数据驱动的信息物理系统(英文版)

Chapter 1 Introduction to Data-driven Cyber Physical Systems 1.1 What are cyber physical systems? 1.2 Data-driven approaches for CPS 1.3 Importance of DDCPS 1.4 Key challenges in DDCPS 1.5 Applications of DDCPS 1.6 Evolution of data-driven approaches in cyber physical systems 1.7 How can data be used to improve cyber physical systems? 1.8 Overview of the book References Chapter 2 Fundamentals of Data-driven Cyber Physical Systems 2.1 Definitions 2.1.1 Definitions of CPS 2.1.2 Definitions of DDCPS 2.2 Characteristics of DDCPS 2.2.1 Networked communication 2.2.2 Scalability 2.2.3 Heterogeneity 2.2.4 Interdisciplinary 2.2.5 Real-time processing 2.2.6 Real-time decision-making 2.3 Components of DDCPS 2.3.1 Sensing components 2.3.2 Computational components 2.3.3 Communication components 2.3.4 Control components 2.4 Examples of DDCPS in different industries 2.4.1 Smart grids 2.4.2 Agriculture 2.4.3 Healthcare 2.4.4 Intelligent transportation 2.4.5 Smart manufacturing 2.5 Challenges of DDCPS 2.5.1 Data storage 2.5.2 Integration 2.5.3 Communication 2.5.4 Cybersecurity 2.5.5 System stability 2.6 Summary References Chapter 3 Data Collection in Cyber Physical Systems 3.1 Sensors and auxiliary components 3.1.1 Type of sensor and auxiliary components 3.1.2 Factors for selecting sensors and auxiliary components 3.1.3 Typical scenarios for data collection 3.2 Types of data 3.2.1 One dimensional data 3.2.2 Image and video data 3.2.3 Other types of data 3.3 Real time and latency 3.3.1 Techniques for reducing latency 3.3.2 Key considerations of real time and latency 3.3.3 Evaluating the performance 3.4 Data quality and reliability issues 3.4.1 Data preprocessing techniques 3.4.2 Impact of data redundancy on reliability 3.4.3 Data validation techniques 3.5 Summary References Chapter 4 Data Storage and Management in Cyber Physical Systems 4.1 Types of data storage for DDCPS 4.1.1 An introduction to data storage in DDCPS 4.1.2 Explore data storage instances in the system 4.2 Data management and processing techniques 4.2.1 Database management techniques 4.2.2 Data processing techniques 4.3 Big data processing technology of DDCPS 4.3.1 Data process for storage and management 4.3.2 Storage for DDCPS 4.3.3 Management for DDCPS 4.3.4 Big data for DDCPS 4.4 Summary References Chapter 5 Data Integration and Fusion in Cyber Physical Systems 5.1 Data integration and fusion 5.1.1 CPS data characteristics 5.1.2 CPS data integration 5.1.3 CPS data fusion 5.1.4 Data integration and fusion framework 5.1.5 Data representation 5.2 Techniques for fusing data from multiple sources 5.2.1 Stage-based data fusion methods 5.2.2 Semantic meaning-based data fusion 5.2.3 Artificial intelligence-based data fusion 5.3 CPS data integration and fusion case studies 5.3.1 Cloud-integrated CPS for smart cities case study 5.3.2 Data fusion framework for smart healthcare case study 5.4 Challenges and future work opportunities 5.4.1 Integrated models challenges 5.4.2 CPS data fusion challenges 5.4.3 Future work opportunities 5.5 Summary References Chapter 6 Data-driven Modeling and Simulation in Cyber Physical Systems - ~ 6.1 Importance of modeling and simulation in cyber physical systems 6.1.1 Importance of complex system modeling for CPS 6.1.2 Importance of complex system simulation for CPS 6.1.3 Benefits of modeling and simulation in CPS 6.2 Data-driven modeling techniques 6.2.1 Introduction to data-driven modeling 6.2.2 Types of data-driven models used in CPS 6.2.3 Methods for model selection and validation 6.2.4 Examples of data-driven modeling in CPS applications 6.3 Simulation and testing of cyber physical systems using data-driven models 6.3.1 Introduction to data-driven simulation 6.3.2 Types of data-driven simulation used in CPS 6.3.3 Model validation and uncertainty quantification 6.3.4 Case studies of simulation and testing using data-driven models in CPS applications 6.4 Summary References Chapter 7 Fault Detection and Predictive Maintenance in Cyber Physical Systems 7.1 An overview of fault detection and maintenance 7.1.1 The development of CPS fault detection 7.1.2 The development of CPS maintenance 7.1.3 Future trends of fault detection and predictive maintenance 7.2 Data-driven approaches for fault detection and predictive maintenance 7.2.1 Data-driven fault detection approaches 7.2.2 Data-driven predictive maintenance approaches 7.2.3 Discussion of fault detection and predictive maintenance 7.3 Applications of fault detection and predictive maintenance 7.3.1 Application background of fault detection and predictive maintenance 7.3.2 Case studies of fault detection and predictive maintenance 7.3.3 Challenges in cases 7.4 Summary References Chapter 8 Cyberseeurity in Data-driven Cyber Physical System 8.1 Cyber attacks in data-driven CPS 8.1.1 Attacks at the perception layer 8.1.2 Attacks at the transmission layer 8.1.3 Attacks at the platform layer 8.1.4 Attacks at the application layer 8.2 Requirements of cybersecurity 8.2.1 Objective of cybersecurity 8.2.2 Hardware security 8.2.3 Software security 8.2.4 Network security 8.2.5 Data security 8.3 Importance of cybersecurity in data-driven CPS 8.3.1 Data integrity and accuracy 8.3.2 Privacy and confidentiality 8.3.3 System resilience and availability 8.3.4 Regulatory requirements 8.4 Challenges of cybersecurity in data-driven CPS 8.4.1 Data-driven techniques for attack detection and mitigation 8.4.2 Data trustworthiness and policy-based sharing 8.4.3 Risk-based security metrics 8.5 Data-driven techniques of cybersecurity in CPS 8.5.1 Data-driven attack detection and migitation 8.5.2 Data-driven data confidence assessment 8.5.3 Risk assessment metrics 8.6 Summary References Chapter 9 Future of Data-driven Cyber Physical Systems 9.1 Potential impacts 9.2 Emerging trends and technologies in DDCPS 9.3 Societal and ethical implications 9.4 Concluding remarks Acknowledgements