Large-scale 802.11 wireless networks data analysis based on graph clustering.

Abstract

This paper analyzes a large-scale dataset of real-world Wi-Fi operating networks, collected from more than 9,000 access points (APs) for 1 year. The APs are distributed among more than 1,200 educational centers in the context of a nation-wide one-to-one computing program, being most of them primary and secondary schools. The data corresponds to RSSI measurements between APs used to build the conflict graphs for each school Wi-Fi network. We propose a simple embedding for the Wi-Fi network conflict graphs based on classical graph features, which proves to be useful to analyze the behavior of the wireless networks, showing a high discrimination power among the different school networks. Moreover, we discuss some practical applications of the embedding. In particular, it enables to study the Wi-Fi network dynamics at each school, analyzing the conflict graphs temporal variations through clustering techniques. The presented methodology allows us to successfully separate the most stable scenarios from those with more significant variability, which therefore require more technical resources to optimize the network. Besides, we also compared the behaviour of the Wi-Fi networks of the different schools, which enable us to reuse the optimal configuration found for one school in all those sites that have similar conflict graph patterns.