SENOCLU, Energy Efficient Approach for Unsupervised Node Clustering in Sensor Networks
Acquisition and analysis of data from sensor networks, where nodes operate in unsupervised way, has become a ubiquitous issue. The biggest challenge in this process is related to limited energy, computational and memory capacity of sensor nodes. Therefore, the main goal of our work is to devise and evaluate the contribution of an energy efficient algorithm for data acquisition in sensor networks.
The proposed SENOCLU algorithm considers specific requirements of sensor network application like energy efficiency, state change detection, load balancing, high-dimensions of the sensed data etc. By applying these techniques, this algorithm contributes in filling the gap between distributed clustering and high-dimensional clustering algorithms that are available in the literature. This work evaluates the contribution of this algorithm in comparison to other competing state-of-the-art techniques.
The experiments show that by applying SENOCLU algorithm better life times of sensor networks are achieved and longer monitoring of different phenomena is provided.
V. Shnayder, M. Hempstead, B. Chen, G. Allen, and M. Welsh, Simulating the Power Consumption of LargeScale Sensor Network Applications, Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, SenSys 2004, Baltimore, MD, USA.
J. Polastre, R. Szewczyk, and D. Culler, Telos: Enabling Ultra-Low Power Wireless Research, Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks, IPSN 2005, Los Angeles, California, USA.
J. B. MacQueen, Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1: 281-297, 1967.
L. Kaufman and P.J.Rousseeuw, Clustering by Means of Medoids in Statistical Data Analysis Based on the L1 Norm, Y. Dodge, Ed., pp. 405-416. North Holland/Elsevier, Amsterdam, 1987.
T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An Efficient Data
Clustering Method for Very Large Databases, SIGMOD 96 6/96 Montreal Canada.
M. Ester, H. Kriegel, J. Sander, X. Xu, A Density-Based Algorithm for Discovering Clusters in Large Spatial Data Bases with Noise, 2-nd International Conference on Knowledge Discovery and Data Mining,
A. Meka and A. K. Singh, Distributed Special Clustering in Sensor
Networks, EDBT 2006, LNCS 3896, pp 980-1000, 2006
P. P. Rodrigues, J. Gama and L. Lopes, Clustering Distributed
Sensor Data Streams, ECML PKDD 2008, LNAI. Springer-Verlag 2008.
J.Yin, M. M. Gaber, Clustering Distributed Time Series in Sensor
Networks, in Proceedings of the Eighth IEEE Conference on Data Mining, ICDM, 2008.
E. Januzaj, H. Kriegel, M. Pfeifle, Scalable Density-Based Distributed Clustering, Proc. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Pisa, Italy, 2004.
A. Silberstein, R. Braynard, G. Filpus, G. Puggioni, A. Gelfand, K. Munagala, and J. Yang, Data-Driven Processing in Sensor Networks, 3rd Biennal Conference on Innovative Data Systems Research (CIDR), Asilomar, California, USA, 2007.
Y. Kotidis, Snapshot Queries: Towards Data-Centric Sensor Networks, Proceeding of the 21st International Conference on Data Engineering, ICDE 2005.
M. Hassani, E. Müller, P. Spaus, A. Faqolli, Th. Palpanas, Th. Seidl, Self-Organizing Energy Aware Clustering of Nodes in Sensor Networks Using Relevant Attributes, SensorKDD10, July 25, 2010, Washington, DC, USA.
E. Baralis, T. Cerquitelli, Selecting Representatives in a Sensor Network, SEBD 2006: 351-360.
Dataset of Intel Berkeley Research Lab, "http://db.csail.mit.edu/
labdata/labdata.html", in [online], 2004
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