Elitist Distance Based Pareto Genetic Unicast Routing Scheme with Always Best Connected Support

Article Preview

Abstract:

In this paper, an ABC (Always Best Connected) supported QoS (Quality of Service) unicast routing scheme is proposed. In the proposed scheme, in order to overcome difficulties on accurately measuring network link parameter values and exactly expressing user QoS requirements, the knowledge of the fuzzy mathematics and probability theory is introduced, and the range is used to describe these parameters; both of the users utility and the network providers utility are computed, and gaming analysis is taken to deal with profits of both the user and the network provider; EDPGA (Elitist Distance-based Pareto Genetic Algorithm) is used to find the specific QoS unicast path with Pareto optimum under Nash Equilibrium among all parties utilities achieved or approached. While comparing with unicast routing scheme based on Dijkstra and the game theory based fuzzy unicast QoS routing scheme, simulation results show that this proposed scheme is both feasible and effective.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2245-2253

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. J. Jonsson. Always best connected. IEEE Wireless Commun. vol. 10(1), pp.49-55, Feb (2003).

DOI: 10.1109/mwc.2003.1182111

Google Scholar

[2] T. B. Zahariadis, K. D. Vaxevankis, and C. P. Tsantilas. Global roaming in next-generation networks. IEEE Commun. Mag. vol. 40(2), pp.145-151, Feb (2002).

DOI: 10.1109/35.983921

Google Scholar

[3] G. Fodor, A. Eriksson, and A. Tuoriniemi. Providing quality of service in always best connected networks. IEEE Commun. Mag. vol. 41(7), pp.154-163, July (2003).

DOI: 10.1109/mcom.2003.1215652

Google Scholar

[4] B. Briscoe, and V. Oliver. A market managed multi-service Internet. Comput. Commun. vol. 26(4), pp.404-414, Feb (2003).

Google Scholar

[5] Z. Wang, J. Crowcroft. Quality of service routing for supporting multimedia applications. IEEE J. Sel. Areas Commun. vol. 14(7), pp.1288-1294, Sep (1996).

DOI: 10.1109/49.536364

Google Scholar

[6] M. Heydarian. A high performance optimal dynamic routing algorithm with unicast multichannel QoS guarantee in communication systems. J. Supercomput. Vol. 63(1), pp.315-344, Jan (2012).

DOI: 10.1007/s11227-011-0723-0

Google Scholar

[7] P. Khadivi, S. Samavi, and T. Todd. Multi-constraint QoS routing using a new single mixed metrics. Journal of Network and Computer Applications. vol. 31(4), pp.656-676, Jun (2004).

DOI: 10.1016/j.jnca.2007.11.004

Google Scholar

[8] Nie, R., Zhou, D., Zhao, D. and Tan, Y. 2010. CPCNN and its application to multiple constrained QoS route. J. Commun. 31, 1 (Jan. 2010), 65-72.

Google Scholar

[9] X. Wang, R. Sun, and M. Huang. organizational evolution-based ABC supported unicast routing scheme. Computer Science. Vol. 38(10), pp.34-38, Oct (2011).

Google Scholar

[10] Q. Du, J. Zhu, and E. Zhang. A novel ant-colony optimized QoS routing algorithm based on multiple transferring strategies for tactical MANETs. Journal of National University of Defense Technology. vol. 34(1), pp.107-114, Feb (2012).

Google Scholar

[11] X. Wang, R. Zou, and M. Huang. A flexible QoS unicast routing scheme based on utility and QGA. Computer engineering & Science. Vol. 32(2), pp.1-2, (2010).

Google Scholar

[12] K. Yang, and Y. Wu. QoS-aware routing in emerging heterogeneous wireless networks. IEEE Commun. Mag. Vol. 45(2), pp.74-80, Feb (2007).

DOI: 10.1109/mcom.2007.313398

Google Scholar

[13] G. L. Xue, A. Sen, and W. Zhang. Finding a path subject to many additive QoS constraints. IEEE / ACM Transactions on Networing. Vol. 15(1), pp.201-211, Feb (2007).

DOI: 10.1109/tnet.2006.890089

Google Scholar

[14] X. Wang, J. Wang, and M. Huang. A hunting search based trust worthy QoS routing algorithm. Journal of Northeastern University. Vol. 33(10), pp.1385-1389, Oct (2012).

Google Scholar

[15] A. Ye, and J. Wu. A Novel Particle Swarm Algorithm to Optimize QoS Unicast Routing. Advanced Materials Research. Vol. 230-232, pp.377-383, May (2011).

DOI: 10.4028/www.scientific.net/amr.230-232.377

Google Scholar

[16] R. Leela, N. Thanulekshmi. S. Selvakumar. Multi-constraint Qos Unicast Routing Using Genetic Algorithm (MURUGA). Applied Soft Computing Journal. Vol. 11(2), pp.1753-1761, Mar (2011).

DOI: 10.1016/j.asoc.2010.05.018

Google Scholar

[17] Mohamed. A, Adel. B, Rion. M, Habib. Y, and Abdelfettah. B. A new scalable multicast routing algorithm for interactive real-time applications [J]. Personal and Ubiquitous Computing. Vol . 15, pp.833-844, (2011).

DOI: 10.1007/s00779-011-0370-8

Google Scholar

[18] Rabindra. G, Seshadri. M. A token-based routing mechanism for GMPLS-controlled WDM networks. Optical Switching and Networking. vol. 9, pp.170-178, (2012).

DOI: 10.1016/j.osn.2011.11.003

Google Scholar

[19] N. Ali, H. Michael, and W. Ning. An ISP and End-User Cooperative Intradomain Routing Algorithm . 2012 IEEE Symposium on Computers and Communications. (2012).

DOI: 10.1109/iscc.2012.6249310

Google Scholar

[20] M. Damanafshan, E. Khosrowshahi-Asl, and M. Abbaspour. GASANT: An ant-inspired least-cost QoS multicast routing approach based on genetic and simulated annealing algorithms. International Journal of Computers Communications & Control. vol. 3, pp.417-431, (2012).

DOI: 10.15837/ijccc.2012.3.1384

Google Scholar

[21] D. Zhang, and S. Huang. An improved elistist distance-based pareto genetic algorithm. Control and Decision. Vol. 19(4), pp.465-467, Apr (2004).

Google Scholar

[22] D. Xiao, J. Feng, and Q. Zhou. Gauss reputation framework for senor networks, J. Commun. Vol. 29(3), pp.47-53, Mar (2008).

Google Scholar

[23] Q. Shi. Game theory. 2000. ShangHai: Shanghai University of Finance Economics Pres. 11-81.

Google Scholar

[24] Z. Li. A necessary and sufficient condition of distinguishing pure strategy Nash equilibrium existence in static games. Journal of AnHui University. Vol. 28(6), pp.10-14, Nov 2004.

Google Scholar

[25] D. Kalyanmoy. Multi-objective optimization using evolutionary algorithms. Chichester, U.K.: Wiley. pp.241-249.

Google Scholar

[26] D. Lei, and Z. Wu. Crowding Measure Based Multi_Objective Evolutionary Algorithm. Chinese Journal of Computers. Vol. 28(8), pp.1320-1326, Aug (2005).

Google Scholar

[27] L. Xu, B. Pang, and Y. Zhao. NS and Network Simulation. Post & Telecom Press. pp.1-9.

Google Scholar

[28] E. W. Dijkstra. A note on two problems in connection with graph. Numerical Mathematics. Vol. 1(5), pp.269-271, Jan (1959).

Google Scholar