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2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery
Mining Recent Approximate Frequent Items in Wireless Sensor Networks
Tianjin, China
August 14-August 16
ISBN: 978-0-7695-3735-1
| ASCII Text | x | ||
| Meirui Ren, Longjiang Guo, "Mining Recent Approximate Frequent Items in Wireless Sensor Networks," Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 2, pp. 463-467, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/FSKD.2009.607, author = {Meirui Ren and Longjiang Guo}, title = {Mining Recent Approximate Frequent Items in Wireless Sensor Networks}, journal ={Fuzzy Systems and Knowledge Discovery, Fourth International Conference on}, volume = {2}, year = {2009}, isbn = {978-0-7695-3735-1}, pages = {463-467}, doi = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.607}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on TI - Mining Recent Approximate Frequent Items in Wireless Sensor Networks SN - 978-0-7695-3735-1 SP463 EP467 A1 - Meirui Ren, A1 - Longjiang Guo, PY - 2009 KW - Wireless sensor networks KW - Frequent items KW - Sensory Data mining VL - 2 JA - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.607
Mining Frequent Items from sensory data is a major research problem in wireless sensor networks(WSNs) and it can be widely used in environmental monitoring. Conventional Lossy Counting algorithm can be applied to solve this problem in centralized manner. However, centralized algorithm brings severely data collision in WSNs, and results in inaccurate mining results. In this paper, we present D-FIMA, a distributed frequent items mining algorithm. D-FIMA, running at every sensor node, establishes items aggregation tree via forwarding mining request beforehand, and each node maintains local approximate frequent items. The root of the aggregation tree outputs the final global approximate frequent items. Theoretical analysis and the simulation results show that energy consumption of D-FIMA is much less than the centralized algorithm, and mining results of D-FIMA is more accurate than the centralized algorithm.
Index Terms:
Wireless sensor networks, Frequent items, Sensory Data mining
Citation:
Meirui Ren, Longjiang Guo, "Mining Recent Approximate Frequent Items in Wireless Sensor Networks," fskd, vol. 2, pp.463-467, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
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