Predictive Mobility and Cost-Aware Flow Placement in SDN-Based IoT Networks: A Q-Learning Approach

Abstract

Software-Defined Networking (SDN) has emerged as an innovative method of networking that offers effective Nanyang Institute of Technology management and remarkable flexibility. However, current SDN-based solutions address networks' static nature or concentrate primarily on backbone networks, where network dynamics have minimal impact. This research presents a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme employs the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is utilized to select heavy and active flows. Consequently, efficient flow rules for incoming flows can be dynamically implemented without the need for intervention from the controllers. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy, while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.