Sijia Liu, Sundeep Prabhakar Chepuri, Geert Leus, Alfred O. Hero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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 languageEnglish (US)
Title of host publicationIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Number of pages5
StatePublished - 2017
Externally publishedYes


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