Learning-based Call Admission Control Framework for QoS Management in Heterogeneous Network

Abul Bashar; Gerard Parr; Sally McClean; Scotney Bryan; Detlef Nauck

doi:10.1007/978-3-642-14306-9_11

Learning-based Call Admission Control Framework for QoS Management in Heterogeneous Network

Abul Bashar, Gerard Parr, Sally McClean, Scotney Bryan, Detlef Nauck

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Citations (Scopus)

Abstract

This paper presents a novel framework for Quality of Service (QoS) management based on the supervised learning approach, Bayesian Belief Networks (BBNs). Apart from proposing the conceptual framework, it provides solution to the problem of Call Admission Control (CAC) in the converged IP-based Next Generation Network (NGN). A detailed description of the modelling procedure and the mathematical underpinning is presented to demonstrate the applicability of our approach. Finally, the theoretical claims have been substantiated through simulations and comparative results are provided as a proof of concept.

Original language	English
Title of host publication	Networked Digital Technologies
Publisher	Springer
Pages	99-111
ISBN (Print)	978-3-642-14305-2 (Print) 978-3-642-14306-9 (Online)
DOIs	https://doi.org/10.1007/978-3-642-14306-9_11
Publication status	Published (in print/issue) - 30 Jun 2010

Keywords

Quality of Service (QoS)
Call Admission Control (CAC)
Bayesian Belief Networks (BBNs)
Next Generation Network (NGN)

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10.1007/978-3-642-14306-9_11

Cite this

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title = "Learning-based Call Admission Control Framework for QoS Management in Heterogeneous Network",

abstract = "This paper presents a novel framework for Quality of Service (QoS) management based on the supervised learning approach, Bayesian Belief Networks (BBNs). Apart from proposing the conceptual framework, it provides solution to the problem of Call Admission Control (CAC) in the converged IP-based Next Generation Network (NGN). A detailed description of the modelling procedure and the mathematical underpinning is presented to demonstrate the applicability of our approach. Finally, the theoretical claims have been substantiated through simulations and comparative results are provided as a proof of concept.",

keywords = "Quality of Service (QoS), Call Admission Control (CAC), Bayesian Belief Networks (BBNs), Next Generation Network (NGN)",

author = "Abul Bashar and Gerard Parr and Sally McClean and Scotney Bryan and Detlef Nauck",

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AU - Bashar, Abul

AU - Parr, Gerard

AU - McClean, Sally

AU - Bryan, Scotney

AU - Nauck, Detlef

PY - 2010/6/30

Y1 - 2010/6/30

N2 - This paper presents a novel framework for Quality of Service (QoS) management based on the supervised learning approach, Bayesian Belief Networks (BBNs). Apart from proposing the conceptual framework, it provides solution to the problem of Call Admission Control (CAC) in the converged IP-based Next Generation Network (NGN). A detailed description of the modelling procedure and the mathematical underpinning is presented to demonstrate the applicability of our approach. Finally, the theoretical claims have been substantiated through simulations and comparative results are provided as a proof of concept.

AB - This paper presents a novel framework for Quality of Service (QoS) management based on the supervised learning approach, Bayesian Belief Networks (BBNs). Apart from proposing the conceptual framework, it provides solution to the problem of Call Admission Control (CAC) in the converged IP-based Next Generation Network (NGN). A detailed description of the modelling procedure and the mathematical underpinning is presented to demonstrate the applicability of our approach. Finally, the theoretical claims have been substantiated through simulations and comparative results are provided as a proof of concept.

KW - Quality of Service (QoS)

KW - Call Admission Control (CAC)

KW - Bayesian Belief Networks (BBNs)

KW - Next Generation Network (NGN)

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DO - 10.1007/978-3-642-14306-9_11

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SP - 99

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BT - Networked Digital Technologies

PB - Springer

ER -

Learning-based Call Admission Control Framework for QoS Management in Heterogeneous Network

Abstract

Keywords

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