Distributed Clustering Using Wireless Sensor Networks

Clustering spatially distributed data is well motivated and especially challenging when communication to a central processing unit is discouraged, e.g., due to power constraints. Distributed clustering schemes are developed in this paper for both deterministic and probabilistic approaches to unsuper...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing Vol. 5; no. 4; pp. 707 - 724
Main Authors: Forero, P. A., Cano, A., Giannakis, G. B.
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Clusters
Computer simulation
Convergence
Distributed algorithms
Distributed databases
expectation-maximization (EM) algorithms
iterative methods
Learning
Probabilistic logic
Probability theory
Robustness
Sensors
Signal processing algorithms
Wireless sensor networks
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary:Clustering spatially distributed data is well motivated and especially challenging when communication to a central processing unit is discouraged, e.g., due to power constraints. Distributed clustering schemes are developed in this paper for both deterministic and probabilistic approaches to unsupervised learning. The centralized problem is solved in a distributed fashion by recasting it to a set of smaller local clustering problems with consensus constraints on the cluster parameters. The resulting iterative schemes do not exchange local data among nodes, and rely only on single-hop communications. Performance of the novel algorithms is illustrated with simulated tests on synthetic and real sensor data. Surprisingly, these tests reveal that the distributed algorithms can exhibit improved robustness to initialization than their centralized counterparts.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2011.2114324