Suppression and Failures in Sensor Networks : A Bayesian Approach

@inproceedings{Silberstein2007SuppressionAF,
  title={Suppression and Failures in Sensor Networks : A Bayesian Approach},
  author={Adam Silberstein and Gavino Puggioni and Alan E. Gelfand and Kamesh Munagala and Jun Yang},
  year={2007}
}
Sensor networks allow continuous data collection on unprecedented scales. The primary limiting factor of such networks is energy, of which communication is the dominant consumer. The default strategy of nodes continually reporting their data to the root results in too much messaging. Suppression stands to greatly alleviate this problem. The simplest such scheme is temporal suppression, in which a node transmits its reading only when it has changed beyond some since last transmitted. In the… CONTINUE READING
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