Novel distributed call admission control solution based on machine learning approach

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The advent of IP-based Next Generation Network (NGN) and its guaranteed QoS promise has attracted significant attention from both service providers and subscribers. However, to fulfil the said promise, there is a need to provide effective Call Admission Control (CAC) based QoS provisioning solutions which are autonomous, intelligent and scalable.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages871-881
Number of pages11
ISBN (Print)978-1-4244-9219-0 (print)
DOIs
Publication statusPublished - 18 Aug 2011
EventIFIP/IEEE International Symposium on Integrated Network Management - Dublin, Ireland
Duration: 18 Aug 2011 → …

Other

OtherIFIP/IEEE International Symposium on Integrated Network Management
Period18/08/11 → …

Bibliographical note

Reference text: General overview of NGN, ITU-T Recommendation Y, 2001, Dec, 2004,

R. Boutaba, J, P. Martin-Flatin, J. L. Hellerstein, R. H. Katz, G. Pavlou, L. Chin-Tau, "Recent advances in autonomic communications [Guest Editorial]," In IEEE Journal on Selected Areas in Communications, vol. 28, no. 1, pp. 1-3, Jan. 2010.

C. Yun and H. Perros, "QoS control for NGN: A Survey of Techniques," In Journal of Network and Systems Management, vol. 18, no. 4, pp. 447-461, Feb. 2010.

E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004.

Resource and admission control functions in next generation networks, ITU-T Recommendation Y.2111, Nov. 2008.

D. Liu, Y. Zhang, H. Zhang, "A self-learning call admission control scheme for COMA cellular networks," In IEEE Transactions on Neural Networks, vol.16, no. 5, pp. 1219-1228, Sep. 2005.

F. R. Yu, V. W. S. Wong, V. C. M. Leung, "A new QoS provisioning method for adaptive multimedia in wireless networks," In IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1899-1909, May 2008.

P. Guo, M. Zhang, Y. Jiang, J. Ren, "Policy-based QoS control using call admission control and SVM," In Proc. of 2nd International Conference on Pervasive Computing and Applications (ICPCA 2007), pp. 685-688, Jul. 2007.

B. Rong, Y. Qian, K. Lu, R. Q. Hu, M. Kadoch, "Mobile agent based handoff in wireless mesh networks: architecture and call admission control," In IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4565-4575, Oct. 2009.

A. Bashar, G. P. Parr, S. I. McClean, B. W. Scotney, D. Nauck, "Learning-based call admission control framework for QoS management in heterogeneous networks," In Proc. of Springer LNCS CCIS series, 2nd International Conference on Networked Digital Technologies (NDT 2010), vol. II, pp. 99-111, Jul. 2010.

A. Bashar, G. P. Parr, S. I. McClean, B. W. Scotney, D. Nauck, "Machine Learning based call admission control approaches: A comparative study," in Proc. of IEEE/IFIP 6th International Conference on Network and Service Management (CNSM 2010), Oct. 2010.

K. B. Laskey, "MEBN: A Language for first-order Bayesian knowledge bases," Artificial Intelligence, vol. 172, no. 2-3, pp. 140-178, Feb. 2008.

J. Qi, F. Wu, L. Li, H. Shu, "Artificial intelligence applications in the telecommunication industry," in Expert Systems, vol. 24, no. 4, pp. 271-291, Sep. 2007.

Opnet Modeler 16.0, http://www.opneLcom

Hugin Researcher 7.3, http://www.hugin.com

Keywords

  • Bayesian methods
  • Call admission control
  • Delay
  • Machine learning
  • Next generation networking
  • Predictive models
  • Support vector machines

Fingerprint

Dive into the research topics of 'Novel distributed call admission control solution based on machine learning approach'. Together they form a unique fingerprint.

Cite this