MR): Blending real and virtual worlds
- 13.2.5 Convergence of Digital Twins and immersive technologies
- 13.3 Methodology
- 13.3.1 Systematic review approach
- 13.3.2 Data collection and selection criteria
- 13.3.3 Data analysis and synthesis
- 13.3.4 Digital Twin integration with immersive technologies
- 13.3.4.1 Enhancing Digital Twins with VR, AR, and MR
- 13.3.4.2 Applications in manufacturing and simulation
- 13.3.5 Healthcare training and patient care
- 13.3.6 Immersive education: Learning experiences
- 13.3.7 Architectural visualization and urban planning
- 13.3.8 Entertainment: Interactive and engaging experiences
- 13.4 Future prospects and innovation
- 13.4.1 Potential disruptions in various sectors
- 13.4.2 Collaborations and cross-industry impacts
- 13.5 Conclusion
- Chapter 14. Digital twins for telemedicine and personalized medicine
- 14.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 14.2 Foundations of digital twins
- 14.2.1 Definition of digital twins
- 14.2.2 Origin of digital twins
- 14.2.3 Evolution of digital twins in healthcare
- 14.2.4 Key features and capabilities
- 14.2.4.1 Real-time data integration
- 14.2.4.2 Predictive analytics
- 14.2.4.3 Bidirectional interactions
- 14.2.4.4 Lifecycle management
- 14.2.4.5 Integration with other digital systems
- 14.2.4.6 Visualization and immersion
- 14.2.4.7 Customization and scalability
- 14.3 Telemedicine and its current landscape
- 14.3.1 Definition and evolution
- 14.3.2 Advantages and challenges
- 14.3.2.1 Advantages
- 14.3.2.2 Challenges
- 14.3.3 Integrating digital twins into telemedicine
- 14.4 Personalized medicine: A new era of healthcare
- 14.4.1 Understanding personalized medicine
- 14.4.2 Genomic data and its role
- 14.4.3 How digital twins elevate personalized treatment?
- 14.5 A systematic review of digital twins for telemedicine and personalized medicine
- 14.5.1 Systematic search and PRISMA diagram
- 14.5.2 Global research publications
- 14.5.3 Contributing countries
- 14.5.4 Contributing affiliations
- 14.5.5 Thematic cluster analysis
- 14.5.6 Coauthorship analysis
- 14.6 Case study: Digital twins for diabetes patients
- 14.7 Advantages of combining digital twins, telemedicine, and personalized medicine
- 14.8 Challenges and considerations
- 14.9 The role of digital twins in smart cities and villages
- 14.10 Future trends and implications
- 14.11 Conclusion and future scope
- Abbreviations
- Chapter 15. Digital twin technology in smart agriculture: Enhancing productivity and sustainability
- 15.1 Introduction
- 15.1.1 Digital twin technology
- 15.1.2 Importance and potential of digital twin in agriculture
- Organization of chapter
- 15.2 Digital twin applications in agriculture
- 15.2.1 Digital twin in soil and irrigation
- 15.2.2 Digital twin in postharvest process
- 15.2.3 Precision farming and crop management
- 15.2.4 Livestock monitoring and management
- 15.2.4.1 Precision livestock farming (PLF) as a predecessor to the digital twin
- 15.2.4.2 Emotional and mental states of animals
- 15.2.4.3 Pig farm energy management
- 15.2.4.4 Understanding dairy cow growth and development
- 15.2.5 Supply chain optimization
- 15.2.5.1 How digital twins can help
- 15.2.5.2 How to implement a digital twin
- 15.2.6 Agricultural equipment maintenance and performance
- 15.3 Components of an agricultural digital twin
- 15.3.1 Data collection and sensing technologies
- 15.3.1.1 Remote sensing
- 15.3.1.2 IoT sensors
- 15.3.1.3 Digital twin platforms
- 15.3.2 IoT integration in agriculture
- 15.3.3 Cloud infrastructure and data management
- 15.4 Challenges and limitations
- 15.4.1 Data privacy and security concerns
- 15.4.2 Adoption barriers
- 15.4.3 Integration challenges with traditional farming systems
- 15.5 Future trends and developments
- 15.5.1 Advancements in AI and machine learning
- 15.5.2 Blockchain integration for traceability
- 15.5.3 Role of swarm robotics in automated agriculture
- 15.5.3.1 Case study 1: Blue River Technology's See & Spray
- 15.5.3.2 Case study 2: swarm robotics for pollination—Harvard's RoboBees
