Topology Inference in Wireless Mesh Networks

Authors:
Kai Xing;Xiuzhen Cheng;Dechang Chen;David Hung-Chang Du
Affiliations:
Department of Computer Science, The George Washington University, Washington DC, USA 20052;Department of Computer Science, The George Washington University, Washington DC, USA 20052;Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences Bethesda, MD, USA 20814;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA MN 55455
Venue:
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Year:
2009

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Abstract

In this paper, we tackle the problem of topology inference in wireless mesh networks and present a novel approach to reconstructing the logical network topology. Our approach is based on the social fingerprint, a short bit pattern computed for each node to characterize the link status of the local neighborhood of the node. To conserve the communication resource, social fingerprints are piggybacked to the gateway with a small probability. Based on the information embedded in the social fingerprints, the gateway first estimates the set of parameters defining a Hidden Markov Model (HMM) that models the logical network topology, then infers the evolutions of the local and global network topologies. We have conducted extensive simulation to verify the performance of our approach in terms of "completeness" and "accuracy". The results indicate that our approach is very effective in topology inference.