报告主题：Big Data Driven VANETs
Vehicular networks (VANETs) connect vehicles to enable information communications among vehicles, other end devices and public networks, which plays a key role in road safety/infotainment, intelligent transportation system, and self-driving system. As new on-road mobile applications and technologies emerge, VANETs are generating an ever-increasing amount of data, requiring reliable storage, fast transmission, and efficient processing. On the other hand, a variety of related data can be analyzed and utilized to facilitate the design and improve the performance of VANETs. In this talk, the emerging big data-driven VANETs will be introduced. The overview of VANETs in big data era and related applications will be presented. A review of the VANET technologies to efficiently and reliably handle the big data is provided. Specifically, utilizing the multiple data pipes on various spectrum frequency bands to deliver the VANETs big data is discussed in detail. Then, the methods employing big data for studying VANETs characteristics and improving VANETs performance are explained. The potentials of applying the big data technology in VANETs will be further discussed.
Dr. Nan Cheng received PhD degree from the Department of Electrical and Computer Engineering, University of Waterloo in 2015, and B.E. degree and the M.S. degree from the Department of Electronics and Information Engineering, Tongji University, Shanghai, China, in 2009 and 2012, respectively. He is now a post-doctoral fellow in University of Toronto. The focus of his research is vehicular communication and Internet of vehicles, including vehicular communication protocol designing, VANETs performance analysis, vehicular big data offloading, heterogeneous VANETs. He has published a monograph, and over 40 papers in reputed IEEE journals and conferences. His first authored paper “Opportunistic WiFi Offloading in Vehicular Environment: A Queueing Analysis” received best paper award in IEEE GLOBECOM 2014.