Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

Effect of Community Structure of Network on Distributed Estimation

Authors
Xiaodan Shao, Feng Chen
Corresponding Author
Xiaodan Shao
Available Online January 2016.
DOI
https://doi.org/10.2991/icsmim-15.2016.126How to use a DOI?
Keywords
Diffusion LMS, community structure, distributed estimation, estimation performance.
Abstract

The diffusion strategies have been widely studied for distributed estimation over adaptive networks. This paper investigates the impacts of community structure of network on the performance of the adaptive-then-combine diffusion LMS. The study covers different local community structures, while the performance is analyzed according to the transient and steady state mean-square errors. Simulations demonstrate that the performance of distributed estimation have regular changes along with the variety of number of community, which indicates that the network community indeed plays an important role in the distributed estimation.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
Series
Advances in Computer Science Research
Publication Date
January 2016
ISBN
978-94-6252-157-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icsmim-15.2016.126How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiaodan Shao
AU  - Feng Chen
PY  - 2016/01
DA  - 2016/01
TI  - Effect of Community Structure of Network on Distributed Estimation
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 677
EP  - 680
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.126
DO  - https://doi.org/10.2991/icsmim-15.2016.126
ID  - Shao2016/01
ER  -