A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic

摘要

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.