An Adaptive and Efficient Network Traffic Measurement Method Based on SDN in IoT

Abstract

The Internet of Things (IoT) is a worldwide information network that connects thousands of technological gadgets. We incorporate the SDN network architecture into IoT networks and investigate the characteristics of SDN-based IoT networks in order to make the IoT more flexible and extendable. SDN (Software Defined Networking) is a logical control center with a centralized control plane that makes network management more flexible and efficient. For IoT network management, fine-grained and reliable traffic information is critical. Then, in SDN-based IoT networks, we construct a network traffic model by analyzing the self-similarity of network traffic in IoT network. Then, we collect some traffic statistics in OpenFlow-based switches as the source data and use it to train the proposed network traffic estimation model. Using the measured network traffic in the IoT network, we use the Kalman Filtering to measure and estimate each flow, this scheme just increases a little overhead. Then, we propose to an algorithm to search the more accuracy of traffic. Finally, we run additional simulations to ensure that the suggested measuring system is accurate. Simulation findings suggest that using intelligent optimization approaches, we can improve the granularity and accuracy of traffic data.