- 15.5.3.3 Case study 3: Small Robot Company's Tom
- 15.6 Conclusion and future scope
- Chapter 16. Digital twins solutions for smart logistics and transportation
- 16.1 Introduction
- 16.1.1 Background and motivation
- 16.1.2 Objectives of the chapter
- 16.1.3 Scope and significance
- Organization of chapter
- 16.2 Digital twins: Concepts and principles
- 16.2.1 Definition and evolution of digital twins
- 16.2.1.1 Definition of digital twins
- 16.2.1.2 Evolution of digital twins
- 16.2.2 Key components and characteristics
- 16.2.2.1 Key components of digital twins are (Fig. 16.4)
- 16.2.2.2 Characteristics of digital twins
- 16.2.3 Role of digital twins in logistics and transportation
- 16.3 Literature review
- 16.4 Smart logistics and transportation challenges
- 16.4.1 Current challenges and limitations
- 16.4.2 Integration of digital twins for problem solving
- 16.5 Applications of digital twins in logistics and transportation
- 16.6 Implementation of digital twins in smart transportation
- 16.7 Benefits and impact of digital twins in logistics
- 16.8 Challenges and considerations of digital twins in logistics and transportation
- 16.9 Case studies: Digital twins in smart logistics
- 16.9.1 Digital twin deployment in freight transportation
- 16.9.1.1 Operational enhancements
- 16.9.1.2 Benefits
- 16.9.2 Digital twins for urban mobility and public transit
- 16.9.2.1 Operational enhancements
- 16.9.2.2 Benefits
- 16.9.3 Port and terminal operations optimization
- 16.9.3.1 Operational enhancements
- 16.9.3.2 Benefits
- 16.10 Future trends and outlook
- 16.11 Conclusion
- Chapter 17. Exploring virtual smart healthcare trends using digital twins
- 17.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 17.2 Digital twins in healthcare
- 17.2.1 Understanding digital twins
- 17.2.2 Evolution of digital twins in healthcare
- 17.2.2.1 Real time implementation with use case diagram
- 17.2.3 Benefits and challenges of implementing digital twins in healthcare
- 17.3 Virtual smart healthcare
- 17.3.1 Role of digital twins in virtual smart healthcare
- 17.3.2 Integration of emerging technologies
- 17.4 Applications of digital twins in healthcare
- 17.4.1 Personalized medicine and treatment
- 17.4.2 Medical imaging and simulation
- 17.4.3 Remote patient monitoring
- 17.4.4 Predictive healthcare analytics
- 17.5 Smart devices and IoT in virtual healthcare
- 17.5.1 Internet of Things (IoT) in healthcare
- 17.5.2 Wearable devices and sensors
- 17.5.3 Smart health monitoring systems
- 17.6 Data privacy and security in virtual healthcare
- 17.6.1 Challenges and concerns
- 17.6.2 Data protection and compliance measures
- 17.7 Virtual healthcare and artificial intelligence
- 17.7.1 AI-driven diagnostics and decision support
- 17.7.2 Machine learning in healthcare
- 17.8 Virtual reality (VR) and augmented reality (AR) in healthcare
- 17.8.1 Enhancing patient experience through VR/AR
- 17.8.2 Surgical training and simulation
- 17.9 Future trends and innovations
- 17.9.1 The role of blockchain in virtual smart healthcare
- 17.9.2 Advancements in medical robotics
- 17.9.3 Human-machine collaboration in healthcare
- 17.9.3.1 Future implications
- 17.10 Conclusion and future directions
- Chapter 18. Harmonizing nature and technology: The synergy of digital twin-enabled smart farming
- 18.1 Introduction
- 18.1.1 Overview of smart farming
- 18.1.2 Introduction to digital twin technology
- Objectives of the chapter
- Organization of chapter
- 18.2 The role of digital twins in agriculture
- 18.2.1 Definition and concepts of digital twins
- 18.2.2 Importance of digital twins in smart farming
- 18.2.3 Key components of digital twins in agriculture
- 18.3 Data collection and integration
- 18.3.1 IoT sensors and devices for data collection
- 18.3.2 Data integration platforms and solutions
- 18.3.3 Challenges and solutions in data integration
- 18.4 Creating the digital twin
- 18.4.1 Building the virtual representation of the farm
- 18.4.2 Incorporating physical farming elements into the model
