Proactive Cellular Network Faults Prediction Through Mobile Intelligent Agent Technology

Kogeda, P; Agbinya, JI

Proactive Cellular Network Faults Prediction Through Mobile Intelligent Agent Technology

Kogeda, P Agbinya, JI

Permalink

Publisher:: IEEE CPS
Publication Type:: Conference Proceeding
Citation:: Proceedings The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, 2007, pp. 55 - 60
Issue Date:: 2007-01

Closed Access

	Filename	Description	Size
	2006010826.pdf		795.84 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Kogeda, P	en_US
dc.contributor.author	Agbinya, JI	en_US
dc.contributor.editor	Kogeda, OP	en_US
dc.contributor.editor	Agbinya, JI	en_US
dc.date	2007-08-27	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	Proceedings The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, 2007, pp. 55 - 60	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2461
dc.description.abstract	Cellular network faults prediction models using mobile intelligent agent are presented in this paper. Cellular net-works are uncertain and dynamic in their behaviours and therefore we use different artificial intelligent techniques to develop platform independent, autonomous, reasoning and robust agents that can report on any unforeseen anomaly within the cellular network service provider. The specific design and implementation is done using Java Agent DE-velopment Framework (JADE). The partial results ob-tained from the experiments conducted are presented and discussed in this paper.	en_US
dc.publisher	IEEE CPS	en_US
dc.relation.ispartof	Proceedings The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications	en_US
dc.relation.ispartof	International conference on Wireless Broadband and Ultra Wideband Communication	en_US
dc.relation.isbasedon	10.1109/AUSWIRELESS.2007.62	en_US
dc.rights	© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Proactive Cellular Network Faults Prediction Through Mobile Intelligent Agent Technology	en_US
dc.type	Conference Proceeding
utslib.location	New York, USA	en_US
utslib.location.activity	Sydney, Australia	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	090609 Signal Processing	en_US
utslib.for	090699 Electrical and Electronic Engineering not elsewhere classified	en_US
dc.location.activity	Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	New York, USA	en_US
pubs.start-date	2007-08-27	en_US

Abstract:

Cellular network faults prediction models using mobile intelligent agent are presented in this paper. Cellular net-works are uncertain and dynamic in their behaviours and therefore we use different artificial intelligent techniques to develop platform independent, autonomous, reasoning and robust agents that can report on any unforeseen anomaly within the cellular network service provider. The specific design and implementation is done using Java Agent DE-velopment Framework (JADE). The partial results ob-tained from the experiments conducted are presented and discussed in this paper.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/2461