Context-aware prediction of access points demand in Wi-Fi networks

Rodríguez-Lozano, David and Gómez-Pulido, Juan A. and Lanza-Gutiérrez, José Manuel and Durán-Domínguez, Arturo (2017). Context-aware prediction of access points demand in Wi-Fi networks. "Computer Networks", v. 117 ; pp. 52-61. ISSN 1389-1286. https://doi.org/10.1016/j.comnet.2017.01.002.

Description

Title: Context-aware prediction of access points demand in Wi-Fi networks
Author/s:
  • Rodríguez-Lozano, David
  • Gómez-Pulido, Juan A.
  • Lanza-Gutiérrez, José Manuel
  • Durán-Domínguez, Arturo
Item Type: Article
Título de Revista/Publicación: Computer Networks
Date: 22 April 2017
ISSN: 1389-1286
Volume: 117
Subjects:
Freetext Keywords: Wi-Fi networks; access point; user behavior; prediction; roaming; matrix factorization; gradient descent
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We present a methodology based on matrix factorization and gradient descent to predict the number of sessions established in the access points of a Wi-Fi network according to the users’ behavior. As the network considered in this work is monitored and controlled by software in order to manage users and resources in real time, we may consider it as a cyber-physical system that interacts with the physical world through access points, whose demands can be predicted according to users’ activity. These predictions are useful for relocating or reinforcing some access points according to the changing physical environment. In this work we propose a prediction model based on machine learning techniques, which is validated by comparing the prediction results with real user’s activity. Our experiments collected the activity of 1,095 users demanding 26,673 network sessions during one month in a Wi-Fi network composed of 10 access points, and the results are qualitatively valid with regard to the previous knowledge. We can conclude that our proposal is suitable for predicting the demand of sessions in access points when some devices are removed taking into account the usual activity of the network users.

More information

Item ID: 53220
DC Identifier: http://oa.upm.es/53220/
OAI Identifier: oai:oa.upm.es:53220
DOI: 10.1016/j.comnet.2017.01.002
Official URL: https://www.sciencedirect.com/science/article/pii/S1389128617300026
Deposited by: Archivo Digital UPM
Deposited on: 10 Dec 2018 09:35
Last Modified: 05 Jun 2019 14:46
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