Digital Twins for Smart Cities and Villages

1st Edition - October 17, 2024
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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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 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.

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

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, India

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, Vietnam

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 States

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, India

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