TY - JOUR
T1 - Big Data Driven Vehicular Networks
AU - Cheng, Nan
AU - Lyu, Feng
AU - Chen, Jiayin
AU - Xu, Wenchao
AU - Zhou, Haibo
AU - Zhang, Shan
AU - Shen, Xuemin Sherman
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - VANETs enable information exchange among vehicles, other end devices and public networks, which plays a key role in road safety/infotainment, intelligent transportation systems, and self-driving systems. As vehicular connectivity soars, and new on-road mobile applications and technologies emerge, VANETs are generating an ever-increasing amount of data, requiring fast and reliable transmissions through VANETs. On the other hand, a variety of VANETs related data can be analyzed and utilized to improve the performance of VANETs. In this article, we first review VANETs technologies to efficiently and reliably transmit big data. Then, the methods employing big data for studying VANETs characteristics and improving VANETs performance are discussed. Furthermore, we present a case study where machine learning schemes are applied to analyze VANETs measurement data for efficiently.
AB - VANETs enable information exchange among vehicles, other end devices and public networks, which plays a key role in road safety/infotainment, intelligent transportation systems, and self-driving systems. As vehicular connectivity soars, and new on-road mobile applications and technologies emerge, VANETs are generating an ever-increasing amount of data, requiring fast and reliable transmissions through VANETs. On the other hand, a variety of VANETs related data can be analyzed and utilized to improve the performance of VANETs. In this article, we first review VANETs technologies to efficiently and reliably transmit big data. Then, the methods employing big data for studying VANETs characteristics and improving VANETs performance are discussed. Furthermore, we present a case study where machine learning schemes are applied to analyze VANETs measurement data for efficiently.
UR - https://www.scopus.com/pages/publications/85046686489
U2 - 10.1109/MNET.2018.1700460
DO - 10.1109/MNET.2018.1700460
M3 - 文章
AN - SCOPUS:85046686489
SN - 0890-8044
VL - 32
SP - 160
EP - 167
JO - IEEE Network
JF - IEEE Network
IS - 6
M1 - 8450539
ER -