Predicting Access Points Workload in Wi-Fi Infrastructures According to Users' Behavior
David Rodriguez-Lozano, Juan Antonio Gomez-Pulido, Arturo Durán-Domínguez · 2016
In this work, we present a methodology based on matrix factorization and gradient descent algorithm to predict the access points workload based on the users' behavior in Wi-Fi wireless networks, under academic and university environments. This knowledge is very useful when it is necessary to relocate determined access points due to the changing physical environment. We start collecting data about sessions and traffic of each user in each access point during certain time. From this information, prediction models are built using techniques that have been successfully applied to learning subjects, and next applied to cases where some access points should be reinforced because they support a high workload. For experimental purposes, we have collected data from the activity of 1,517 users in 37 access points of a wireless network in the School of Technology of the University of Extremadura, Spain, during October 2015. The obtained results are qualitatively valid with regard to the previous knowledge. The purpose of this methodology proposal is to apply the obtained results to improve the network infrastructure, as well as maintain the network against changing environments, since we can predict the access points workload when a determined device is removed, according to the usual activity of the network users.