Mobile terminal with multiradios is getting common nowadays with the presence of heterogeneous wireless networks such as 3G, WiMAX, and WiFi. That Network selection mechanism plays an important role in ensuring mobile terminals are always connected to the most suitable network. In this paper, we introduce and evaluate the performance of load distribution model to facilitate better network selection. We focus on the optimization of network resource utilization using the particle swarm optimizer (PSO) with the objective to distribute the system load according to the various conditions of the heterogeneous networks in order to achieve minimum system cost. Simulation results showed that the proposed approach outperformed the conventional iterative algorithm by a cost improvement of 7.24% for network size of 1000 mobile terminals using 10 particles. 1. Introduction There has been a drastic and huge development in both mobile technologies such as global system for mobile communications (GSM), general packet radio service (GPRS), and universal mobile telecommunications system (UMTS) which promise high mobility, wide coverage, but low bandwidth rate, as well as on other wireless technologies such as wireless fidelity (WiFi) and worldwide interoperability for microwave access (WiMAX) which offer faster rates at lower cost but suffered from limited mobility and coverage. The different characteristics of these mobile/wireless technologies help compensating for coverage, mobility, bandwidth, and speed, and this helps meeting the requirements due to the increase of user demands in a complementary manner [1]. It is therefore believed that the future network infrastructure will consist of coverage overlapping of heterogeneous networks [2], where multiradios mobile devices could seamlessly and conveniently access to any network in a ubiquitous manner according to the concept of always best connected (ABC) [3]. The challenge to ubiquitous access to any network lies on an efficient and effective mobility management framework which initially focused on enabling seamless vertical handover across heterogeneous networks due to user mobility. Recently, vertical handover is also considered as proactive means to system performance improvement [4, 5]. Realizing a seamless and ubiquitous network access heavily depends on the second phase in vertical handover process called handover decision, which determines and selects one of the most optimal alternative networks to connect to. The selection of network is usually based on parameters such as signal strength, network conditions,
References
[1]
J. S. Kim, E. Serpedin, D. R. Shin, and K. Qaraqe, “Handoff triggering and network selection algorithms for load-balancing handoff in CDMA-WLAN integrated networks,” Eurasip Journal on Wireless Communications and Networking, vol. 2008, Article ID 136939, 2008.
[2]
M. M. Alkhawlani and K. A. Alsalem, “Radio network selection for tight-coupled wireless networks,” in Proceedings of the 7th International Conference on Informatics and Systems (INFOS '10), Cairo, Egypt, March 2010.
[3]
N. Passas, S. Paskalis, A. Kaloxylos et al., “Enabling technologies for the always best connected concept,” Wireless Communications and Mobile Computing, vol. 6, no. 4, pp. 523–540, 2006.
[4]
X. Liu, V. O. K. Li, and P. Zhang, “Joint radio resource management through vertical handoffs in 4G networks,” in Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '06), December 2006.
[5]
A. E. M. Taha, H. S. Hassanein, and H. T. Mouftah, “Vertical handoffs as a radio resource management tool,” Computer Communications, vol. 31, no. 5, pp. 950–961, 2008.
[6]
Y. C. Yee, K. N. Choong, A. L. Y. Low, and S. W. Tan, “SIP-based proactive and adaptive mobility management framework for heterogeneous networks,” Journal of Network and Computer Applications, vol. 31, no. 4, pp. 771–792, 2008.
[7]
X. Jiang, Mobility management in next generation all-IP based wireless systems [Ph.D. thesis], Georgia Institute of Technology, Atlanta, Ga, USA, 2004.
[8]
H. Jia, Z. Zhang, P. Cheng, H. H. Chen, and S. Li, “Study on network selection for next-generation heterogeneous wireless networks,” in Proceedings of IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '06), September 2006.
[9]
K. N. Choong, V. S. Kesavan, S. L. Ng, F. de Carvalho, A. L. Y. Low, and C. Maciocco, “SIP-based IEEE802.21 media independent handover—A BT Intel collaboration,” BT Technology Journal, vol. 25, no. 2, pp. 219–230, 2007.
[10]
P. Si, F. R. Yu, H. Ji, and V. C. M. Leung, “Optimal network selection in heterogeneous wireless multimedia networks,” in Proceedings of IEEE International Conference on Communications (ICC '09), Dresden, Germany, June 2009.
[11]
B. Li, W. Shi, and Y. Zhao, “A load balancing algorithm based on dividing IP flow for high-speed traffic over heterogeneous wireless networks,” in Proceedings of the 3rd International Congress on Image and Signal Processing (CISP '10), pp. 4294–4298, Yantai, China, October 2010.
[12]
W. Song and W. Zhuang, “Multi-service load sharing for resource management in the cellular/WLAN integrated network,” IEEE Transactions on Wireless Communications, vol. 8, no. 2, pp. 725–735, 2009.
[13]
J. Xu, Y. Jiang, A. Perkis, and E. Khloussy, “Multi-service load balancing in a heterogeneous network with vertical handover,” in Proceedings of the 1st European Teletraffic Seminar, Poznan, Poland, 2011.
[14]
W. Song, W. Zhuang, and Y. Cheng, “Load balancing for cellular/WLAN integrated networks,” IEEE Network, vol. 21, no. 1, pp. 27–33, 2007.
[15]
M. M. Alkhawlani and A. A. Hussein, “Intelligent radio network selection for next generation networks,” in Proceedings of the 7th International Conference on Informatics and Systems (INFOS '10), Cairo, Egypt, March 2010.
[16]
D. Niyato and E. Hossain, “Dynamics of network selection in heterogeneous wireless networks: an evolutionary game approach,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 2008–2017, 2009.
[17]
H. J. Wang, R. H. Katz, and J. Giese, “Policy-enabled handoffs across heterogeneous wireless networks,” in Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA '99), pp. 51–61, February 1999.
[18]
X. Wu, S. Lei, J. Yang, H. Xu, J. Cho, and S. Lee, “Swarm based sensor deployment optimization in Ad Hoc sensor networks,” in Proceedings of the 2nd International Conference on Embedded Software and Systems, pp. 533–541, Xi'an, China, 2005.
[19]
S. Pandey, L. Wu, S. M. Guru, and R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments,” in Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA '10), pp. 400–407, April 2010.
[20]
M. Khodier and M. Al-Aqeel, “Linear and circular array optimization: a study using particle swarm intelligence,” Progress In Electromagnetics Research B, no. 15, pp. 347–373, 2009.
[21]
S. Deb, C. J. Singh, N. B. Singh, A. K De, and S. K. Sarkar, “Parameter optimization of quantum well nanostructure: a PSO and GA based comparitive study,” International Journal of Latest Trends in Computing, vol. 35, no. 1, pp. 35–40, 2010.
[22]
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 1, pp. 1942–1948, December 1995.
[23]
M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002.
[24]
C. F. Teo, Y. C. Foo, S. F. Chien, A. L. Y. Low, A. H. You, and G. Casta？ón, “Wavelength converters placement in all optical networks using particle swarm optimization,” Photonic Network Communications, vol. 10, no. 1, pp. 23–37, 2005.
