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In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.

- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.5 pp.1037-1048

- Publication Date
- 2009/05/01

- Publicized

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.E92.D.1037

- Type of Manuscript
- PAPER

- Category
- Computation and Computational Models

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Behrooz SAFARINEJADIAN, Mohammad B. MENHAJ, Mehdi KARRARI, "A Distributed Variational Bayesian Algorithm for Density Estimation in Sensor Networks" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 5, pp. 1037-1048, May 2009, doi: 10.1587/transinf.E92.D.1037.

Abstract: In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1037/_p

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@ARTICLE{e92-d_5_1037,

author={Behrooz SAFARINEJADIAN, Mohammad B. MENHAJ, Mehdi KARRARI, },

journal={IEICE TRANSACTIONS on Information},

title={A Distributed Variational Bayesian Algorithm for Density Estimation in Sensor Networks},

year={2009},

volume={E92-D},

number={5},

pages={1037-1048},

abstract={In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.},

keywords={},

doi={10.1587/transinf.E92.D.1037},

ISSN={1745-1361},

month={May},}

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TY - JOUR

TI - A Distributed Variational Bayesian Algorithm for Density Estimation in Sensor Networks

T2 - IEICE TRANSACTIONS on Information

SP - 1037

EP - 1048

AU - Behrooz SAFARINEJADIAN

AU - Mohammad B. MENHAJ

AU - Mehdi KARRARI

PY - 2009

DO - 10.1587/transinf.E92.D.1037

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E92-D

IS - 5

JA - IEICE TRANSACTIONS on Information

Y1 - May 2009

AB - In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.

ER -