Distributed processing of probabilistic top-k queries in wireless sensor networks

Mao Ye, Wang-chien Lee, Dik Lun Lee, Xingjie Liu

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

In this paper, we introduce the notion of sufficient set and necessary set for distributed processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two concepts have very nice properties that can facilitate localized data pruning in clusters. Accordingly, we develop a suite of algorithms, namely, sufficient set-based (SSB), necessary set-based (NSB), and boundary-based (BB), for intercluster query processing with bounded rounds of communications. Moreover, in responding to dynamic changes of data distribution in the network, we develop an adaptive algorithm that dynamically switches among the three proposed algorithms to minimize the transmission cost. We show the applicability of sufficient set and necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured network topologies. Experimental results show that the proposed algorithms reduce data transmissions significantly and incur only small constant rounds of data communications. The experimental results also demonstrate the superiority of the adaptive algorithm, which achieves a near-optimal performance under various conditions.

Original languageEnglish (US)
Article number5936067
Pages (from-to)76-91
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number1
DOIs
StatePublished - Jan 1 2013

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Wireless sensor networks
Adaptive algorithms
Processing
Query processing
Communication
Data communication systems
Switches
Topology
Costs

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

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Distributed processing of probabilistic top-k queries in wireless sensor networks. / Ye, Mao; Lee, Wang-chien; Lee, Dik Lun; Liu, Xingjie.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 1, 5936067, 01.01.2013, p. 76-91.

Research output: Contribution to journalArticle

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