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Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions begins with discussions on how the transportation system has transformed into today’s Intelligent Transportation System (ITS). It explores the design goals, challenges, and frameworks for modeling an ITS network, discussing vehicular network model technologies, mobility management architectures, and routing mechanisms and protocols. It looks at the Internet of Vehicles, the vehicular cloud, and vehicular network security and privacy issues.
The book investigates cooperative vehicular systems, a promising solution for addressing current and future traffic safety needs, also exploring cooperative cognitive intelligence, with special attention to spectral efficiency, spectral scarcity, and high mobility. In addition, users will find a thorough examination of experimental work in such areas as Controller Area Network protocol and working function of On Board Unit, as well as working principles of roadside unit and other infrastructural nodes.
Finally, the book examines big data in vehicular networks, exploring various business models, application scenarios, and real-time analytics, concluding with a look at autonomous vehicles.
- Proposes cooperative, cognitive, intelligent vehicular networks
- Examines how intelligent transportation systems make more efficient transportation in urban environments
- Outlines next generation vehicular networks technology
Vehicular and Wireless Network researchers, instructors, students, designers, and engineers
- Chapter 1: Introduction: intelligent vehicular communications
- 1.1. Background of transportation networks
- 1.2. Evolution of transportation models
- 1.3. Vehicular network standardization
- 1.4. Vehicular communication technologies
- 1.5. Concluding statement
- Chapter 2: Intelligent transportation systems
- 2.1. Intelligent transportation systems
- 2.2. ITS applications and enabling technologies
- 2.3. Emerging ITS applications
- 2.4. ITS market segmentation
- 2.5. Case study
- 2.6. Conclusions
- Chapter 3: Vehicular network (VN) model
- 3.1. Cluster-based vehicular networks
- 3.2. Vehicle platooning
- 3.3. Vehicular cloud
- 3.4. Hybrid sensor–vehicular networks
- 3.5. Information distribution
- 3.6. Internet of Vehicles
- 3.7. Chapter summary
- Chapter 4: Evaluation of vehicular network models
- 4.1. Data dissemination in vehicular networks
- 4.2. Mobility management: IPv6-based Internet
- 4.3. A seamless flow mobility management architecture for vehicular communication networks
- 4.4. A seamless flow mobility management architecture
- 4.5. Vehicular-delay tolerant network
- 4.6. Formal model of human driving behavior
- Chapter 5: Cognitive radio in vehicular network
- 5.1. Cognitive radio for vehicular networks
- 5.2. Cooperative cognitive radio networks
- 5.3. Concluding comments
- Chapter 6: Theory and application of vehicular networks
- 6.1. Automotive context-aware in vehicular network
- 6.2. A novel vehicular information network architecture based on named data networking
- 6.3. Vehicular cloud networking: architecture and design principles
- 6.4. Trust-based information dissemination framework for vehicular networks
- 6.5. Knowledge-based intelligent transportation system
- 6.6. Hybrid sensor and vehicular networks
- 6.7. Intravehicle networks
- 6.8. Vision-based vehicle behavior analysis
- 6.9. Networked vehicle surveillance in ITS
- 6.10. Conclusions
- Chapter 7: Vehicular network as business model in Big Data
- 7.1. Big Data technology in vehicular networks
- 7.2. Data validation in Big Data
- 7.3. Real-time analysis of Data in VANET
- 7.4. Vehicular density analysis using Big Data
- 7.5. Vehicular carriers for Big Data
- Chapter 8: Big Data collision analysis framework
- 8.1. Road traffic data
- 8.2. Collision rate model
- 8.3. Design of road traffic Big Data collision analysis processing framework
- 8.4. Vehicle XML device collaboration with Big Data
- 8.5. Big Data technologies in support of real-time capturing and understanding of electric vehicles
- 8.6. Conclusions
- Chapter 9: Future trends and challenges in ITS
- 9.1. Next generation vehicular networks
- 9.2. Framework definition
- 9.3. Supporting augmented floating car data through smartphone-based crowd-sensing
- 9.4. Enabling vehicular mobility in citywide IEEE 802.11 networks through predictive handovers
- 9.5. Real-time path planning based on hybrid-VANET-enhanced transportation system
- 9.6. Recent advances in cryptographic solutions for vehicular networks
- 9.7. Standards harmonization efforts on future ITS
- 9.8. A novel vehicular mobility modeling technique for developing ITS applications
- 9.9. Conclusions
- No. of pages:
- © Elsevier 2016
- 1st September 2016
- eBook ISBN:
- Paperback ISBN:
Anand Paul is currently working in The School of Computer Science and Engineering, Kyungpook National University, South Korea as Associate Professor. He got his Ph.D. degree in the electrical engineering at National Cheng Kung University, Taiwan, R.O.C. in 2010. His research interests include Algorithm and Architecture Reconfigurable Embedded Computing. He is a delegate representing South Korea for M2M focus group and for MPEG. He has been awarded Outstanding International Student Scholarship, and in 2009, 2015 he won the best paper award in national computer Symposium, in Taipei Taiwan and international conference on Softcomputing and network security, India.
Kyungpook National University, South Korea
Naveen Chilamkurti is Acting Head of Department, Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. He is Editor-in-Chief of International Journal of Wireless Networks and Broadband Technologies, and Associate Editor of several other international journals. Dr. Naveen has published more than 165 journal and conference papers, and his research includes intelligent transport systems, wireless multimedia, and wireless sensor networks.
La Trobe University, Melbourne, Australia
Alfred Daniel is currently an assistant professor of Research and Cloud Computing at SNS College of Technology in Coimbatore, India. His research interests incllude Computer Architecture, artificial intelligence, and computer networks.
SNS College of Technology, India
Dr. Seungmin Rho, Ph.D. is a faculty of Department of Media Software at Sungkyul University in Korea. In 2012, he was an assistant professor at Division of Information and Communication in Baekseok University. In 2009-2011, he had been working as a Research Professor at School of Electrical Engineering in Korea University. In 2008-2009, he was a Postdoctoral Research Fellow at the Computer Music Lab of the School of Computer Science in Carnegie Mellon University. He gained his B.Science. (2001) in Computer Science from Ajou University, Korea (South), M.Science. (2003) and Ph.D. (2008) in Information and Communication Technology from the Graduate School of Information and Communication at Ajou University. He visited Multimedia Systems and Networking Lab. in Univ. of Texas at Dallas from Dec. 2003 to March 2004. Before he joined the Computer Sciences Department of Ajou University, he spent two years in industry. His current research interests include database, big data analysis, music retrieval, multimedia systems, machine learning, knowledge management as well as computational intelligence.
Sungkyul University, Korea
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