Topology inference in wireless mesh networks

Kai Xing, Xiuzhen Cheng, Dechang Chen, David H Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationWireless Algorithms, Systems, and Applications - 4th International Conference, WASA 2009, Proceedings
Pages159-168
Number of pages10
DOIs
StatePublished - Sep 28 2009
Event4th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2009 - Boston, MA, United States
Duration: Aug 16 2009Aug 18 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5682 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2009
CountryUnited States
CityBoston, MA
Period8/16/098/18/09

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