On a Novel Deep-Learning-Based Intelligent Partially Overlapping Channel Assignment in SDN-IoT

Recently, SDN has emerged as a promising technology to cost-effectively provide the scale and flexibility necessary for IoT services. In this article, we consider the wireless SDN for IoT, referred to as SDN-IoT, which is anticipated to smartly route traffic and use underutilized network resources to deliver IoT data to the cloud/Internet. However, the rapid increase of IoT devices and the subsequent massive surge of the IoT data traffic are expected to place a huge strain on the SDN-IoT. In this article, we focus on this issue and point out the importance of assigning suitable channels to each SDN-IoT switch to avoid potential network congestion. In particular, we consider how to exploit POC assignment in the SDN-IoT. However, our investigation reveals that the conventional fixed POC assignment algorithms are not viable for the highly dynamic large-scale SDN-IoT. Therefore, in this article, we propose a novel deep-learning-based intelligent POC assignment for the wireless SDN-IoT where the IoT data traffic dynamically changes. In particular, we envision two deep-learning-based strategies to predict the future IoT traffic load and to adaptively assign POCs according to predicted traffic load, respectively. Computer-based simulation results demonstrate that with the envisioned deep learning methods carried out at the SDN-IoT controller, our proposal achieves high accuracy of traffic load prediction and quick convergence of the channel assignment process. Additionally, in contrast with the conventional POC assignment algorithms, our proposal significantly improves the network performance.