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Appl. Sci. 2017, 7(8), 825; doi:10.3390/app7080825

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

1
Escuela Polítécnica, Universidad de Extremadura, 10003 Cáceres, Spain
2
Centro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain
3
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
*
Author to whom correspondence should be addressed.
Received: 22 June 2017 / Revised: 30 July 2017 / Accepted: 9 August 2017 / Published: 11 August 2017
(This article belongs to the Special Issue Green Wireless Networks)
View Full-Text   |   Download PDF [859 KB, uploaded 11 August 2017]   |  

Abstract

Some maintenance tasks in Wi-Fi networks may involve removing an access point due to several reasons. As a result, the new infrastructure registers a different number of roamings in the access points according to the users’ behaviour, with a certain energy impact added to the consumption caused by the own operations of the devices. This energy effect should be understood in order to tackle the measures aimed at planning the infrastructure deployment. In this work, we propose a methodology to predict the energy consumption in the access points of a Wi-Fi network when we remove a particular device, based on a twofold support. We predict the number of roamings following a method previously validated; on the other hand, we assess the relationship between roamings and energy in the full infrastructure, using the data collected from a high number of network users during a given time in order to reflect the users’ behaviour with the maximum accuracy. From this knowledge, we can infer the energy prediction for a different environment where the roamings are predicted using techniques based on recommender systems and machine learning. View Full-Text
Keywords: Wi-Fi networks; energy; access point; prediction; roamings; recommender systems Wi-Fi networks; energy; access point; prediction; roamings; recommender systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rodriguez-Lozano, D.; Gomez-Pulido, J.A.; Lanza-Gutierrez, J.M.; Duran-Dominguez, A.; Crawford, B.; Soto, R. Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour. Appl. Sci. 2017, 7, 825.

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