Distributed EM algorithms for density estimation and clustering in sensor networks

The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described as a mixture of some elementary conditions. The measurements are thus statistically modeled with a mixture of G...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2003-08, Vol.51 (8), p.2245-2253
Main Author: Nowak, R.D.
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Applied sciences
Approximation algorithms
Clustering
Clustering algorithms
Convergence
Density
Distributed processing
Estimating
Exact sciences and technology
Gaussian
Gaussian approximation
Gaussian distribution
Gaussian processes
Mathematical models
Networks
Probability
Sensor phenomena and characterization
Sensors
Studies
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
Transmission and modulation (techniques and equipments)
Valuation and optimization of characteristics. Simulation
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Summary:The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described as a mixture of some elementary conditions. The measurements are thus statistically modeled with a mixture of Gaussians, where each Gaussian component corresponds to one of the elementary conditions. The paper presents a distributed expectation-maximization (EM) algorithm for estimating the Gaussian components, which are common to the environment and sensor network as a whole, as well as the mixing probabilities that may vary from node to node. The algorithm produces an estimate (in terms of a Gaussian mixture approximation) of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, the algorithm can be viewed as a distributed processing strategy for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the distributed EM algorithm is investigated, and simulations demonstrate the potential of this approach to sensor network data analysis.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2003.814623