Automatic Route Shortening Based on Link Quality Classification in Ad Hoc Networks
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.
KeywordsAd hoc network routing machine learning support vector machine
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- 3.IEEE 802.11, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Std. (2007)Google Scholar
- 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.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
- 8.NS Notes and Document, http://www.isi.edu/nsnam/ns/
- 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.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.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.Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (2000)Google Scholar
- 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