The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, é considerado o problema de estimativa de densidade e agrupamento em redes de sensores. Supõe-se que as medições dos sensores podem ser modeladas estatisticamente por um modelo de mistura gaussiana comum. Este artigo desenvolve um algoritmo bayesiano variacional distribuído (DVBA) para estimar os parâmetros deste modelo. Este algoritmo produz uma estimativa da densidade dos dados do sensor sem exigir que os dados sejam transmitidos e processados em um local central. Alternativamente, o DVBA pode ser visto como uma abordagem de processamento distribuído para agrupar os dados do sensor em componentes correspondentes às características ambientais predominantes detectadas pela rede. A convergência do DVBA proposto é então investigada. Por fim, para verificar o desempenho do DVBA, realizamos diversas simulações de redes de sensores. Os resultados da simulação são muito promissores.
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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 -