Digital Twins for Smart Cities and Villages / edited by Sailesh Iyer [and three others].

Format:

Book

Contributor:

Iyer, Sailesh, editor.

Language:

English

Subjects (All):

Smart cities.

Digital twins (Computer simulation).

Physical Description:

1 online resource (702 pages)

Edition:

First edition.

Place of Publication:

Amsterdam, Netherlands : Elsevier, [2025]

Summary:

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.

Contents:

Front Cover

Digital Twins for Smart Cities and Villages

Copyright

Contents

Contributors

About the editors

Preface

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

References

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

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

3 - Digital twins tools and technologies

3.1 Introduction

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

4 - Future trends and research challenges in digital twins

4.1 Introduction

4.2 Development of digital twins

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

5 - Research advancements in quantum computing and digital twins

5.1 Introduction

Organization of chapter.

5.2 Background literature on the merge of quantum computing and digital twins.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

0-443-28885-2

OCLC:

1463063892