Abstract
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.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Liu, J.-S., Lin, C.-H.: RBR: Refinement-Based Route Maintenance Protocol in Wireless Ad Hoc Networks. Computer Communications 28, 908–920 (2005)
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)
IEEE 802.11, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std. (2007)
Gui, C., Mohapatra, P.: A Framework for Self-Healing and Optimizing Routing Techniques for Mobile Ad Hoc Networks. Wireless Networks 14, 29–46 (2008)
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)
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)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for supportive vector machines. ACM Trans. on Intel. Sys. and Tech. 2, 1–27 (2011)
NS Notes and Document, http://www.isi.edu/nsnam/ns/
Chapelle, O., Bousquet, O., Mukherjee, S.: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46, 131–159 (2002)
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)
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)
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)
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)
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)
Chen, Q., Kanhere, S.S., Hassan, M.: Adaptive position update for geographic routing in mobile ad hoc netwoks. IEEE Trans. Mobile Computing 12 (2013)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (2000)
Akyildiz, I.: Wireless sensor networks: a survey. Computer Networks 38, 393–422 (2002)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Liang, Z., Wakahara, Y. (2014). Automatic Route Shortening Based on Link Quality Classification in Ad Hoc Networks. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J. (eds) Advances in Systems Science. Advances in Intelligent Systems and Computing, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-01857-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-01857-7_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01856-0
Online ISBN: 978-3-319-01857-7
eBook Packages: EngineeringEngineering (R0)