Mobile user location prediction: genetic algorithm-based approach

Mobile user location prediction: genetic algorithm-based approach

Context histories, especially when recorded over a long term, offer a wide range of possibilities to enhance the services provided by ubiquitous computing system application. These possibilities include inferring of current and past user location, and selection of devices. However, the prediction o...

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Main Authors: Mantoro, Teddy, Muataz, Zubaidah, Ayu, Media Anugerah
Format: Conference or Workshop Item
Language:English
Published: 2010
Subjects:
T Technology (General)
T58.5 Information technology
Online Access:http://irep.iium.edu.my/2914/
http://irep.iium.edu.my/2914/
http://irep.iium.edu.my/2914/
http://irep.iium.edu.my/2914/1/GeneticAlg_05679444.pdf
id iium-2914
recordtype eprints
spelling iium-29142011-09-22T00:26:33Z http://irep.iium.edu.my/2914/ Mobile user location prediction: genetic algorithm-based approach Mantoro, Teddy Muataz, Zubaidah Ayu, Media Anugerah T Technology (General) T58.5 Information technology Context histories, especially when recorded over a long term, offer a wide range of possibilities to enhance the services provided by ubiquitous computing system application. These possibilities include inferring of current and past user location, and selection of devices. However, the prediction of future context based on the recorded past locations is often conceived as the ultimate challenge in exploiting the whole of context histories. This paper presents a prediction of user location using genetic algorithm and based location history in finding a number of solutions of possible location when applied to recorded context histories, framework is presented and the result are discussed. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/2914/1/GeneticAlg_05679444.pdf Mantoro, Teddy and Muataz, Zubaidah and Ayu, Media Anugerah (2010) Mobile user location prediction: genetic algorithm-based approach. In: ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications, 3 - 5 Oct 2010, Penang. http://dx.doi.org/10.1109/ISIEA.2010.5679444 doi:10.1109/ISIEA.2010.5679444
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)
T58.5 Information technology
spellingShingle T Technology (General)
T58.5 Information technology
Mantoro, Teddy
Muataz, Zubaidah
Ayu, Media Anugerah
Mobile user location prediction: genetic algorithm-based approach
description Context histories, especially when recorded over a long term, offer a wide range of possibilities to enhance the services provided by ubiquitous computing system application. These possibilities include inferring of current and past user location, and selection of devices. However, the prediction of future context based on the recorded past locations is often conceived as the ultimate challenge in exploiting the whole of context histories. This paper presents a prediction of user location using genetic algorithm and based location history in finding a number of solutions of possible location when applied to recorded context histories, framework is presented and the result are discussed.
format Conference or Workshop Item
author Mantoro, Teddy
Muataz, Zubaidah
Ayu, Media Anugerah
author_facet Mantoro, Teddy
Muataz, Zubaidah
Ayu, Media Anugerah
author_sort Mantoro, Teddy
title Mobile user location prediction: genetic algorithm-based approach
title_short Mobile user location prediction: genetic algorithm-based approach
title_full Mobile user location prediction: genetic algorithm-based approach
title_fullStr Mobile user location prediction: genetic algorithm-based approach
title_full_unstemmed Mobile user location prediction: genetic algorithm-based approach
title_sort mobile user location prediction: genetic algorithm-based approach
publishDate 2010
url http://irep.iium.edu.my/2914/
http://irep.iium.edu.my/2914/
http://irep.iium.edu.my/2914/
http://irep.iium.edu.my/2914/1/GeneticAlg_05679444.pdf
first_indexed 2023-09-18T20:10:36Z
last_indexed 2023-09-18T20:10:36Z
_version_ 1777407451430125568

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