Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2006
Abstract
Recent advances in hardware and wireless technologies have led to sensor networks consisting of large number of sensors capable of gathering and processing data collectively. Query processing on these sensor networks has to consider various inherent constraints. While simple queries such as select and aggregate queries in wireless sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. In this paper, we present a synopsis join strategy for evaluating join queries in sensor networks with communication efficiency. In this strategy, instead of directly joining two relations distributed in a sensor network, synopses of the relations are firstly joined to prune those data tuples that do not contribute to join results. We discuss various issues related to the optimization of synopsis join. Through experiments, we show the effectiveness of the synopsis join techniques in terms of communication cost for different join selectivities and other parameters.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Frontiers of WWW Research and Development - APWeb 2006: 8th Asia Pacific Web Conference (APWEB'06)
Volume
3841
First Page
263
Last Page
274
ISBN
9783540324379
Identifier
10.1007/11610113_24
Publisher
Springer Verlag
City or Country
Harbin, China
Citation
YU, Hai; LIM, Ee Peng; and ZHANG, Jun.
In-network join processing for sensor networks. (2006). Frontiers of WWW Research and Development - APWeb 2006: 8th Asia Pacific Web Conference (APWEB'06). 3841, 263-274.
Available at: https://ink.library.smu.edu.sg/sis_research/1299
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1007/11610113_24
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons