An Improved Artificial Fish Swarm Algorithm Based Handover Decision Scheme with Always-Best-Connected Supported

Article Preview

Abstract:

Oriented to the actual requirements of roaming among heterogeneous networks, a handover decision scheme with ABC (Always Best Connected) supported was proposed in this paper. Network application types, access network and mobile terminal models were described. Plenty of factors such as access network conditions, application requirements, users preference in coding systems and providers, mobile terminals condition etc were considered comprehensively, an optimal handover scheme of N terminals among M access networks was designed based on improved artificial fish swarm algorithm. By gaming analysis, Pareto optimum under Nash equilibrium of user utility and network utility was achieved or approached to the found solution. Simulation results showed that the proposed scheme has better performance than existed schemes.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2259-2267

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] E. Gustafsson, and A. Jonsson, Always best connected, IEEE Wireless Communications, vol. 10, pp.49-55, Febuary (2003).

DOI: 10.1109/mwc.2003.1182111

Google Scholar

[2] B. Briscoe, V. Darlagiannis, O. Heckman, H. Oliver, V. Siris, and D. Songhurst, et al, A market managed multi-service Internet, Computer Communications, vol. 26, pp.404-414, March (2003).

DOI: 10.1016/s0140-3664(02)00158-5

Google Scholar

[3] X. Wang, H. Cheng, P. Qin, M. Huang, and L. Guo, ABC supported handoff decision scheme based on population migration, Applications of Evolutionary Computation, LNCS, Vol. 6025, pp.111-120, April (2010).

DOI: 10.1007/978-3-642-12242-2_12

Google Scholar

[4] K. C. Zhu, and M. Y. Jiang, An improved artificial fish swarm algorithm based on chaotic search and feedback strategy, IEEE, International Conference on Computational Intelligence and Software Engineering (CISE 2009), Jinan, pp.1-4, December, (2009).

DOI: 10.1109/cise.2009.5366958

Google Scholar

[5] An architecture for differentiated services, Internet RFC 2475, (1998).

Google Scholar

[6] End-user multimedia QoS categories, ITU-T G. 1010, (2001).

Google Scholar

[7] X. Wang, H. Cheng, K. Li, J. Li, and J. Sun, A cross-layer optimization based integrated routing and grooming algorithm for green multi-granularity transport networks, Journal of Parallel Distributed Computing, vol. 73, no. 6, p.807 – 822, June (2013).

DOI: 10.1016/j.jpdc.2013.02.010

Google Scholar

[8] X. Wang, H. Cheng, and M. Huang, Multi-robot navigation based QoS routing in self-organizing network, Engineering Applications of Artificial Intelligence, vol. 26, no. 1, p.262 – 272, January (2013).

DOI: 10.1016/j.engappai.2012.01.008

Google Scholar

[9] X. Li, Z. Shao, J. Qian, An optimizing method based on autonomous animats: fish-swarm algorithm, Systems Engineering – Theory & Practice, vol. 22, pp.32-38, November 2002. (in Chinese with English Abstract).

Google Scholar

[10] J. R. Gallardo-Medina, U. Pineda-Rico, and E. Stevens-Navarro, VIKOR method for vertical handoff decision in beyond 3G wireless networks, Proc. of the 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2009), Toluca, Mexico, pp.1-5, January (2009).

DOI: 10.1109/iceee.2009.5393320

Google Scholar

[11] C. J. Chang, T. L. Tsai, and Y. H. Chen, Utility and game-theory based network selection scheme in heterogeneous wireless networks, Proc. of the IEEE Wireless Communication and Networking Conference (WCNC 2009), Budapest, Hungary, pp.2846-2850, April (2009).

DOI: 10.1109/wcnc.2009.4918016

Google Scholar