Research Area:  Vehicular Ad Hoc Networks

Abstract:

Due to the lack of fully considering dynamic traffic information, in Vehicular Ad-hoc Networks (VANETs), existing routing schemes are easily stuck in local optimum, path redundancy, and congestion problems. By combining Software Defined Network (SDN) with VANET, the emerging Software Defined Vehicular Network (SDVN) can provide a global perspective of the traffic network to bridge the gap. In this paper, we introduce an intelligent fuzzy-based routing scheme for urban SDVN. First, a large urban area is divided into multiple sub-areas, in which each area is centered on an intersection. Second, the central controller maintains a routing table that records the priorities of packets be forwarded from an area to another, and all values in the routing table are initialized using Fuzzy Logic. Finally, we propose a hierarchical greedy routing with link stability (GLS) to calculate the routing path with the highest link stability according to the routing table. Meanwhile, considering the dynamic nature of vehicles in an area, Reinforcement Learning is employed to update the routing table during the routing process. Simulation results show that the proposed routing scheme achieved a significant improvement in performance over its counterparts.

Keywords:  

Author(s) Name:  Liang Zhao,Zhenguo Bi,Mingwei Lin,Ammar Hawbani,Junling Shi,Yunchong Guan

Publisher name:  ELSEVIER

DOI:  10.1016/j.comnet.2021.107837

Volume Information:  Volume 187, 14 March 2021, 107837

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1389128621000219