Extreme learning machine for user location prediction in mobile environment

Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal s...

Full description

Bibliographic Details
Main Authors:	Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media Anugerah, Md. Tap, Abu Osman
Format:	Article
Language:	English
Published:	Emerald Group Publishing Limited 2011
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/3358/ http://irep.iium.edu.my/3358/ http://irep.iium.edu.my/3358/ http://irep.iium.edu.my/3358/4/Extreme_learning_IJPervasiveCnC.pdf

id	iium-3358
recordtype	eprints
spelling	iium-33582013-07-31T01:34:58Z http://irep.iium.edu.my/3358/ Extreme learning machine for user location prediction in mobile environment Mantoro, Teddy Olowolayemo, Akeem Olatunji, Sunday O. Ayu, Media Anugerah Md. Tap, Abu Osman T Technology (General) Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. Design/methodology/approach – Prediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k-Nearest Neighbor approaches. Findings – WiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k-Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k-Nearest Neighbor approach. Originality/value – A new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ). Emerald Group Publishing Limited 2011 Article PeerReviewed application/pdf en http://irep.iium.edu.my/3358/4/Extreme_learning_IJPervasiveCnC.pdf Mantoro, Teddy and Olowolayemo, Akeem and Olatunji, Sunday O. and Ayu, Media Anugerah and Md. Tap, Abu Osman (2011) Extreme learning machine for user location prediction in mobile environment. International Journal of Pervasive Computing and Communications, 7 (2). pp. 162-180. ISSN 1742-7371 http://dx.doi.org/10.1108/17427371111146446 doi:10.1108/17427371111146446
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Mantoro, Teddy Olowolayemo, Akeem Olatunji, Sunday O. Ayu, Media Anugerah Md. Tap, Abu Osman Extreme learning machine for user location prediction in mobile environment
description	Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. Design/methodology/approach – Prediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k-Nearest Neighbor approaches. Findings – WiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k-Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k-Nearest Neighbor approach. Originality/value – A new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ).
format	Article
author	Mantoro, Teddy Olowolayemo, Akeem Olatunji, Sunday O. Ayu, Media Anugerah Md. Tap, Abu Osman
author_facet	Mantoro, Teddy Olowolayemo, Akeem Olatunji, Sunday O. Ayu, Media Anugerah Md. Tap, Abu Osman
author_sort	Mantoro, Teddy
title	Extreme learning machine for user location prediction in mobile environment
title_short	Extreme learning machine for user location prediction in mobile environment
title_full	Extreme learning machine for user location prediction in mobile environment
title_fullStr	Extreme learning machine for user location prediction in mobile environment
title_full_unstemmed	Extreme learning machine for user location prediction in mobile environment
title_sort	extreme learning machine for user location prediction in mobile environment
publisher	Emerald Group Publishing Limited
publishDate	2011
url	http://irep.iium.edu.my/3358/ http://irep.iium.edu.my/3358/ http://irep.iium.edu.my/3358/ http://irep.iium.edu.my/3358/4/Extreme_learning_IJPervasiveCnC.pdf
first_indexed	2023-09-18T20:11:10Z
last_indexed	2023-09-18T20:11:10Z
_version_	1777407486938054656

Extreme learning machine for user location prediction in mobile environment

Similar Items