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.
Highlights
The Access Point (AP) is one of the most important communication devices present in Wi-Fi networks
Our work focuses on the connection between the number of roamings established by the users and the energy consumption in the APs
We propose a methodology for predicting the energy in the APs once one of them has been removed from the wireless network, considering the data collected previously during a large period
Summary
The Access Point (AP) is one of the most important communication devices present in Wi-Fi networks. This question can be answered by predicting the number of roamings for the new infrastructure and establishing a relationship between the number of roamings and the energy levels in the access points For this purpose, it is necessary to collect data of the network usage by a high number of network users during enough time in order to reflect the users’ behavior with the maximum accuracy. It is necessary to collect data of the network usage by a high number of network users during enough time in order to reflect the users’ behavior 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 machine learning and big data analysis. A discussion of the results is left for Section 8
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