A novel distributed variational approximation method for density estimation in sensor networks

被引：11

作者：

Safarinejadian, Behrouz ^{[1
]}

Estahbanati, Mahboobeh Estakhri ^{[1
]}

机构：

[1] Shiraz Univ Technol, Dept Control Engn, Modarres Blvd,POB 71555-313, Shiraz, Iran

来源：

MEASUREMENT | 2016年 / 89卷

关键词：

Sensor networks; Consensus filter; Density estimation; Mixture of Gaussians; Variational approximations; EM ALGORITHM; AUTONOMOUS AGENTS; GAUSSIAN MIXTURES; FINITE MIXTURE; CONSENSUS;

D O I：

10.1016/j.measurement.2016.03.074

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, a consensus filter based distributed variational Bayesian (CFBDVB) algorithm is developed for distributed density estimation. Sensor measurements are assumed to be statistically modeled by a finite mixture model for which the CFBDVB algorithm is used to estimate the parameters, including means, covariances and weights of components. This algorithm is based on three steps: (1) calculating local sufficient statistics at every node, (2) estimating a global sufficient statistics vector using a consensus filter, (3) updating parameters of the finite mixture model based on the global sufficient statistics vector. Scalability and robustness are two advantages of the proposed algorithm. Convergence of the CFBDVB algorithm is also proved using Robbins-Monro stochastic approximation method. Finally, to verify performance of CFBDVB algorithm, we perform several simulations of sensor networks. Simulation results are very promising. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：78 / 86

页数：9

共 39 条

[21]

Paskin Mark., 2005, International_Symposium_on_Information_Processing_in_Sensor Networks, page, P8

[22]

Penny W. D., 2000, PARG200001 OXF U DEP

[23]

Safarinejadian B., 2009, Journal of Applied Sciences, V9, P854, DOI 10.3923/jas.2009.854.864

[24] Distributed variational Bayesian algorithms for Gaussian mixtures in sensor networks [J].

Safarinejadian, B. ;

Menhaj, M. B. ;

Karrari, M. .

SIGNAL PROCESSING, 2010, 90 (04) :1197-1208

[25] Distributed Unsupervised Gaussian Mixture Learning for Density Estimation in Sensor Networks [J].

Safarinejadian, Behrooz ;

Menhaj, Mohammad B. ;

Karrari, Mehdi .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2010, 59 (09) :2250-2260

[26] A distributed EM algorithm to estimate the parameters of a finite mixture of components [J].

Safarinejadian, Behrooz ;

Menhaj, Mohammad B. ;

Karrari, Mehdi .

KNOWLEDGE AND INFORMATION SYSTEMS, 2010, 23 (03) :267-292

[27] A Fault Tolerant Peer-to-Peer Distributed EM Algorithm [J].

Safarinejadian, Behrooz ;

Menhaj, Mohammad B. ;

Karrari, Mehdi .

2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING, 2009, :46-52

[28] A Distributed Variational Bayesian Algorithm for Density Estimation in Sensor Networks [J].

Safarinejadian, Behrooz ;

Menhaj, Mohammad B. ;

Karrari, Mehdi .

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2009, E92D (05) :1037-1048

[29] Consensus in ad hoc WSNs with noisy links -: Part I:: Distributed estimation of deterministic signals [J].

Schizas, Ioannis D. ;

Ribeiro, Alejandro ;

Giannakis, Georgios B. .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2008, 56 (01) :350-364

[30]

Pereira SS, 2013, INT CONF ACOUST SPEE, P4449, DOI 10.1109/ICASSP.2013.6638501

← 1 2 3 4 →