Joint synchronization and localization based on Gaussian belief propagation in sensor networks

Weijie YUAN; Nan WU; Hua WANG; Bin LI; Jingming KUANG

doi:10.1109/ICC.2015.7249384

Joint synchronization and localization based on Gaussian belief propagation in sensor networks

Weijie YUAN, Nan WU^*, Hua WANG, Bin LI, Jingming KUANG

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

7 Citations (Scopus)

Abstract

In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.

Original language	English
Title of host publication	2015 IEEE International Conference on Communications, ICC 2015
Publisher	IEEE
Pages	6646-6651
Number of pages	6
ISBN (Electronic)	9781467364324
ISBN (Print)	9781467364317
DOIs	https://doi.org/10.1109/ICC.2015.7249384
Publication status	Published - 9 Sept 2015
Externally published	Yes
Event	IEEE International Conference on Communications, ICC 2015 - London, United Kingdom Duration: 8 Jun 2015 → 12 Jun 2015

Publication series

Name	IEEE International Conference on Communications
Volume	2015-September
ISSN (Print)	1550-3607

Conference

Conference	IEEE International Conference on Communications, ICC 2015
Country/Territory	United Kingdom
City	London
Period	8/06/15 → 12/06/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Belief Propagation
Cooperative Joint Localization and Synchronization
Factor Graph
Gaussian Message Representation
Message Passing Schedule
Wireless Sensor Network

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10.1109/ICC.2015.7249384

Cite this

@inproceedings{d078c6c3ec5a4270a82a0ff35c9c8bb7,

title = "Joint synchronization and localization based on Gaussian belief propagation in sensor networks",

abstract = "In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.",

keywords = "Belief Propagation, Cooperative Joint Localization and Synchronization, Factor Graph, Gaussian Message Representation, Message Passing Schedule, Wireless Sensor Network",

author = "Weijie YUAN and Nan WU and Hua WANG and Bin LI and Jingming KUANG",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Communications, ICC 2015 ; Conference date: 08-06-2015 Through 12-06-2015",

year = "2015",

month = sep,

day = "9",

doi = "10.1109/ICC.2015.7249384",

language = "English",

isbn = "9781467364317",

series = "IEEE International Conference on Communications",

publisher = "IEEE",

pages = "6646--6651",

booktitle = "2015 IEEE International Conference on Communications, ICC 2015",

}

YUAN, W, WU, N, WANG, H, LI, B & KUANG, J 2015, Joint synchronization and localization based on Gaussian belief propagation in sensor networks. in 2015 IEEE International Conference on Communications, ICC 2015., 7249384, IEEE International Conference on Communications, vol. 2015-September, IEEE, pp. 6646-6651, IEEE International Conference on Communications, ICC 2015, London, United Kingdom, 8/06/15. https://doi.org/10.1109/ICC.2015.7249384

Joint synchronization and localization based on Gaussian belief propagation in sensor networks. / YUAN, Weijie; WU, Nan; WANG, Hua et al.
2015 IEEE International Conference on Communications, ICC 2015. IEEE, 2015. p. 6646-6651 7249384 (IEEE International Conference on Communications; Vol. 2015-September).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Joint synchronization and localization based on Gaussian belief propagation in sensor networks

AU - YUAN, Weijie

AU - WU, Nan

AU - WANG, Hua

AU - LI, Bin

AU - KUANG, Jingming

PY - 2015/9/9

Y1 - 2015/9/9

N2 - In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.

AB - In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.

KW - Belief Propagation

KW - Cooperative Joint Localization and Synchronization

KW - Factor Graph

KW - Gaussian Message Representation

KW - Message Passing Schedule

KW - Wireless Sensor Network

UR - http://www.scopus.com/inward/record.url?scp=84953707610&partnerID=8YFLogxK

U2 - 10.1109/ICC.2015.7249384

DO - 10.1109/ICC.2015.7249384

M3 - Conference paper (refereed)

AN - SCOPUS:84953707610

SN - 9781467364317

T3 - IEEE International Conference on Communications

SP - 6646

EP - 6651

BT - 2015 IEEE International Conference on Communications, ICC 2015

PB - IEEE

T2 - IEEE International Conference on Communications, ICC 2015

Y2 - 8 June 2015 through 12 June 2015

ER -

Joint synchronization and localization based on Gaussian belief propagation in sensor networks

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