Energy-aware sensor selection in field reconstruction

Sijia Liu; Aditya Vempaty; Makan Fardad; Engin Masazade; Pramod K. Varshney

doi:10.1109/LSP.2014.2342198

Energy-aware sensor selection in field reconstruction

Sijia Liu, Aditya Vempaty, Makan Fardad, Engin Masazade, Pramod K. Varshney

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

37 Scopus citations

Abstract

In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ₁ relaxation of the ℓ₀ norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

Original language	English (US)
Article number	6867304
Pages (from-to)	1476-1480
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	21
Issue number	12
DOIs	https://doi.org/10.1109/LSP.2014.2342198
State	Published - Dec 2014

Keywords

Alternating direction method of multipliers
convex relaxation
field reconstruction
proximal gradient method
sensor selection
sparsity

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/LSP.2014.2342198

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title = "Energy-aware sensor selection in field reconstruction",

abstract = "In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1 relaxation of the ℓ0 norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.",

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AU - Liu, Sijia

AU - Vempaty, Aditya

AU - Fardad, Makan

AU - Masazade, Engin

AU - Varshney, Pramod K.

PY - 2014/12

Y1 - 2014/12

N2 - In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1 relaxation of the ℓ0 norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

AB - In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1 relaxation of the ℓ0 norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

KW - Alternating direction method of multipliers

KW - convex relaxation

KW - field reconstruction

KW - proximal gradient method

KW - sensor selection

KW - sparsity

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Energy-aware sensor selection in field reconstruction

Abstract

Keywords

ASJC Scopus subject areas

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