IEICE TRANSACTIONS on Information
A Mobile Agent Based Distributed Variational Bayesian Algorithm for Flow and Speed Estimation in a Traffic System
Summary :
This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.12 pp.2934-2942
- Publication Date
- 2016/12/01
- Publicized
- 2016/08/24
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016PAP0002
- Type of Manuscript
- Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
- Category
- Sensor network
Authors
Mohiyeddin MOZAFFARI
Shiraz University of Technology
Behrouz SAFARINEJADIAN
Shiraz University of Technology
Keyword
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Mohiyeddin MOZAFFARI, Behrouz SAFARINEJADIAN, "A Mobile Agent Based Distributed Variational Bayesian Algorithm for Flow and Speed Estimation in a Traffic System" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 12, pp. 2934-2942, December 2016, doi: 10.1587/transinf.2016PAP0002.
Abstract: This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016PAP0002/_p
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@ARTICLE{e99-d_12_2934,
author={Mohiyeddin MOZAFFARI, Behrouz SAFARINEJADIAN, },
journal={IEICE TRANSACTIONS on Information},
title={A Mobile Agent Based Distributed Variational Bayesian Algorithm for Flow and Speed Estimation in a Traffic System},
year={2016},
volume={E99-D},
number={12},
pages={2934-2942},
abstract={This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.},
keywords={},
doi={10.1587/transinf.2016PAP0002},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - A Mobile Agent Based Distributed Variational Bayesian Algorithm for Flow and Speed Estimation in a Traffic System
T2 - IEICE TRANSACTIONS on Information
SP - 2934
EP - 2942
AU - Mohiyeddin MOZAFFARI
AU - Behrouz SAFARINEJADIAN
PY - 2016
DO - 10.1587/transinf.2016PAP0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2016
AB - This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.
ER -
English
