On optimal sensor collaboration for distributed estimation with individual power constraints

Sijia Liu; Swarnendu Kar; Makan Fardad; Pramod Kumar Varshney

doi:10.1109/ACSSC.2015.7421194

On optimal sensor collaboration for distributed estimation with individual power constraints

Sijia Liu, Swarnendu Kar, Makan Fardad, Pramod Kumar Varshney

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.

Original language	English (US)
Title of host publication	Conference Record - Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE Computer Society
Pages	571-575
Number of pages	5
Volume	2016-February
ISBN (Print)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421194
State	Published - Feb 26 2016
Event	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015

Other

Other	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country	United States
City	Pacific Grove
Period	11/8/15 → 11/11/15

Keywords

bilinear relaxation
convex-concave procedure
Distributed estimation
networks
sensor collaboration
ℓ1 norm

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

Access to Document

10.1109/ACSSC.2015.7421194

Fingerprint Dive into the research topics of 'On optimal sensor collaboration for distributed estimation with individual power constraints'. Together they form a unique fingerprint.

Cite this

On optimal sensor collaboration for distributed estimation with individual power constraints. / Liu, Sijia; Kar, Swarnendu; Fardad, Makan ; Varshney, Pramod Kumar.

Conference Record - Asilomar Conference on Signals, Systems and Computers. Vol. 2016-February IEEE Computer Society, 2016. p. 571-575 7421194.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, S, Kar, S, Fardad, M & Varshney, PK 2016, On optimal sensor collaboration for distributed estimation with individual power constraints. in Conference Record - Asilomar Conference on Signals, Systems and Computers. vol. 2016-February, 7421194, IEEE Computer Society, pp. 571-575, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/8/15. https://doi.org/10.1109/ACSSC.2015.7421194

@inproceedings{ab9e1b083bfd4337b6d6a7a228897dbf,

title = "On optimal sensor collaboration for distributed estimation with individual power constraints",

abstract = "In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.",

keywords = "bilinear relaxation, convex-concave procedure, Distributed estimation, networks, sensor collaboration, ℓ1 norm",

author = "Sijia Liu and Swarnendu Kar and Makan Fardad and Varshney, {Pramod Kumar}",

year = "2016",

month = feb,

day = "26",

doi = "10.1109/ACSSC.2015.7421194",

language = "English (US)",

isbn = "9781467385763",

volume = "2016-February",

pages = "571--575",

booktitle = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

address = "United States",

note = "49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 08-11-2015 Through 11-11-2015",

}

TY - GEN

T1 - On optimal sensor collaboration for distributed estimation with individual power constraints

AU - Liu, Sijia

AU - Kar, Swarnendu

AU - Fardad, Makan

AU - Varshney, Pramod Kumar

PY - 2016/2/26

Y1 - 2016/2/26

N2 - In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.

AB - In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.

KW - bilinear relaxation

KW - convex-concave procedure

KW - Distributed estimation

KW - networks

KW - sensor collaboration

KW - ℓ1 norm

UR - http://www.scopus.com/inward/record.url?scp=84969776668&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84969776668&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2015.7421194

DO - 10.1109/ACSSC.2015.7421194

M3 - Conference contribution

AN - SCOPUS:84969776668

SN - 9781467385763

VL - 2016-February

SP - 571

EP - 575

BT - Conference Record - Asilomar Conference on Signals, Systems and Computers

PB - IEEE Computer Society

T2 - 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015

Y2 - 8 November 2015 through 11 November 2015

ER -