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 finegrained 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.

In Software Defined Networking (SDN), the fine-grained measurements are crucial for network management and design. However, the measurement overhead and accuracy are contradiction, how to accurately measure the network traffic with low overhead has become a hot topic. Artificial Intelligence (AI) has been used to predict the traffic in networks. Then, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for traffic measurement in SDN with low overhead and high measurement accuracy. Firstly, we use measurements in the front to train the AI-based traffic prediction model and utilize the model to predict traffic in SDN. Then, we obtain the sequence of sampling points by judging the change of traffic prediction and send the measurement primitive to switches to obtain coarse-grained measurements. At last, we utilize the interpolation theory to fill the coarse-grained measurement and propose an optimization function to optimize the fine-grained measurement. Simulation results show that the ALAMM is feasible, and the measurement overhead of ALAMM is low.