Two-phase clustering based aggregation of sensor data Online publication date: Fri, 04-Jun-2010
by Manish Kumar, Pritish Kumar Varadwaj, Shekhar Verma
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 2, No. 3, 2010
Abstract: To conserve energy, wireless sensor networks need to send only interesting data to the base station. Since the general nature of the data to be sensed is known a priori, the present work proposes ART based clustering and compares it with density based clustering of sensed data. ART based clustering works in two phases, offline and online. Fuzzy ART is employed as initial clustering process in the pre-deployment offline phase to detect natural clusters in data. To cater to the dynamic nature of the environment, fuzzy ARTMAP neural network (FAMNN) is used for clustering after deployment in the online phase. The second technique density based aggregation (DBA) is based on density of points within the clusters. Simulation results on synthetic data indicate that FAMNN is suited for wireless sensor network. DBA is able to match the initial clustering ability but creates large number of outliers as seen with the data under test.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook