Chapter 1 Introduction to Data-driven Cyber Physical Systems 1
1.1 What are cyber physical systems 2
1.2 Data-driven approaches for CPS 3
1.3 Importance of DDCPS 3
1.4 Key challenges in DDCPS 4
1.5 Applications of DDCPS 11
1.6 Evolution of data-driven approaches in cyber physical systems 12
1.7 How can data be used to improve cyber physical systems 15
1.8 Overview of the book 18
References 18
Chapter 2 Fundamentals of Data-driven Cyber Physical Systems 20
2.1 Definitions 20
2.1.1 Definitions of CPS 20
2.1.2 Definitions of DDCPS 31
2.2 Characteristics of DDCPS 34
2.2.1 Networked communication 35
2.2.2 Scalability 36
2.2.3 Heterogeneity 38
2.2.4 Interdisciplinary 39
2.2.5 Real-time processing 40
2.2.6 Real-time decision-making 41
2.3 Components of DDCPS 41
2.3.1 Sensing components 41
2.3.2 Computational components 42
2.3.3 Communication components 43
2.3.4 Control components 44
2.4 Examples of DDCPS in different industries 45
2.4.1 Smart grids 45
2.4.2 Agriculture 46
2.4.3 Healthcare 47
2.4.4 Intelligent transportation 49
2.4.5 Smart manufacturing 51
2.5 Challenges of DDCPS 53
2.5.1 Data storage 54
2.5.2 Integration 55
2.5.3 Communication 56
2.5.4 Cybersecurity 57
2.5.5 System stability 58
2.6 Summary 60
References 60
Chapter 3 Data Collection in Cyber Physical Systems 66
3.1 Sensors and auxiliary components 66
3.1.1 Type of sensor and auxiliary components 67
3.1.2 Factors for selecting sensors and auxiliary components 71
3.1.3 Typical scenarios for data collection 75
3.2 Types of data 79
3.2.1 One dimensional data 81
3.2.2 Image and video data 83
3.2.3 Other types of data 85
3.3 Real time and latency 87
3.3.1 Techniques for reducing latency 88
3.3.2 Key considerations of real time and latency 92
3.3.3 Evaluating the performance 95
3.4 Data quality and reliability issues 98
3.4.1 Data preprocessing techniques 100
3.4.2 Impact of data redundancy on reliability 103
3.4.3 Data validation techniques 104
3.5 Summary 107
References 108
Chapter 4 Data Storage and Management in Cyber Physical Systems 115
4.1 Types of data storage for DDCPS 116
4.1.1 An introduction to data storage in DDCPS 116
4.1.2 Explore data storage instances in the system 128
4.2 Data management and processing techniques 131
4.2.1 Database management techniques 133
4.2.2 Data processing techniques 137
4.3 Big data processing technology of DDCPS 140
4.3.1 Data process for storage and management 141
4.3.2 Storage for DDCPS 141
4.3.3 Management for DDCPS 143
4.3.4 Big data for DDCPS 144
4.4 Summary 144
References 145
Chapter 5 Data Integration and Fusion in Cyber Physical Systems 153
5.1 Data integration and fusion 153
5.1.1 CPS data characteristics 154
5.1.2 CPS data integration 155
5.1.3 CPS data fusion 156
5.1.4 Data integration and fusion framework 157
5.1.5 Data representation 160
5.2 Techniques for fusing data from multiple sources 161
5.2.1 Stage-based data fusion methods 161
5.2.2 Semantic meaning-based data fusion 163
5.2.3 Artificial intelligence-based data fusion 170
5.3 CPS data integration and fusion case studies 173
5.3.1 Cloud-integrated CPS for smart cities case study 173
5.3.2 Data fusion framework for smart healthcare case study 175
5.4 Challenges and future work opportunities 179
5.4.1 Integrated models challenges 179
5.4.2 CPS data fusion challenges 181
5.4.3 Future work opportunities 185
5.5 Summary 187
References 188
Chapter 6 Data-driven Modeling and Simulation in Cyber Physical Systems 194
6.1 Importance of modeling and simulation in cyber physical systems 195
6.1.1 Importance of complex system modeling for CPS 197
6.1.2 Importance of complex system simulation for CPS 200
6.1.3 Benefits of modeling and simulation in CPS 203
6.2 Data-driven modeling techniques 205
6.2.1 Introduction to data-driven modeling 207
6.2.2 Types of data-driven models used in CPS 210
6.2.3 Methods for model selection and validation 226
6.2.4 Examples of data-driven modeling in CPS applications 229
6.3 Simulation and testing of cyber physical systems using data-driven models 230
6.3.1 Introduction to data-driven simulation 232
6.3.2 Types of data-driven simulation used in CPS 234
6.3.3 Model validation and uncertainty quantification 237
6.3.4 Case studies of simulation and testing using data-driven models in CPS
applications 238
6.4 Summary 240
References 241
Chapter 7 Fault Detection and Predictive Maintenance in Cyber Physical
Systems 247
7.1 An overview of fault detection and maintenance 247
7.1.1 The development of CPS fault detection 248
7.1.2 The development of CPS maintenance 250
7.1.3 Future trends of fault detection and predictive maintenance 251
7.2 Data-driven approaches for fault detection and predictive maintenance 253
7.2.1 Data-driven fault detection approaches 254
7.2.2 Data-driven predictive maintenance approaches 259
7.2.3 Discussion of fault detection and predictive maintenance 264
7.3 Applications of fault detection and predictive maintenance 266
7.3.1 Application background of fault detection and predictive maintenance 267
7.3.2 Case studies of fault detection and predictive maintenance 273
7.3.3 Challenges in cases 283
7.4 Summary 285
References 285
Chapter 8 Cybersecurity in Data-driven Cyber Physical System 291
8.1 Cyber attacks in data-driven CPS 293
8.1.1 Attacks at the perception layer 294
8.1.2 Attacks at the transmission layer 297
8.1.3 Attacks at the platform layer 299
8.1.4 Attacks at the application layer 301
8.2 Requirements of cybersecurity 302
