Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining

In an IEEE 802.11 Infrastructure network, as the mobile node is movingfrom one access pointto another, the resource allocation and smooth hand offmay be a problem. Ifsome reliable prediction is done on mobile node's next move, then resources can be allocated optimally as the mobile node moves...

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Main Authors: Biju, Issac, Khairuddin, Ab Hamid, C.E., Tan
Format: Article
Language:English
Published: Journal of IT in Asia 2010
Subjects:
Online Access:http://ir.unimas.my/id/eprint/780/
http://ir.unimas.my/id/eprint/780/1/Hybrid%20mobility%20prediction%20of%20802.11%20Infrastructure%20nodes%20by%20location%20Tracking%20and%20data%20mining.pdf
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author Biju, Issac
Khairuddin, Ab Hamid
C.E., Tan
author_facet Biju, Issac
Khairuddin, Ab Hamid
C.E., Tan
author_sort Biju, Issac
building UNIMAS Institutional Repository
collection Online Access
description In an IEEE 802.11 Infrastructure network, as the mobile node is movingfrom one access pointto another, the resource allocation and smooth hand offmay be a problem. Ifsome reliable prediction is done on mobile node's next move, then resources can be allocated optimally as the mobile node moves around. This would increase the performance throughput ofwireless network. We plan to investigate on a hybrid mobility prediction scheme that uses location tracking and data mining to predict thefuture path ofthe mobile node. We also propose a secure version ofthe same scheme. Through simulation and analysis, we present the prediction accuracy ofour proposal.
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institution Universiti Malaysia Sarawak
institution_category Local University
language English
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publishDate 2010
publisher Journal of IT in Asia
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spelling unimas-7802023-03-22T02:44:01Z http://ir.unimas.my/id/eprint/780/ Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining Biju, Issac Khairuddin, Ab Hamid C.E., Tan Q Science (General) T Technology (General) In an IEEE 802.11 Infrastructure network, as the mobile node is movingfrom one access pointto another, the resource allocation and smooth hand offmay be a problem. Ifsome reliable prediction is done on mobile node's next move, then resources can be allocated optimally as the mobile node moves around. This would increase the performance throughput ofwireless network. We plan to investigate on a hybrid mobility prediction scheme that uses location tracking and data mining to predict thefuture path ofthe mobile node. We also propose a secure version ofthe same scheme. Through simulation and analysis, we present the prediction accuracy ofour proposal. Journal of IT in Asia 2010 Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/780/1/Hybrid%20mobility%20prediction%20of%20802.11%20Infrastructure%20nodes%20by%20location%20Tracking%20and%20data%20mining.pdf Biju, Issac and Khairuddin, Ab Hamid and C.E., Tan (2010) Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining. Journal of IT in Asia, 3 (1).
spellingShingle Q Science (General)
T Technology (General)
Biju, Issac
Khairuddin, Ab Hamid
C.E., Tan
Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining
title Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining
title_full Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining
title_fullStr Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining
title_full_unstemmed Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining
title_short Hybrid mobility prediction of 802.11 Infrastructure nodes by location Tracking and data mining
title_sort hybrid mobility prediction of 802.11 infrastructure nodes by location tracking and data mining
topic Q Science (General)
T Technology (General)
url http://ir.unimas.my/id/eprint/780/
http://ir.unimas.my/id/eprint/780/1/Hybrid%20mobility%20prediction%20of%20802.11%20Infrastructure%20nodes%20by%20location%20Tracking%20and%20data%20mining.pdf