Reliable Vertical Handoff Technique Based on Probabilistic Classification Model

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 828)


The Next Generation wireless network framework has introduced cooperative communication philosophy to provide better service to the clients. Vertical Handoff is one such cooperative technique, which switches the client’s network from the current to another in-order to continue providing requested Quality of Service (QoS). There are multiple parameters that need to be considered for achieving vertical handoff such as-service cost, data rate, mobile device speed, network latency, interference ratio, device battery level, Received Signal Strength Information (RSSI) etc.

Until now, vertical hand off schemes have targeted to achieve effective selection of suitable alternate networks in providing required connection transfer. Many classification schemes based on Neural Networks, Support Vector Machine were utilized in designing vertical handoff techniques. These techniques do a good job in choosing suitable alternate networks, but, once the handoff is made, there is no guarantee that, the new network will continuously provide the requested QoS. The client might require new handoff if the recently migrated network is not able to deliver the specified QoS. Frequent handoff’s can be expensive and inefficient for the client. Ideally, when making the first handoff, it is important to consider the reliability of new networks in continuously providing the requested QoS. In the existing literature, this problem has not been properly addressed.

In this work, new vertical handoff scheme is proposed, which addresses the reliability issue. This proposed vertical handoff scheme is built over probabilistic classification model. Empirical results obtained through simulation, reveal the excellent effectiveness of the proposed vertical handoff scheme.


  1. 1.
    Xia, L., Ling-ge, J., Chen, H., Hong-wei, L.: An intelligent vertical handoff algorithm in heterogeneous wireless networks. In: Neural Networks and Signal Processing, International Conference, pp. 550–555 (2008)Google Scholar
  2. 2.
    Ling, Y., Yi, B., Zhu, Q.: An improved vertical handoff decision algorithm for heterogeneous wireless networks. In: Wireless Communications, Networking and Mobile Computing, WiCOM 2008, pp. 1–3 (2008)Google Scholar
  3. 3.
    Guo, Q., Zhu, J., Xu, X.: An adaptive multi-criteria vertical handoff decision algorithm for radio heterogeneous network. In: IEEE International Conference on Communications, ICC 2005, pp. 2769–2773 (2005)Google Scholar
  4. 4.
    Stoyanova, M., Mahonen, P.: Algorithmic approaches for vertical handoff in heterogeneous wireless environment. In: Wireless Communications and Networking Conference, WCNC, pp. 3780–3785 (2007)Google Scholar
  5. 5.
    Nkansah-Gyekye, Y., Agbinya, J.I.: A vertical handoff decision algorithm for next generation wireless networks. In: Third International Conference on Broadband Communications, Information Technology and Biomedical Applications, pp. 358–364 (2008)Google Scholar
  6. 6.
    Bhattacharya, P.P.: Application of artificial neural network in cellular handoff management. In: Conference on Computational Intelligence and Multimedia Applications, International Conference, vol. 1, pp. 237–241 (2007).
  7. 7.
    Nasser, N., Guizani, S., Al-Masri, E.: Middleware vertical handoff manager: a neural network-based solution. In: IEEE International Conference on Communications, ICC 2007, pp. 5671–5676 (2007).
  8. 8.
    Onel, T., Ersoy, C., Cayırcı, E., Parr, G.: A multi criteria handoff decision scheme for the next generation tactical communications systems. Comput. Netw. 46(5), 695–708 (2004)CrossRefGoogle Scholar
  9. 9.
    Çalhan, A., Çeken, C.: An optimum vertical handoff decision algorithm based on adaptive fuzzy logic and genetic algorithm. Wirel. Pers. Commun. (2010).
  10. 10.
    Horrich, S., Ben Jamaa, S., Godlewski, P.: Neural networks for adaptive vertical handover decision. In: 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops 2007, WiOpt 2007, pp. 1–7 (2007).
  11. 11.
    Zayani, R., Bouallegue, R., Roviras, D.: Levenberg-marquardt learning neural network for adaptive pre-distortion for time-varying HPA with memory in OFDM systems. In: 16th European Signal Processing Conference on EUSIPCO 2008 (2008)Google Scholar
  12. 12.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by backpropagation errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  13. 13.
    Hagan, M.T., Menhaj, M.B.: Training feed forward network with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)CrossRefGoogle Scholar
  14. 14.
    Levenberg, K.: A method for the solution of certain nonlinear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ceken, C., Arslan, H.: An adaptive fuzzy logic based vertical handoff decision algorithm for wireless heterogeneous networks. In: Wireless and Microwave Technology (WAMI) Conference (WAMICON 2009), pp. 1–9 (2009)Google Scholar
  17. 17.
    Çalhan, A., Çeken, C.: Case study on handoff strategies for wireless overlay networks. Comput. Stan. Interfaces (2012).
  18. 18.
    Çalhan, A., Çeken, C.: An adaptive neuro-fuzzy based vertical handoff decision algorithm for wireless heterogeneous networks. In: The 21th Personal, Indoor and Mobile Radio Conference, pp. 2271–2276 (2010)Google Scholar
  19. 19.
    Tripathi, N.D., Reed, J.H., Van Landingham, H.F.: Radio Resource Management in Cellularsystems. Kluwer, Dordrecht (2001)Google Scholar
  20. 20.
    Calhan, A., Ceken, C.: Artificial neural network based vertical handoff algorithm for reducing handoff latency. In: Wireless Personal Communication (2013).

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of ISEBITBangaloreIndia
  2. 2.Department of CSEBMSCEBangaloreIndia

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