DACON: A Novel Traffic Prediction and Data-Highway-Assisted Content Delivery Protocol for Intelligent Vehicular Networks

Nowadays, to deal with driving safety-related issues and improve travel comfort, the VehiculAr NETwork (VANET) has gained tremendous attention from researchers in both academia and industry around the world. By taking advantage of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, the VANET can significantly enhance road safety and travel comfort by improving drivers' awareness of their surrounding road environment and providing entertainment-related data service for passengers, respectively. However, due to the highly dynamic nature of the network topology in VANET, how to achieve reliable data transmission and content delivery is a critical task for implementing VANETs. Accordingly, in this article, we provide a novel data-highway-assisted content delivery protocol for addressing the content delivery problem in VANETs, in which, we explore the advantages of the predicted vehicular traffic volume driven by a newly designed fast traffic flow prediction scheme. We evaluate the performance of the proposed traffic flow prediction scheme by using three different data sets with different vehicles traffic flow patterns are chosen from the England Highways data set. Moreover, extensive simulations have been implemented to evaluate the proposed content delivery protocol.

Duke Scholars

Author Peng Sun DKU Faculty