Distributed Sensor Selection for Field Estimation

Conference Paper (2017)
Author(s)

Sijia Liu (University of Michigan)

S.P. Chepuri (TU Delft - Signal Processing Systems)

G. Leus (TU Delft - Signal Processing Systems)

A.O. Hero (University of Michigan)

Research Group
Signal Processing Systems
DOI related publication
https://doi.org/10.1109/ICASSP.2017.7952959
More Info
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Publication Year
2017
Language
English
Research Group
Signal Processing Systems
Pages (from-to)
4257-4261
ISBN (electronic)
978-1-5090-4117-6

Abstract

We study the sensor selection problem for field estimation, where a best subset of sensors is activated to monitor a spatially correlated random field. Different from most commonly used centralized selection algorithms, we propose a decentralized architecture where sensor selection can be carried out in a distributed way and by the sensors themselves. A decentralized approach is essential since each sensor has access only to the information (e.g., correlation) in its neighborhood. To make distributed optimization possible, we decompose the global cost function into local cost functions that require only the information in local neighborhoods of sensors. We then employ the alternating direction method of multipliers (ADMM) to solve the proposed sensor selection problem. In our algorithm, each sensor solves small-scale optimization problems, and communicates directly only with its immediate neighbors. Numerical results are provided to show the effectiveness of our approach.

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