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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 625798, 15 pages
Research Article

Aggregate Queries in Wireless Sensor Networks

1Division of Computer Science & Engineering, Konkuk University, Seoul 143-701, Republic of Korea
2Division of Software Engineering, Northeastern University, Shenyang 110819, China
3Cloud Computing Research Department, Electronics and Telecommunications Research Institute, Daejeon 305-700, Republic of Korea

Received 19 February 2012; Revised 25 May 2012; Accepted 25 June 2012

Academic Editor: Jianliang Xu

Copyright © 2012 Jeong-Joon Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation (BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a quadtree, and then processes aggregate queries in parallel for each cell region according to routing. It sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.