MASS: Mobile Autonomous Station Simulation

TL;DR

This paper introduces a GAN-based toolset for replaying wireless network traffic traces that preserve privacy and statistical properties, enabling realistic simulations across various scenarios and user counts.

Contribution

The paper presents a novel GAN-based method for generating privacy-preserving, statistically accurate wireless network traces adaptable to different user numbers and contexts.

Findings

GAN outperforms traditional statistical methods in trace generation

Generated traces retain key statistical properties and correlations

Tools are validated in Linux, Android, and NS3 simulation environments

Abstract

We propose a set of tools to replay wireless network traffic traces, while preserving the privacy of the original traces. Traces are generated by a user- and context-aware trained generative adversarial network (GAN). The replay allows for realistic traces from any number of users and of any trace duration to be produced given contextual parameters like the type of application and the real-time signal strength. We demonstrate the usefulness of the tools in three replay scenarios: Linux- and Android-station experiments and NS3 simulations. We also evaluate the ability of the GAN model to generate traces that retain key statistical properties of the original traces such as feature correlation, statistical moments, and novelty. Our results show that we beat both traditional statistical distribution fitting approaches as well as a state-of-the-art GAN time series generator across these metrics. The ability of our GAN model to generate any number of user traces regardless of the number of users in the original trace also makes our tools more practically applicable compared to previous GAN approaches. Furthermore, we present a use case where our tools were employed in a Wi-Fi research experiment.