Kai Liu, Penglin Dai, Victor C.S. Lee
Toward Connected, Cooperative and Intelligent IoV
Frontier Technologies and Applications
Kai Liu, Penglin Dai, Victor C.S. Lee
Toward Connected, Cooperative and Intelligent IoV
Frontier Technologies and Applications
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This book offers a comprehensive introduction to technological advances in Internet of Vehicles (IoV), including vehicular communications, vehicular system architectures, data dissemination algorithms, resource allocation schemes, and AI-enabled applications. It focuses on the state-of-the-art IoV with regard to three major directions, namely networking, cooperation, and intelligence, including advanced wireless communication technologies, algorithm theory, optimization mechanisms, and AI technologies. In addition, the book includes a number of case studies with system prototype implementation…mehr
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This book offers a comprehensive introduction to technological advances in Internet of Vehicles (IoV), including vehicular communications, vehicular system architectures, data dissemination algorithms, resource allocation schemes, and AI-enabled applications. It focuses on the state-of-the-art IoV with regard to three major directions, namely networking, cooperation, and intelligence, including advanced wireless communication technologies, algorithm theory, optimization mechanisms, and AI technologies. In addition, the book includes a number of case studies with system prototype implementation and hands-on experiments in IoV, making it suitable both as a technical reference work for professionals and as a textbook for graduate students.
Produktdetails
- Produktdetails
- Verlag: Springer / Springer Nature Singapore / Springer, Berlin
- Artikelnr. des Verlages: 978-981-99-9649-0
- Seitenzahl: 348
- Erscheinungstermin: 15. März 2025
- Englisch
- Abmessung: 235mm x 155mm x 19mm
- Gewicht: 529g
- ISBN-13: 9789819996490
- ISBN-10: 981999649X
- Artikelnr.: 74264154
- Herstellerkennzeichnung
- Springer-Verlag KG
- Sachsenplatz 4-6
- 1201 Wien, AT
- ProductSafety@springernature.com
- Verlag: Springer / Springer Nature Singapore / Springer, Berlin
- Artikelnr. des Verlages: 978-981-99-9649-0
- Seitenzahl: 348
- Erscheinungstermin: 15. März 2025
- Englisch
- Abmessung: 235mm x 155mm x 19mm
- Gewicht: 529g
- ISBN-13: 9789819996490
- ISBN-10: 981999649X
- Artikelnr.: 74264154
- Herstellerkennzeichnung
- Springer-Verlag KG
- Sachsenplatz 4-6
- 1201 Wien, AT
- ProductSafety@springernature.com
Xu Quanyong is an associate research fellow and digital engine scientist at Tsinghua University, holding a Ph.D. from Beihang University (2010). Since 2012, he has focused on aero engine research, exploring digital and intelligent transformation technology, multi-field coupling co-simulation, and high-load compressor aerodynamic design. He developed software for aero-engine aerodynamic thermodynamic massively parallel computing, applied in gas turbine and aero-engine performance simulation. Xu led the creation of the Tsinghua Digital Engine Simulation System (T-DESS), used in future aero engine development. He managed 15 research projects, published 38 papers, and holds 31 patents. Xu has won awards including the Geneva Invention Exhibition silver Award and industry-research cooperation Award. Yang Jiali, male, graduated with a Ph.D. from the Department of Computer Science and Technology at Tsinghua University in 2016. From 2016 to 2019, he conducted postdoctoral research at the same department. Between 2019 and 2022, he worked as a postdoctoral researcher and assistant researcher at the Institute for Aero Engine (IAE) of Tsinghua University, focusing on the research of a real-time model-driven immersive virtual operation system for aerospace engines. During this period, he served as the project leader for the virtual operation system of the Digital Engine Project (Phase I). Since December 2022, he has been working as an associate researcher, conducting research on multi-system co-simulation of digital engines, automatic generation of control code, and other related topics. He is currently the project leader for the system prototype of the Digital Engine Project (Phase II). Cao Wenyu is a senior engineer at the Institute for Aerospace Propulsion, Tsinghua University, is the lead for the structural prototype of Tsinghua University’s Digital Engine Project. He has been engaged in the overall structural design and strength research of aerospace engines for an extended period. As the overall structural leader, he has participated in the development of multiple turbofan/turbojet engines and micro gas turbines. Additionally, he has been a key contributor to several major projects, including the National Science and Technology Project, the Chinese Academy of Sciences for Key Topics in Innovation Engineering. In the field of digital engineering, he is responsible for developing critical technologies for the digital thread of aerospace engines. His primary research areas include the integration and traceability of multi-source heterogeneous data across design, simulation, and manufacturing, MBSE-based integrated structural design and simulation¿ and configuration management based on physical systems. Dr. Hu Zhongzhi is currently the chief researcher of Institute for Aero Engine of Tsinghua University. He was once the deputy director of the R&D center of China Aero-Engine Commercial Development and the deputy chief designer of the large passenger engine project. Previously, he served as a chief engineer, project manager, research manager, and department manager at General Electric Company in the United States. Before entering the academic field, he has more than 20 years of experience in the design, development, integration, testing, and airworthiness certification of aeroengine control systems and health management systems both domestically and internationally. The current research areas are multi-disciplinary modeling and simulation of aeroengine control systems, multivariable and multimodal fault-tolerant control, intelligent health management, and MBSE method research and application. He has more than 20 domestic and foreign patents and has published more than 60 papers.
