A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic. (29th July 2019) - Huo, Liuwei; Jiang, Dingde; Zhu, Xiangnan; Wang, Yuqing; Lv, Zhihan; Singh, Surjit
Summary: In smart cities, a large number of vehicles are connected into an intelligent transportation system and share information via the vehicular communication network (VCN). Accurate, fine‐grained, and comprehensive traffic measurements are very crucial for the controller's decision making in software‐defined networking (SDN) in VCN. Fine‐grained traffic measurements can accurately portray network behaviors for VCN. However, this will increase a large amount of measurement overhead. Therefore, how to effectively obtain accurate and fine‐grained traffic is a huge challenge for VCN. To the end, this paper proposes a novel accurate and SDN‐based fine‐grained traffic measurement approach to obtain comprehensive traffic in VCN. Firstly, based on SDN architecture, we exploit the pull‐based sampling mechanism to quickly obtain coarse‐grained traffic measurement values. Secondly, based on the matrix completion theory, we use both interpolation and optimization methods to obtain fine‐grained traffic measurements. Thirdly, the optimization model and detailed algorithm are proposed to attain accurate traffic. Finally, we conduct a larger number of simulations to validate the measurement approach proposed in this paper. Simulation results show that our approach exhibits better performance and is promising. Abstract : This paper proposes a novel accurate and SDN‐based fine‐grained traffic measurement approach to obtain comprehensive traffic in the vehicular communication network (VCN). Based on SDN architecture and the matrix completion theory, we exploit the pull‐based sampling mechanism to quickly obtain coarse‐grained traffic measurement values, and we use both interpolation and optimization methods to obtain fine‐grained traffic measurements. The optimization model and detailed algorithm are proposed to attain accurate traffic.