- 18.4.3 Challenges in developing the digital twin
- 18.5 Real-time monitoring and control
- 18.5.1 Leveraging IoT for real-time monitoring
- 18.5.2 Automation and remote control of farming operations
- 18.5.3 Benefits and limitations of real-time monitoring
- 18.6 Predictive analytics and decision support
- 18.6.1 Simulation and predictive modeling with digital twins
- 18.6.2 Data-driven decision-making in agriculture
- 18.6.3 Case studies of digital twin-based decision support
- 18.7 Resource optimization and sustainability
- 18.7.1 Optimizing resource usage with digital twins
- 18.7.2 Water management and irrigation systems
- 18.7.3 Precision agriculture techniques and digital twins
- 18.8 Risk management and resilience
- 18.8.1 Identifying and mitigating risks with digital twins
- 18.8.2 Climate and weather predictions for agriculture
- 18.8.3 Enhancing resilience with digital twin technology
- 18.9 Adoption and implementation challenges
- 18.9.1 Technological challenges in smart farming
- 18.9.2 Data privacy and security concerns
- 18.9.3 Addressing farmer adoption barriers
- 18.10 Future directions and opportunities
- 18.10.1 Emerging trends in digital twin technology
- 18.10.2 Integration with AI and machine learning
- 18.10.3 Promising applications of digital twins in agriculture
- 18.11 Conclusion
- 18.11.1 Summary of key points
- 18.11.2 Vision for the future of smart farming with digital twins
- Chapter 19. Design for digital twins in smart manufacturing
- 19.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 19.2 Literature review
- 19.2.1 Evaluation of digital twins
- 19.2.2 Smart manufacturing and industry 4.0
- 19.2.3 The need for design integration in manufacturing
- 19.3 Digital twin technology
- 19.3.1 Data acquisition and sensor integration
- 19.3.2 Data analytics and achine learning for twin simulation
- 19.3.3 Real-time monitoring and control
- 19.4 Design for digital twins (D4DT) framework
- 19.4.1 Rationale for D4DT implementation
- 19.4.2 Challenges and opportunities of D4DT adoption
- 19.5 Applications of D4DT in smart manufacturing
- 19.6 Benefits and impact of D4DT
- 19.6.1 Improved design efficiency and iteration
- 19.6.2 Enhanced predictive capabilities
- 19.6.3 Cost reduction and resource optimization
- 19.6.4 Accelerated time-to-market
- 19.7 Challenges and future directions
- 19.7.1 Data security and privacy concerns
- 19.7.2 Interoperability and standardization
- 19.7.3 Scalability and integration with existing systems
- 19.7.4 Potential of artificial intelligence and quantum computing
- 19.8 Real-world examples of digital twins in manufacturing
- 19.9 Case studies of successful implementations of digital twins in the manufacturing industry
- 19.9.1 Future prospects and trends in digital twin adoption
- 19.9.2 Limitations and challenges in digital twin implementation
- 19.9.3 Innovations and emerging technologies in smart manufacturing
- 19.10 Conclusion and future scope
- 19.10.1 Conclusion
- 19.10.2 Future scope
- Chapter 20. Digital Twins-enabled model for Smart Farming
- 20.1 Introduction
- 20.1.1 Technologies for Digital Twins in Smart Farming
- 20.1.2 Applications of Digital Twins in Smart Farming
- Objectives of the chapter
- Organization of chapter
- 20.2 Role of Digital Twins in agriculture
- 20.3 Literature review
- 20.4 Building and implementing Digital Twins for Smart Farming
- 20.5 Real-time monitoring and control
- 20.6 Challenges and considerations
- 20.7 Case studies of Digital Twins in Smart Farming
- 20.7.1 The John Deere Precision Agriculture System
- 20.7.2 Smart greenhouse management using Digital Twins from Microsoft Azure
- 20.8 Future trends and innovations
- 20.9 Conclusion and future scope
- Chapter 21. Electrical digital twins–enabled smart grid
- 21.1 Introduction
- Objectives of the chapter
- Organization of the chapter
- 21.2 Smart grids and digital transformation
- 21.2.1 Historical development of electrical grids
- 21.2.2 Traditional electrical grids
- 21.2.3 The emergence of smart grids
- 21.2.3.1 What are smart grids?
