Big Data Driven Vehicular Networks

TLDR

VANETs facilitate information exchange among vehicles, devices, and public networks, supporting road safety, infotainment, ITS, and autonomous driving, while the surge in connectivity generates vast data that must be transmitted reliably and can be leveraged to enhance network performance. The article reviews technologies for efficient, reliable big‑data transmission in VANETs and discusses methods that use big data to analyze and improve VANET characteristics. The authors review transmission technologies, survey big‑data methods for VANET analysis, and present a machine‑learning case study that detects adverse communication conditions from measurement data. The case study demonstrates that machine‑learning schemes can efficiently detect negative communication conditions from VANET measurement data.

Abstract

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 detecting negative communication conditions.