AI-Enabled Learning Architecture Using Network Traffic Traces over IoT Network: A Comprehensive Review

Abstract

WiFi and private 5G networks, commonly referred to as P5G, provide Internet of Things (IoT) devices the ability to communicate at fast speeds, with low latency and with a high capacity. Will they coexist and share the burden of delivering a connection to devices at home, on the road, in the workplace, and at a park or a stadium? Or will one replace the other to manage the increase in endpoints and traffic in the enterprise, campus, and manufacturing environments? In this research, we describe IoT device testbeds to collect network traffic in a local area network and cyberspace including beyond 5G/6G network traffic traces at different layers. We also describe research problems and challenges, such as traffic classification and traffic prediction by the traffic traces of devices. An AI-enabled hierarchical learning architecture for the problems above using sources like network packets, frames, and signals from the traffic traces with machine learning models is also presented.