Dynamic clustering for acoustic target tracking in wireless sensor networks

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Dynamic clustering for acoustic target tracking in wireless sensor networks (2003)

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by Wei-peng Chen , Jennifer C. Hou , Lui Sha

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138 - 1 self

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@INPROCEEDINGS{Chen03dynamicclustering,
    author = {Wei-peng Chen and Jennifer C. Hou and Lui Sha},
    title = {Dynamic clustering for acoustic target tracking in wireless sensor networks},
    booktitle = {},
    year = {2003},
    pages = {258--271}
}

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Abstract

In the paper, we devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of (a) a static backbone of sparsely placed high-capability sensors which will assume the role of a cluster head (CH) upon triggered by certain signal events; and (b) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a pre-determined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs cooperate with one another to ensure that only one CH (preferably the CH that is closest to the target) is active with high probability; (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with non-redundant, essential information to determine the target location; and and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we show with the use of Voronoi diagram, the CH that is usually closest to the target is (implicitly) selected as the leader and and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.

Keyphrases

acoustic target wireless sensor network target location voronoi diagram sensor information dynamic clustering algorithm quality data populated low-end sensor certain signal event devise solution approach pre-determined threshold high-capability sensor high probability vicinity reply dynamic clustering significant collision acoustic signal strength information solicitation packet hierarchical sensor network static backbone chs report active ch cluster head essential information active ch solicits chs cooperate accurate estimate ns-2 simulation sensing information sufficient number probabilistic analysis

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