Automatic Route Shortening Based on Link Quality Classification in Ad Hoc Networks

  • Zilu LiangEmail author
  • Yasushi Wakahara
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 240)


The highly dynamic topology of an ad hoc network often causes route redundancy in the network. Several route shortening methods have been proposed to eliminate the redundancy. However, the existing works do not consider intensively the quality of the shortening links; unstable shortening links may degrade the network performance by causing unnecessary control overhead. In this paper, we seek to enhance the performance of route shortening through intensive consideration of the quality of shortening links. In our proposed SVM-ARS, all potential shortening opportunities are classified into preferred shortenings and non-preferred shortenings, and only preferred shortenings are executed in practice. The classification is achieved by Support Vector Machine (SVM). We compared SVM-ARS with a node mobility prediction model UMM and the Geographic Automatic Route Shortening (GARS) protocol. The simulations results confirm that our proposal significantly outperforms UMM and GARS through reducing the control overhead.


Ad hoc network routing machine learning support vector machine 


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  1. 1.
    Liu, J.-S., Lin, C.-H.: RBR: Refinement-Based Route Maintenance Protocol in Wireless Ad Hoc Networks. Computer Communications 28, 908–920 (2005)CrossRefGoogle Scholar
  2. 2.
    Cheng, R.-H., Wu, T.-K.: A highly topology adaptable ad hoc routing protocol with complementary preemptive link breaking avoidance and pat shortening mechanisms. Wireless Network 16, 1289–1311 (2010)CrossRefGoogle Scholar
  3. 3.
    IEEE 802.11, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std. (2007)Google Scholar
  4. 4.
    Gui, C., Mohapatra, P.: A Framework for Self-Healing and Optimizing Routing Techniques for Mobile Ad Hoc Networks. Wireless Networks 14, 29–46 (2008)CrossRefGoogle Scholar
  5. 5.
    Liang, Z., Taenaka, Y., Ogawa, T., Wakahara, Y.: Pro-Reactive Route Recovery with Automatic Route Shortening in Wireless Ad Hoc Networks. In: Proc. of ISADS 2011 (2011)Google Scholar
  6. 6.
    Liang, Z., Taenaka, Y., Ogawa, T., Wakahara, Y.: Automatic route shortening for performance enhancement in wireless ad hoc networks. In: The 13th Network Software Conference (2011)Google Scholar
  7. 7.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for supportive vector machines. ACM Trans. on Intel. Sys. and Tech. 2, 1–27 (2011)CrossRefGoogle Scholar
  8. 8.
    NS Notes and Document,
  9. 9.
    Chapelle, O., Bousquet, O., Mukherjee, S.: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46, 131–159 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Xu, J., Li, Q.-M., Zhang, H., Liu, F.-Y.: Model and Analysis of Path Compression for Mobile Ad Hoc Networks. Computers and Electrical Engineering 36, 442–454 (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    Chen, C.-W., Wang, C.-C.: A Power Efficiency Routing and Maintenance Protocol in Wireless Multi-Hop Networks. The Journal of Systems and Software 85, 62–76 (2012)CrossRefGoogle Scholar
  12. 12.
    Nguyen, X., Jordan, M., Sinopoli, B.: A kernel-based learning approach to ad hoc sensor network localization. ACM Trans. Sensor Networks 1, 134–152 (2005)CrossRefGoogle Scholar
  13. 13.
    Feng, V.-S., Chang, S.Y.: Determination of wireless networks parameters through parallel hierarchical support vector machines. IEEE Trans. Parallel and Distributed Systems 23 (2012)Google Scholar
  14. 14.
    Fiore, M., Casetti, C.E., Chiasserini, C.-F., Papadimitratos, P.: Discovery and verification of neighbour positions in mobile ad hoc networks. IEEE Trans. Mobile Computing 12 (2013)Google Scholar
  15. 15.
    Chen, Q., Kanhere, S.S., Hassan, M.: Adaptive position update for geographic routing in mobile ad hoc netwoks. IEEE Trans. Mobile Computing 12 (2013)Google Scholar
  16. 16.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (2000)Google Scholar
  17. 17.
    Akyildiz, I.: Wireless sensor networks: a survey. Computer Networks 38, 393–422 (2002)CrossRefGoogle Scholar
  18. 18.
    Roberts, J., Stirling, T., Zufferey, J., Floreano, D.: 2.5D infrared range and bearing system for collective robotics. In: Proc. of IEEE/RSJ IROS, pp. 3659–3664 (2009)Google Scholar
  19. 19.
    Chen, Y.-W., Lin, C.-J.: Combing SVMs with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction. STUDFUZZ, vol. 207, pp. 315–324. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Fan, R.-E., Chen, P.-H., Lin, C.-J.: Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889–1918 (2005)zbMATHMathSciNetGoogle Scholar
  21. 21.
    Su, W., Lee, S.-J., Gerla, M.: Mobility prediction and routing in ad hoc wireless networks. International Journal of Network Management 11, 3–30 (2001)CrossRefGoogle Scholar
  22. 22.
    Guo, Z., Malakooti, S., Sheikh, S.: Multi-objective OLSR for proactive routing in MANET with delay, energy, and link lifetime predictions. Applied Mathematical Modelling 35, 1413–1426 (2011)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Graduate School of EngineeringThe University of TokyoBunkyo-KuJapan

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