TY - GEN
T1 - Topology inference in wireless mesh networks
AU - Xing, Kai
AU - Cheng, Xiuzhen
AU - Chen, Dechang
AU - Du, David Hung Chang
N1 - Funding Information:
This research is partially supported by the US National Science Foundation under grants CNS-0831852 and CNS-0831939.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70349304104&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03417-6_16
DO - 10.1007/978-3-642-03417-6_16
M3 - Conference contribution
AN - SCOPUS:70349304104
SN - 3642034160
SN - 9783642034169
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 168
BT - Wireless Algorithms, Systems, and Applications - 4th International Conference, WASA 2009, Proceedings
T2 - 4th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2009
Y2 - 16 August 2009 through 18 August 2009
ER -