Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1155/2012/625798 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Field | DocType | Volume |
Sensor node,Query optimization,Data structure,Data mining,Data transmission,Computer science,Computer network,Filter (signal processing),Online aggregation,Wireless sensor network,Quadtree | Journal | 2012 |
Issue | ISSN | Citations |
null | 1550-1477 | 2 |
PageRank | References | Authors |
0.38 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeong-Joon Kim | 1 | 5 | 4.11 |
| Shin In In-Su | 2 | 4 | 1.11 |
| Yan-Sheng Zhang | 3 | 2 | 0.38 |
| Dong-Oh Kim | 4 | 15 | 6.72 |
| Ki-Joon Han | 5 | 26 | 6.76 |