POT: an efficient top-k monitoring method for spatially correlated sensor readings

  • Authors:
  • YongHyun Cho;Jihoon Son;Yon Dohn Chung

  • Affiliations:
  • Korea University, Seoul, Korea;Korea University, Seoul, Korea;Korea University, Seoul, Korea

  • Venue:
  • Proceedings of the 5th workshop on Data management for sensor networks
  • Year:
  • 2008

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Abstract

In this paper, we discuss the top-k monitoring over sensor networks. Since sensor readings are usually correlated with location, top-k nodes are clustered at some areas. Motivated by such a characteristic, we propose a novel tree structure named partial ordered tree(POT) to efficiently maintain clusters of the highest readings. By using POTs, only candidate nodes which might be included in top-k result are evaluated for query processing. Through simulation experiments, we evaluate the performance of the POT method in comparison with conventional methods.