A Fuzzy Decision Tree Based Mobility Prediction Mechanism in Mobile Internet

Wang, X; Cai, S; Cheng, H; Huang, M; Cao, J

A Fuzzy Decision Tree Based Mobility Prediction Mechanism in Mobile Internet

Wang, X Cai, S Cheng, H Huang, M Cao, J

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Publication Type:: Conference
Issue Date:: 2007-03-12T22:05:48Z

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Field	Value	Language
dc.contributor.author	Wang, X	en_AU
dc.contributor.author	Cai, S	en_AU
dc.contributor.author	Cheng, H	en_AU
dc.contributor.author	Huang, M	en_AU
dc.contributor.author	Cao, J	en_AU
dc.date.accessioned	2007-03-12T22:05:48Z
dc.date.accessioned	2012-12-15T02:29:32Z
dc.date.available	2007-03-12T22:05:48Z
dc.date.available	2012-12-15T02:29:32Z
dc.date.issued	2007-03-12T22:05:48Z
dc.identifier.uri	http://hdl.handle.net/10453/19595
dc.description.abstract	Considering environment factor and user movement randomness, a fuzzy decision tree based mobility prediction mechanism in mobile Internet is proposed. Based on the instance set, an initial fuzzy decision tree is built up with fuzzy attribute and classification entropy. The tree produces fuzzy decision rules for instant mobility predictions and evolves with time. Simulation results have shown that the proposed mechanism is both feasible and effective with relatively high accuracy and low overhead of mobility prediction	en_AU
dc.format.extent	521493 bytes
dc.format.mimetype	application/pdf
dc.language.iso	en_AU
dc.relation.replaces	http://hdl.handle.net/2100/161
dc.title	A Fuzzy Decision Tree Based Mobility Prediction Mechanism in Mobile Internet	en_AU
dc.type	Conference
utslib.copyright.status	open_access

Abstract:

Considering environment factor and user movement randomness, a fuzzy decision tree based mobility prediction mechanism in mobile Internet is proposed. Based on the instance set, an initial fuzzy decision tree is built up with fuzzy attribute and classification entropy. The tree produces fuzzy decision rules for instant mobility predictions and evolves with time. Simulation results have shown that the proposed mechanism is both feasible and effective with relatively high accuracy and low overhead of mobility prediction

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http://hdl.handle.net/10453/19595