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.
|Original language||English (US)|
|Title of host publication||IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)|
|Number of pages||5|
|State||Published - 2017|
Bibliographical noteKAUST Repository Item: Exported on 2022-06-23
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work was partially supported by the US Army Research Office under grant W911NF-15-1-0479 and the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.