Big Data Driven Vehicular Networks

TL;DR

This paper reviews how big data analytics and machine learning can enhance the performance and reliability of vehicular networks by analyzing large-scale data for better communication and safety.

Contribution

It provides a comprehensive review of VANETs technologies and discusses innovative big data and machine learning methods to improve VANETs performance.

Findings

Machine learning can detect negative communication conditions in VANETs.

Big data analysis improves VANETs reliability and efficiency.

Review of VANETs technologies and data-driven performance enhancement.

Abstract

Vehicular communications networks (VANETs) enable information exchange 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 the 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 the VANETs technologies to efficiently and reliably transmit the 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 the VANETs measurement data for efficiently detecting negative communication conditions.