DocumentCode :
507340
Title :
MCC: Model-Based Continuous Clustering in Wireless Sensor Networks
Author :
Hu, Haifeng ; Ma, Xiuli ; Tang, Shiwei ; Chen, Guanhua ; Zhao, Qisheng
Author_Institution :
Key Lab. of Machine Perception, Peking Univ., Beijing, China
Volume :
5
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
265
Lastpage :
269
Abstract :
Nowadays, many sensor network applications are not only interested in the raw data of a single sensor node, but also the overview distribution features of network-wide sensory data. Data-centric clustering can provide an overview of the sensory data distribution. However most data-centric clustering researches are based on snapshot data rather than evolving data, which is not feasible as data evolves. In this paper, we propose a novel clustering method in wireless sensor networks called MCC: Model-based Continuous Clustering. In MCC, we do the clustering in a continuous way to make every node belong to the most suitable cluster at every epoch which guarantees that the clustering structure can represent the up-to-date data distribution to the most extent. Moreover, we build a model for every sensor node based on its recent data to capture the data evolving trend. MCC is based on these models. The extensive experiments on real-datasets show the effectiveness and efficiency of MCC.
Keywords :
pattern clustering; wireless sensor networks; data-centric clustering; model based continuous clustering; sensory data distribution; wireless sensor networks; Autocorrelation; Clustering methods; Continuing education; Costs; Data acquisition; Fuzzy systems; Laboratories; Sensor phenomena and characterization; Sensor systems and applications; Wireless sensor networks; Clustering; Wireless sensor network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
Type :
conf
DOI :
10.1109/FSKD.2009.636
Filename :
5360619
Link To Document :
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