- 21.2.4 Benefits of smart grids
- 21.2.5 Challenges and considerations of smart grids
- 21.2.6 Case studies: Smart grid implementation around the world
- 21.2.7 Role of digital twins in grid modernization
- 21.2.7.1 The imperative for grid modernization
- 21.2.7.2 Digital twins in action: Applications in grid modernization
- 21.2.7.3 Case studies: Digital twin deployments in real world
- 21.3 Components of electrical digital twins
- 21.3.1 Real-time monitoring and control
- 21.3.2 Simulation and predictive analytics
- 21.4 Enhanced grid management
- 21.4.1 Adaptive load balancing and optimization
- 21.4.1.1 Challenges of load balancing
- 21.4.1.2 Digital twins in adaptive load balancing and optimization
- 21.4.1.3 Simulation and optimization
- 21.4.1.4 Benefits of adaptive load balancing and optimization with digital twins
- 21.4.2 Voltage and frequency regulation
- 21.4.2.1 Significance of voltage and frequency regulation
- 21.4.2.2 Digital twins in voltage and frequency regulation
- 21.4.2.3 Benefits of digital twins in voltage and frequency regulation
- 21.4.2.4 Real world examples
- 21.4.3 Microgrid operation and resilience
- 21.4.3.1 Microgrid operation
- 21.4.3.2 Microgrid resilience
- 21.4.3.3 Digital twins for microgrid operation and resilience
- 21.4.3.4 Real world examples
- 21.5 Fault detection and diagnosis
- 21.5.1 Early detection and isolation of faults
- 21.5.1.1 Significance of early detection
- 21.5.1.2 Techniques for early fault detection
- 21.5.1.3 Isolating faults
- 21.5.1.4 Role of technology
- 21.5.2 Self-healing mechanisms with digital twins
- 21.5.2.1 Promise of self-healing mechanisms
- 21.5.2.2 The role of digital twins
- 21.5.2.3 Self-healing strategies
- 21.5.2.4 Real-world examples
- 21.6 Demand response and energy efficiency
- 21.6.1 Dynamic demand management
- 21.6.1.1 Imperative of dynamic demand management
- 21.6.1.2 Principles of dynamic demand management
- 21.6.1.3 Real-world applications
- 21.6.1.4 Challenges and future directions
- 21.6.2 Peak load shaving and energy conservation
- 21.6.2.1 Challenges of peak loads
- 21.6.2.2 Leveraging digital twins
- 21.6.2.3 Peak load shaving strategies
- 21.6.2.4 Real-world applications
- 21.6.2.5 Challenges and future directions
- 21.7 AI-driven insights and decision support
- 21.7.1 Machine learning for anomaly detection
- 21.7.1.1 Imperative for anomaly detection
- 21.7.1.2 Machine learning for anomaly detection
- 21.7.1.3 Benefits of machine learning for anomaly detection
- 21.7.1.4 Case studies: Machine learning in anomaly detection
- 21.7.2 Predictive maintenance of grid infrastructure
- 21.7.2.1 Imperative for predictive maintenance
- 21.7.2.2 AI-driven predictive maintenance
- 21.7.2.3 Benefits of AI-driven predictive maintenance
- 21.7.2.4 Real world application of AI-driven predictive maintenance
- 21.8 Ethical and security considerations
- 21.8.1 Data privacy and ownership
- 21.8.1.1 Value of data in grid management
- 21.8.1.2 Data privacy and security challenges
- 21.8.1.3 Ethical considerations
- 21.8.1.4 Data ownership and governance
- 21.8.1.5 Security measures
- 21.8.2 Cyber security in digital twin-enabled smart grids
- 21.8.2.1 The digital twin revolution
- 21.8.2.2 Cyber security imperative
- 21.8.2.3 Cyber threat landscape
- 21.8.2.4 Ethical considerations
- 21.8.2.5 Security measures
- 21.9 Conclusion and future scope
- Chapter 22. Digital twins in microclimate analysis: A mixed review using a science mapping approach
- 22.1 Introduction
- Objectives of the Chapter
- Organization of Chapter
- 22.2 Materials and methods
- 22.2.1 Data collection
- 22.2.2 Data analysis
- 22.3 Results and discussion
- 22.3.1 Major contributors
- 22.3.2 Intellectual base
- 22.3.3 Hotspots of microclimate research
- 22.3.3.1 Cluster #0: Environmental impacts
- 22.3.3.2 Cluster #1: Urban morphology
- 22.3.3.3 Cluster #2: Energy efficiency
- 22.3.3.4 Cluster #3: Energy performance strategies
- 22.3.3.5 Cluster #4: Urban development impact
- 22.3.3.6 Cluster #5: Controlled environment agriculture (CEA)
- 22.3.3.7 Cluster #6: Biophilic design
- 22.3.3.8 Cluster #7: Biodiversity
- 22.3.3.9 Cluster #8: Building materials & urban design
- 22.4 Research gaps
