KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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On Efficient Processing of Continuous Reverse Skyline Queries in Wireless Sensor Networks

  • Yin, Bo (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and technology) ;
  • Zhou, Siwang (College of Information Science and Engineering, Hunan University) ;
  • Zhang, Shiwen (School of Computer Science and Engineering, Hunan University of Science and Technology) ;
  • Gu, Ke (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and technology) ;
  • Yu, Fei (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and technology)
  • Received : 2016.09.20
  • Accepted : 2017.01.03
  • Published : 2017.04.30
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Abstract

The reverse skyline query plays an important role in information searching applications. This paper deals with continuous reverse skyline queries in sensor networks, which retrieves reverse skylines as well as the set of nodes that reported them for continuous sampling epochs. Designing an energy-efficient approach to answer continuous reverse skyline queries is non-trivial because the reverse skyline query is not decomposable and a huge number of unqualified nodes need to report their sensor readings. In this paper, we develop a new algorithm that avoids transmission of updates from nodes that cannot influence the reverse skyline. We propose a data mapping scheme to estimate sensor readings and determine their dominance relationships without having to know the true values. We also theoretically analyze the properties for reverse skyline computation, and propose efficient pruning techniques while guaranteeing the correctness of the answer. An extensive experimental evaluation demonstrates the efficiency of our approach.

Keywords

Acknowledgement

Supported by : Natural Science Foundation of China, Natural Science Foundation of Hunan Province

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