Abstract
The future mobile terminal will be dependent on the multiple wireless access technology simultaneously for accessing Internet to offer best Internet connectivity to the user. But providing such interworking among wireless heterogeneous networks and routing the selected traffic to particular wireless interface is a key challenge. Currently, existing algorithms are simple and proprietary, and there is no support to route the specific application traffic automatically. The proposed decision algorithm finds the optimal network by combining fuzzy logic system with multiple-attribute decision-making and uses naïve Bayes classifier to classify the application traffic to route into appropriate interface to reduce the service cost. The performance analysis shows that the proposed algorithm efficiently uses the network resources by maintaining active connection simultaneously with 3G and Wi-Fi. It routes 71.99 % of application traffic using Wi-Fi network and 28.008 % of application traffic using UMTS network to reduce the service cost and to reduce network load on the cellular operator.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
B. Hu, Y. Shen, Machine learning based network traffic classification: a survey. J. Inf. Comput. Sci. 9, 3161–3170 (2012)
T.T.T. Nguyen, G. Armitage, A survey of techniques for internet traffic classification using machine learning. Commun. Surv. Tutorials IEEE 10(4), 56–76 (2008)
A. Dainotti, A. Pescape, K.C. Claffy, Issues and future directions in traffic classification. Netw. IEEE 26(1), 35–40 (2012)
J. Erman, A. Mahanti, M. Arlitt, QRP05-4: internet traffic identification using machine learning, in Global Telecommunications Conference (2006) pp. 1–6
Y. Kirsal, E. Ever, G. Mapp, O. Gemikonakli, Enhancing the modeling of vertical handover in integrated cellular/WLAN environments, in Advanced Information Networking and Applications (2013) pp. 924–930
L. Ning, Z. Wang, Q. Guo, K. Jiang, Fuzzy clustering based group vertical handover decision for heterogeneous wireless networks, in Wireless Communications and Networking Conference (WCNC), vol. 7(10) (IEEE, 2013) pp. 1231–1236
A.D. Grishaeva, V.Y. Voropayeva, Development of the vertical handover algorithm for heterogeneous wireless networks, in Microwave and Telecommunication Technology. 23rd International Crimean Conference, vol. 8(14) (2013) pp. 480–481
M. Kassar, B. Kervella, G. Pujolle, An overview of vertical handover decision strategies in heterogeneous wireless networks. Comput. Commun. 31(10), 2607–2620 (2008)
A. Mehbodniya, F. Kaleem, K.K. Yen, F. Adachi, A fuzzy MADM ranking approach for vertical mobility in next generation hybrid networks, in Ultra Modern Telecommunications and Control Systems and Workshop (2012) pp. 262–267
Y. Wang, Y. Xiang, S.Z. Yu, Automatic application signature construction from unknown traffic, in Advanced Information Networking and Applications IEEE (IEEE, 2010) pp. 1115–1120
Y. Wang, Y. Xiang, S. Yu, Internet traffic classification using machine learning: a token-based approach, in Computational Science and Engineering, (IEEE, 2011) pp. 285–289
S. Zander, T. Nguyen, G. Armitage, Automated traffic classification and application identification using machine learning, in Local Computer Networks, (IEEE, 2005) pp. 250–257
Acknowledgments
We are highly indebted to the authorities of Mobile and Wireless Networks Research Laboratory of CSE Department of Amrita Vishwa Vidyapeetham for providing necessary hardware resources and test bed for carrying out this research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Anantha Narayanan, V., Sureshkumar, V., Rajeswari, A. (2015). Automatic Traffic Classification Using Machine Learning Algorithm for Policy-Based Routing in UMTS–WLAN Interworking. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 324. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2126-5_34
Download citation
DOI: https://doi.org/10.1007/978-81-322-2126-5_34
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2125-8
Online ISBN: 978-81-322-2126-5
eBook Packages: EngineeringEngineering (R0)