- 22.5 Conclusion & future scope
- Chapter 23. Data analytics and visualization using digital twins
- 23.1 Introduction
- 23.1.1 Overview of data analytics and visualization in digital twins
- 23.1.2 Benefits of using data analytics and visualization
- Objectives of the chapter
- Organization of chapter
- 23.2 Machine learning and AI for digital twin analytics
- 23.2.1 Data preprocessing
- 23.2.2 Feature extraction
- 23.2.3 Model selection and evaluation
- 23.2.4 Neural networks
- 23.2.5 Decision trees
- 23.2.6 Support vector machines
- 23.3 Big data analytics and visualization
- 23.3.1 Data storage and management
- 23.3.2 Data processing tools
- 23.3.3 Data visualization techniques for digital twins
- 23.3.4 Apache Hadoop
- 23.3.5 Apache Spark and its significance in digital twin environments
- 23.3.6 Apache Flink and its significance in real-time digital twin analytics
- 23.4 Applications in smart cities
- 23.4.1 Digital twins for traffic management
- 23.4.2 Energy management use cases
- 23.4.3 Waste management systems
- 23.5 Challenges and limitations
- 23.6 Conclusion and future scope
- Chapter 24. Research trends in blockchain and digital twins
- 24.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 24.2 Blockchain technology
- 24.2.1 Blockchain technology overview
- 24.2.2 Blockchain components
- 24.2.3 Types of blockchain
- 24.2.4 Key concepts
- 24.2.5 Characteristics
- 24.2.6 Applications
- 24.2.7 Recent research trends in blockchain
- 24.2.8 Working of blockchain technology
- 24.3 Digital twins
- 24.3.1 Digital twins concept and applications
- 24.3.2 Components
- 24.3.3 Applications
- 24.3.4 Benefits
- 24.3.5 Advantages of digital twins with blockchain
- 24.4 Understanding blockchain and digital twins
- 24.4.1 Intersection of blockchain with digital twins
- 24.5 Benefits and challenges of integrating blockchain and digital twins
- 24.5.1 Benefits
- 24.5.2 Challenges
- 24.5.3 Benefits of digital twins using blockchain
- 24.5.3.1 Use cases of blockchain-enabled digital twins
- 24.5.4 Supply chain management
- 24.5.5 Smart cities and infrastructure
- 24.5.6 Urban planning and design
- 24.5.7 Building and infrastructure management
- 24.5.8 Environmental sustainability
- 24.5.9 Transportation and mobility
- 24.5.10 Public services and emergency response
- 24.5.11 Infrastructure maintenance and optimization
- 24.5.12 Public services and citizen engagement
- 24.5.13 Resilience and disaster recovery
- 24.5.14 Transportation and mobility
- 24.5.15 Data-driven decision-making
- 24.6 Digital twins applications
- 24.7 Security and privacy considerations
- 24.7.1 Data integrity and immutability through blockchain
- 24.7.2 Decentralized identity and access management for digital twins
- 24.7.3 Privacy-preserving techniques for blockchain-digital twin systems
- 24.8 Standardization and Interoperability
- 24.8.1 Existing standards in blockchain and IoT
- 24.8.2 Challenges in ensuring interoperability of blockchain-digital twin ecosystems
- 24.8.3 Initiatives and efforts for standardization
- 24.9 Summary, conclusion and future scope
- 24.9.1 Implications for industries and research
- 24.9.2 Conclusion
- 24.9.3 Future scope
- Chapter 25. Blockchain-based digital twin for supply chain management: A survey
- 25.1 Introduction
- Objectives of the chapter
- Organization of chapter
- 25.2 Background
- 25.2.1 Digital twins
- 25.2.2 Blockchain technology
- 25.2.3 Blockchain-based digital twin
- 25.3 Blockchain-based digital supply chain twin (BC-DSCT)
- 25.3.1 Key benefits of integrating DT with blockchain
- 25.3.1.1 Enhanced security and fraud prevention
- 25.3.1.2 Traceability of digital supply chain twin data (DSCT)
- 25.3.1.3 Transparency and privacy-preserving for DSCT data transaction
- 25.3.1.4 Decentralization and immutability of DSCT data storage
- 25.3.1.5 Blockchain-based access control on DSCT data
- 25.3.1.6 Secure DSCT data in a trustless blockchain system
- 25.3.2 Potential implementation in industry 4.0
- 25.3.2.1 Smart manufacturing
- 25.3.2.2 Intelligent maintenance
- 25.3.2.3 Blockchain-based digital twin shop floor
- 25.3.2.4 Blockchain-based digital twin warehouse and logistics
- 25.4 Future research opportunities
