A Distributed Variational Bayesian Algorithm for Density Estimation in Sensor Networks
Summary :
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 -
English
