An optimum network selection solution for multihomed hosts using Hopfield Networks

Jorge Espi; R.C. Atkinson; D.A. Harle; Ivan Andonovic

doi:10.1109/ICN.2010.47

An optimum network selection solution for multihomed hosts using Hopfield Networks

Jorge Espi, R.C. Atkinson, D.A. Harle, Ivan Andonovic

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

3 Citations (Scopus)

30 Downloads (Pure)

Abstract

This work introduces a Hopfield Neural Network approach to network selection for multihomed hosts which considers a range of relevant network parameters including available radio access technologies and traffic types (VoIP, video streaming, Web browsing and FTP-based). Also proposed is a novel utility function that further improves network selection. Results show that, in terms of QoS, the allocation obtained using proposed algorithm outperforms other two reference allocation schemes under a range of different scenarios.

Original language	English
Title of host publication	Ninth International Conference on Networks, 2010
Publisher	IEEE
Pages	249-254
Number of pages	6
ISBN (Print)	978-1-4244-6083-0
DOIs	https://doi.org/10.1109/ICN.2010.47
Publication status	Published - Apr 2010

Keywords

network selection algorithms
Hopfield neural networks
Hopfield networks

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10.1109/ICN.2010.47

Espi-etal-2010-An-optimum-network-selection-solution-for-multihomed-hostsAccepted author manuscript, 272 KB

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title = "An optimum network selection solution for multihomed hosts using Hopfield Networks",

abstract = "This work introduces a Hopfield Neural Network approach to network selection for multihomed hosts which considers a range of relevant network parameters including available radio access technologies and traffic types (VoIP, video streaming, Web browsing and FTP-based). Also proposed is a novel utility function that further improves network selection. Results show that, in terms of QoS, the allocation obtained using proposed algorithm outperforms other two reference allocation schemes under a range of different scenarios.",

keywords = "network selection algorithms, Hopfield neural networks, Hopfield networks",

author = "Jorge Espi and R.C. Atkinson and D.A. Harle and Ivan Andonovic",

note = "{\textcopyright} 2010 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.",

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AU - Espi, Jorge

AU - Atkinson, R.C.

AU - Harle, D.A.

AU - Andonovic, Ivan

N1 - © 2010 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.

PY - 2010/4

Y1 - 2010/4

N2 - This work introduces a Hopfield Neural Network approach to network selection for multihomed hosts which considers a range of relevant network parameters including available radio access technologies and traffic types (VoIP, video streaming, Web browsing and FTP-based). Also proposed is a novel utility function that further improves network selection. Results show that, in terms of QoS, the allocation obtained using proposed algorithm outperforms other two reference allocation schemes under a range of different scenarios.

AB - This work introduces a Hopfield Neural Network approach to network selection for multihomed hosts which considers a range of relevant network parameters including available radio access technologies and traffic types (VoIP, video streaming, Web browsing and FTP-based). Also proposed is a novel utility function that further improves network selection. Results show that, in terms of QoS, the allocation obtained using proposed algorithm outperforms other two reference allocation schemes under a range of different scenarios.

KW - network selection algorithms

KW - Hopfield neural networks

KW - Hopfield networks

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DO - 10.1109/ICN.2010.47

M3 - Conference contribution book

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SP - 249

EP - 254

BT - Ninth International Conference on Networks, 2010

PB - IEEE

ER -

An optimum network selection solution for multihomed hosts using Hopfield Networks

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