Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour
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 t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-08-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/7/8/825 |
| _version_ | 1819149541884035072 |
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| author | David Rodriguez-Lozano Juan A. Gomez-Pulido Jose M. Lanza-Gutierrez Arturo Duran-Dominguez Broderick Crawford Ricardo Soto |
| author_facet | David Rodriguez-Lozano Juan A. Gomez-Pulido Jose M. Lanza-Gutierrez Arturo Duran-Dominguez Broderick Crawford Ricardo Soto |
| author_sort | David Rodriguez-Lozano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T14:03:15Z |
| format | Article |
| id | doaj.art-50635f17f8a440df9d166a26b53838d3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-22T14:03:15Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-50635f17f8a440df9d166a26b53838d32022-12-21T18:23:21ZengMDPI AGApplied Sciences2076-34172017-08-017882510.3390/app7080825app7080825Energy Prediction of Access Points in Wi-Fi Networks According to Users’ BehaviourDavid Rodriguez-Lozano0Juan A. Gomez-Pulido1Jose M. Lanza-Gutierrez2Arturo Duran-Dominguez3Broderick Crawford4Ricardo Soto5Escuela Polítécnica, Universidad de Extremadura, 10003 Cáceres, SpainEscuela Polítécnica, Universidad de Extremadura, 10003 Cáceres, SpainCentro de Electrónica Industrial, Universidad Politécnica de Madrid, 28006 Madrid, SpainEscuela Polítécnica, Universidad de Extremadura, 10003 Cáceres, SpainEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, ChileSome 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.https://www.mdpi.com/2076-3417/7/8/825Wi-Fi networksenergyaccess pointpredictionroamingsrecommender systems |
| spellingShingle | David Rodriguez-Lozano Juan A. Gomez-Pulido Jose M. Lanza-Gutierrez Arturo Duran-Dominguez Broderick Crawford Ricardo Soto Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour Applied Sciences Wi-Fi networks energy access point prediction roamings recommender systems |
| title | Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour |
| title_full | Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour |
| title_fullStr | Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour |
| title_full_unstemmed | Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour |
| title_short | Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour |
| title_sort | energy prediction of access points in wi fi networks according to users behaviour |
| topic | Wi-Fi networks energy access point prediction roamings recommender systems |
| url | https://www.mdpi.com/2076-3417/7/8/825 |
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