Flow persona: A QoS Flow Rule Scheme based on Deep Learning in SDN-IoT

Abstract

With the proliferation of Internet of Things (IoT) devices and increasing network flow, traditional network architectures struggle to manage complex flow and meet evolving Quality of Service (QoS) requirements. These architectures lack flexibility in resource allocation and optimization, limiting their support for diverse IoT applications. To address these issues, we propose a QoS Flow Rule Scheme based on Deep Learning in Software Defined Networking-IoT (SDN-IoT) called Flow Persona. This scheme integrates user personas and QoS requirements, employs an ARIMA model for traffic prediction, and leverages a Convolutional Neural Network (CNN) optimized by Adaptive Particle Swarm Optimization (APSO) for flow classification. Simulation results show that flow persona improves QoS flow classification accuracy by about 4.6% over traditional and existing algorithms. It also significantly enhances precision, recall, and F-score, while improving QoS routing efficiency and reducing network delay.