04971nam a22006375i 4500 978-981-99-9647-6 DE-He213 20251030111722.0 cr nn 008mamaa 240314s2024 si | s |||| 0|eng d 9789819996476 978-981-99-9647-6 10.1007/978-981-99-9647-6 doi QA76.59 UMS bicssc COM051460 bisacsh UMS thema Liu, Kai. author. aut http://id.loc.gov/vocabulary/relators/aut 73078 Toward Connected, Cooperative and Intelligent IoV [electronic resource] : Frontier Technologies and Applications / by Kai Liu, Penglin Dai, Victor C.S. Lee, Joseph Kee-Yin Ng, Sang Hyuk Son. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XX, 327 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Mobile computing. 64148 Cooperating objects (Computer systems). 64303 Mathematical optimization. 65522 Algorithms. 58677 Internet of things. 58845 Machine learning. 55059 Mobile Computing. 64148 Cyber-Physical Systems. 65638 Discrete Optimization. 73079 Design and Analysis of Algorithms. 67299 Internet of Things. 58845 Machine Learning. 55059 Dai, Penglin. author. aut http://id.loc.gov/vocabulary/relators/aut 73080 Lee, Victor C.S. author. aut http://id.loc.gov/vocabulary/relators/aut 73081 Ng, Joseph Kee-Yin. author. aut http://id.loc.gov/vocabulary/relators/aut 73082 Son, Sang Hyuk. author. aut http://id.loc.gov/vocabulary/relators/aut 73083 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819996469 Printed edition: 9789819996483 Printed edition: 9789819996490 https://doi.org/10.1007/978-981-99-9647-6 ZDB-2-SCS ZDB-2-SXCS 58343 58343