Greedy Sensor Placement With Cost Constraints

The problem of optimally placing sensors under a cost constraint arises naturally in the design of industrial and commercial products, as well as in scientific experiments. We consider a relaxation of the full optimization formulation of this problem and then extend a well-established greedy algorit...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 19; no. 7; pp. 2642 - 2656
Main Authors: Clark, Emily, Askham, Travis, Brunton, Steven L., Nathan Kutz, J.
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
computation theory
computational and artificial intelligence
Computational fluid dynamics
data preprocessing
data processing
data systems
Error detection
Face recognition
Fluid mechanics
Greedy algorithms
Interpolation
Matrix decomposition
Optimization
Placement
Sensor phenomena and characterization
Sensors
Singular value decomposition
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:The problem of optimally placing sensors under a cost constraint arises naturally in the design of industrial and commercial products, as well as in scientific experiments. We consider a relaxation of the full optimization formulation of this problem and then extend a well-established greedy algorithm for the optimal sensor placement problem without cost constraints. We demonstrate the effectiveness of this algorithm on the datasets related to facial recognition, climate science, and fluid mechanics. This algorithm is scalable and often identifies sparse sensors with near-optimal reconstruction performance, while dramatically reducing the overall cost of the sensors. We find that the cost-error landscape varies by application, with intuitive connections to the underlying physics. In addition, we include experiments for various pre-processing techniques and find that a popular technique based on the singular value decomposition is often suboptimal.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2018.2887044