- 25.5 Conclusion
- Chapter 26. Rise of blockchain based digital twin: A transformative tool for new research trends
- 26.1 Introduction
- Objective of the chapter
- Organization of the chapter
- 26.2 Background of digital twin and blockchain
- 26.2.1 Evaluation of digital twin
- 26.2.2 Evaluation of blockchain
- 26.2.2.1 The initial years of blockchain technology from 1991 to 2008
- 26.2.2.2 Evolution of blockchain: Transactions, phase 1 (2008–13): blockchain 1.0 (bitcoin)
- 26.2.2.3 The development of ethereum on the blockchain 2.0, phase 2 (2013–15)
- 26.2.2.4 Evolution of blockchain: applications, phase 3 (2015 onwards)
- 26.2.2.5 In the year 2017, the blockchain platform known as EOS.IO was introduced
- 26.2.2.6 In the year 2018, the blockchain 3.0: Future
- 26.3 Architecture of blockchain
- 26.3.1 Core components of blockchain: How does it works?
- 26.3.2 Types of blockchain
- 26.4 Benefits of Blockchain in Industry
- 26.5 Features of blockchain
- 26.6 Taxonomy of blockchain technologies
- 26.7 Research trends involved in blockchain based digital twin
- 26.7.1 Layout solution in creating digital twins
- 26.7.2 Layout solution in creating blockchain
- 26.7.3 Layout solution in security
- 26.8 Unified architecture for blockchain based on digital twin
- 26.8.1 Layout process for digital twin
- 26.8.2 Layout process for blockchain
- 26.8.3 Layout process for security
- 26.9 The future of integrating blockchain-based digital twins with AI
- 26.9.1 Integrated artificial intelligence and blockchain benefits
- 26.9.2 AI and blockchain use cases
- 26.9.3 Challenges for integrated artificial intelligence and blockchain
- 26.10 Adoption of blockchain technology in India
- 26.10.1 Chandrayaan 3 and blockchain technology: A step in the right direction
- 26.11 Privacy concerns in blockchain
- 26.12 Future trends in blockchain technologies and digital twin
- 26.12.1 Future prospects in blockchain technologies
- 26.12.2 Future prospects in digital twin
- 26.13 Case studies in blockchain and digital twin
- 26.14 Conclusion and further scope
- Chapter 27. Exploring diverse use cases of digital twins projecting digital transformation: Unlocking potential, addressing challenges and viable solutions
- 27.1 Introduction
- 27.1.1 Background of study
- 27.1.2 Scope of chapter
- Objectives of the chapter
- Organization of chapter
- 27.2 Digital twins- evolution of digital twins from concept to reality
- 27.2.1 Significance in the era of industry 4.0 and Internet of Things (IoT)
- 27.3 Manufacturing Industry
- 27.3.1 Product design and prototyping: Digital twins enhance product development lifecycle
- 27.3.2 Case studies of successful implementations- aerospace, automotive and consumer goods industries
- 27.3.3 Predictive maintenance- leveraging digital twins for predictive maintenance in manufacturing equipment
- 27.3.4 Quality control: Role of digital twins in ensuring product quality
- 27.3.5 Applications in defect detection and process optimization: Harnessing the power of digital twins
- 27.4 Healthcare and digital twins
- 27.4.1 Personalized medicine: Utilizing digital twins for patient-specific treatment plans
- 27.4.2 Case studies in genomics and pharmaceutical research
- 27.4.3 Virtual patient models: Creating digital replicas of patients for simulation and training
- 27.4.4 Enhancing medical education and surgical planning
- 27.5 Smart cities and digital twins: Urban planning and infrastructure
- 27.5.1 Implementing digital twins for efficient city planning
- 27.5.2 Examples of smart infrastructure management and optimization
- 27.5.3 Public safety and emergency response: Utilizing digital twins for disaster preparedness and response
- 27.5.4 Case studies demonstrating improved emergency management
- 27.6 Challenges and ethical considerations
- 27.6.1 Data privacy and security: Addressing concerns relating to collection and use of sensitive data
- 27.6.2 Strategies for ensuring secure digital twin ecosystems
- 27.7 Emerging technologies and digital twins: Exploring dimensions
- 27.7.1 Artificial intelligence, machine learning and blockchain with digital twins
- 27.7.2 Potential advancements and their implications