Part I. Introduction.- Chapter 1. Background of IoV.- Chapter 2. State-of-the-Art.- Part II. Connected IoV: Vehicular Communications and Data Dissemination.- Chapter 3. Data Dissemination via I2V/V2V Communications in Software Defined Vehicular Networks.- Chapter 4. Network Coding Assisted Data Broadcast in Large-Scale Vehicular Networks.- Chapter 5. Fog Computing Empowered Data Dissemination in Heterogeneous Vehicular Networks.- Chapter 6. Temporal Data Uploading and Dissemination in Real-time Vehicular Networks.- Part III Cooperative IoV: End-Edge-Cloud Cooperative Scheduling and Optimization.- Chapter 7. Convex Optimization on Vehicular End-Edge-Cloud Cooperative Task Offloading.- Chapter 8. An Approximation Algorithm for Joint Data Uploading and Task Offloading in IoV.- Chapter 9. Distributed Task Offloading and Workload Balancing in IoV.- Part IV. Intelligent IoV: Key Enabling Technologies in Vehicular Edge Intelligence.- Chapter 10. Toward Timely and Reliable DNN Inference in Vehicular Edge Intelligence.- Chapter 11. Deep Q-learning based Adaptive Multimedia Streaming in Vehicular Edge Intelligence.- Chapter 12. A Multi-agent Multi-objective Deep Reinforcement Learning Solution for Digital Twin in Vehicular Edge Intelligence.- Part V. Case Studies.- Chapter 13. See Through System.- Chapter 14. Non-Line-of-Sight Collision Warning System.- Chapter 15. Proactive Traffic Abnormity Warning System.- Chapter 16. UAV-assisted Pedestrian Detection System.- Chapter 17. Vehicular Indoor Localization and Tracking System.- Part VI. Conclusion and Future Directions.- Chapter 18. Conclusion .- Chapter 19. Future Directions.
Part I. Introduction.- Chapter 1. Background of IoV.- Chapter 2. State-of-the-Art.- Part II. Connected IoV: Vehicular Communications and Data Dissemination.- Chapter 3. Data Dissemination via I2V/V2V Communications in Software Defined Vehicular Networks.- Chapter 4. Network Coding Assisted Data Broadcast in Large-Scale Vehicular Networks.- Chapter 5. Fog Computing Empowered Data Dissemination in Heterogeneous Vehicular Networks.- Chapter 6. Temporal Data Uploading and Dissemination in Real-time Vehicular Networks.- Part III Cooperative IoV: End-Edge-Cloud Cooperative Scheduling and Optimization.- Chapter 7. Convex Optimization on Vehicular End-Edge-Cloud Cooperative Task Offloading.- Chapter 8. An Approximation Algorithm for Joint Data Uploading and Task Offloading in IoV.- Chapter 9. Distributed Task Offloading and Workload Balancing in IoV.- Part IV. Intelligent IoV: Key Enabling Technologies in Vehicular Edge Intelligence.- Chapter 10. Toward Timely and Reliable DNN Inference in Vehicular Edge Intelligence.- Chapter 11. Deep Q-learning based Adaptive Multimedia Streaming in Vehicular Edge Intelligence.- Chapter 12. A Multi-agent Multi-objective Deep Reinforcement Learning Solution for Digital Twin in Vehicular Edge Intelligence.- Part V. Case Studies.- Chapter 13. See Through System.- Chapter 14. Non-Line-of-Sight Collision Warning System.- Chapter 15. Proactive Traffic Abnormity Warning System.- Chapter 16. UAV-assisted Pedestrian Detection System.- Chapter 17. Vehicular Indoor Localization and Tracking System.- Part VI. Conclusion and Future Directions.- Chapter 18. Conclusion .- Chapter 19. Future Directions.







