DLIRS: Deep Learning based Intelligent Routing in Software Defined IoT

Software Defined Network (SDN) is a next generation networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric values. However, IoT network’s heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because, traditional routing algorithms lack the self-adaptions, intelligence, and efficient utilization of resources. Though SDN has managed the IoT heterogeneity and complexity to some extent, owing to its centralized control and flexibility, still it lacks intelligence. To address this challenge, we proposed a model called Deep Learning based Intelligent Routing Strategy (DLIRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Moreover, it has outperformed benchmark routing algorithm (OSPF) and provided encouraging results during high dynamic traffic flow.