8.2.1 Objective of cybersecurity 302
8.2.2 Hardware security 303
8.2.3 Software security 305
8.2.4 Network security 306
8.2.5 Data security 307
8.3 Importance of cybersecurity in data-driven CPS 308
8.3.1 Data integrity and accuracy 309
8.3.2 Privacy and confidentiality 310
8.3.3 System resilience and availability 311
8.3.4 Regulatory requirements 313
8.4 Challenges of cybersecurity in data-driven CPS 314
8.4.1 Data-driven techniques for attack detection and mitigation 314
8.4.2 Data trustworthiness and policy-based sharing 316
8.4.3 Risk-based security metrics 317
8.5 Data-driven techniques of cybersecurity in CPS 318
8.5.1 Data-driven attack detection and migitation 319
8.5.2 Data-driven data confidence assessment 330
8.5.3 Risk assessment metrics 332
8.6 Summary 334
References 334
Chapter 9 Future of Data-driven Cyber Physical Systems 345
9.1 Potential impacts 345
9.2 Emerging trends and technologies in DDCPS 349
9.3 Societal and ethical implications 351
9.4 Concluding remarks 353
Acknowledgements 355
內容試閱：
The field of cyber physical systems (CPS) has been rapidly evolving over the past few years， driven by advancements in computing， networking， and data analytics. As the world becomes more digitized and interconnected， CPS are playing an increasingly critical role in shaping the way we interact with technology and the world around us. The rise of the Internet of Things (IoT) and big data has brought about new opportunities and challenges in CPS design and development.
In complex systems， where interactions and interdependencies among components are intricate and diverse， data is even more crucial as it provides a basis for understanding system behavior， optimizing performance， and managing risk. As we move towards the data era， the need for CPS that can respond to the large amounts of data being generated becomes increasingly paramount. In this vein， I have written a comprehensive guide aimed at providing researchers， practitioners， and students with a thorough understanding of the principles， design， and implementation of data-driven CPS. In essence， this book should be a valuable resource for those interested in the applicability of CPS in various domains， including healthcare， transportation， energy， and manufacturing. The ideas and knowledge presented herein will enable readers to gain the necessary skills needed to design and develop effective data-driven CPS that can process and analyze large volumes of information quickly and efficiently.
The book takes a data-driven approach to CPS system design， covering vital techniques such as data acquisition， analysis， and modeling， AI and machine learning， network and distributed computing， and cybersecurity. The content is structured to cater to readers who are interested in developing a deep understanding of the state-of-the-art technologies and methodologies used in the development of data-driven CPS systems. It combines theoretical concepts with practical examples that highlight the potential of data-driven CPS in solving real-world problems.
Chapter 1 provides an introduction to CPS， their importance， and the challenges associated with their design and implementation. Chapter 2 discusses the basics of data-driven CPS. Chapter 3 covers the data collection， including data acquisition and evaluation for CPS. Chapter 4 talks about data storage and management. Chapter 5 delves into data integration and fusion for the CPS. Chapter 6 explores machine learning and AI techniques and their applications in modeling and simulation in CPS applications. Chapter 7 discusses fault detection and predictive maintenance with real-world case studies from various industries， highlighting the practical applications of CPS. Chapter 8 presents cybersecurity and protection issues in CPS. Finally， Chapter 9 concludes the book by summarizing the key points covered in the previous chapters and offering some final thoughts on the future of data-driven CPS.
As the field of cyber physical systems continues to evolve， data-driven approaches have become increasingly important in developing intelligent and responsive systems. With this book， I aim to provide a comprehensive resource for anyone interested in exploring the principles， design， and implementation of data-driven CPS. Whether you are a researcher， practitioner， or student， this book offers essential knowledge and techniques that will enable you to design effective CPS capable of processing and analyzing large volumes of data in real-time.
Throughout the writing process， I have strived to present the material in a clear， accessible manner， aided by examples and illustrations that elucidate key concepts and techniques. My hope is that readers will find this book useful not only as a technical reference but also as a source of inspiration for future research and development in the field.
I would like to extend my heartfelt appreciation to my colleagues， friends， and family who have supported and encouraged me throughout the writing process. Additionally， I would like to express my deepest gratitude to the reviewers who provided invaluable feedback and suggestions that helped to improve the quality and clarity of this book. Finally， I hope that this book will be a valuable resource for anyone interested in exploring the exciting world of data-driven cyber physical systems and the immense possibilities that they offer.
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