- 27.8 Regulatory frameworks: Need for standardized regulations to guide ethical use of digital twins
- 27.8.1 Recommendations for policymakers and industry stakeholders
- 27.8.2 Education and skill development: Importance of training professionals to harness the full potential of digital twins
- 27.8.3 Proposals for educational programs and certifications
- 27.9 Conclusion and future scope
- Index
- Edition: 1
- Published: October 17, 2024
- Imprint: Elsevier
- No. of pages: 700
- Language: English
- Paperback ISBN: 9780443288845
- eBook ISBN: 9780443288852
SI
Sailesh Iyer
Dr. Sailesh Iyer has a Ph.D. (Computer Science), pursuing Post Doc from University of Louisiana, Lafayette, USA, and currently serving as a Professor with Rai University, Ahmedabad. He has more than 23 years of experience in academics, industry and corporate training. He has been awarded a Research Excellence Award for 2021 by Rai University and an Honorary Adjunct Research Scientist at Neurolabs International under Dana Brain Health Institute, Iran, from August 2022 to August 2025. He is an editor for book projects with various international publishers. He has been invited as keynote speaker in various international conferences. He has excelled in corporate training, delivered more than 100 expert talks in various AICTE sponsored STTP’s, ATAL FDP’s, reputed universities, government organized workshops, orientations and refresher courses. His research interest areas include computer vision and image processing, cybersecurity, data mining and analytics, artificial intelligence, machine learning, and blockchain.
Affiliations and expertise
Professor and Dean, CSE/IT Department, Rai School of Engineering, Rai University, Ahmedabad, Gujarat, IndiaAN
Anand Nayyar
Dr. Anand Nayyar received his Ph.D (Computer Science) from Desh Bhagat University in 2017 in Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Faculty of Information Technology- Duy Tan University, Vietnam. He has published numerous research papers in various high-impact journals and holds 10 Australian patents and 1 Indian Design to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing.
Affiliations and expertise
Professor, Scientist, Vice-Chairman (Research) and Director at IoT and Intelligent Systems Lab, Duy Tan University, VietnamAP
Anand Paul
Anand Paul is an Associate Professor with the Biostatistics and Data Science Program at Louisiana State University Health Science Center. He obtained his Ph.D. degree from the School of Electrical and Computer Engineering at National Cheng Kung University, Taiwan, R.O.C. in 2010. His research focuses on Big Data Analytics and Mathematical Modelling of Machine Learning models, He has done extensive work on Big data/IoT based Smart Cities. Dr. Paul is the founder and director of the Centre for Resilient and Evolving Intelligence at Kyungpook National University, South Korea, where he served from 2012 to 2024. He has been recognized as one of the top 2% scientists globally by both Stanford University and Elsevier Publisher for the years 2021 and 2022. He has been an IEEE Senior Member since 2015 and has held editorial roles in prestigious publications including Editor in Chief of the International Journal of Smart Vehicles and Smart Transportation (IGI Global) from 2019 to 2021, and as Associate Editor in IEEE Access, IET Wireless Sensor Systems, ICT Express, PeerJ Computer Science, ACM Applied Computing Reviews, Cyber Physical Systems (Taylor & Francis), and International Journal of Interactive Multimedia & Artificial Intelligence. Dr. Paul also served as the track chair for Smart Human-Computer Interaction in ACM SAC from 2014 to 2019.
Affiliations and expertise
Louisiana State University, United StatesMN
Mohd Naved
Dr. Mohd Naved is an Associate Professor at Jaipuria Institute of Management in Noida, India, with over a decade of experience in Business Analytics, Data Science, and Artificial Intelligence. His research focuses on the applications of business analytics, data science, and artificial intelligence across various industries.
Affiliations and expertise
Associate Professor, Jaipuria Institute of Management, IndiaRead Digital Twins for Smart Cities and Villages on ScienceDirect