Energy-efficient skyline query optimization in wireless sensor networks

  • Authors:

  • Baichen Chen;Weifa Liang;Jeffrey Xu Yu

  • Affiliations:

  • Research School of Computer Science, The Australian National University, Canberra, Australia ACT 0200;Research School of Computer Science, The Australian National University, Canberra, Australia ACT 0200;Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

  • Venue:
  • Wireless Networks
  • Year:
  • 2012

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Abstract

With the deployment of wireless sensor networks (WSNs) for environmental monitoring and event surveillance, WSNs can be treated as virtual databases to respond to user queries. It thus becomes more urgent that such databases are able to support complicated queries like skyline queries. Skyline query which is one of popular queries for multi-criteria decision making has received much attention in the past several years. In this paper we study skyline query optimization and maintenance in WSNs. Specifically, we first consider skyline query evaluation on a snapshot dataset, by devising two algorithms for finding skyline points progressively without examining the entire dataset. Two key strategies are adopted: One is to partition the dataset into several disjoint subsets and produce the skyline points in each subset progressively. Another is to employ a global filter that consists of some skyline points in the processed subsets to filter out unlikely skyline points from the rest of unexamined subsets. We then consider the query maintenance issue by proposing an algorithm for incremental maintenance of the skyline in a streaming dataset. A novel maintenance mechanism is proposed, which is able to identify which skyline points from past skylines to be the global filter and determine when the global filter is broadcast. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on both synthetic and real sensing datasets, and the experimental results demonstrate that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation.