@inproceedings{dbb27c05ece048fca80ae775907a83e8,
title = "Sparsity-aware field estimation via ordinary Kriging",
abstract = "In this paper, we consider the problem of estimating a spatially varying field in a wireless sensor network, where resource constraints limit the number of sensors selected in the network that provide their measurements for field estimation. Based on a one-to-one correspondence between the selected sensors and the nonzero elements of Kriging weights, we propose a sparsity-promoting ordinary Kriging approach where we minimize the Kriging error variance while penalizing the number of nonzero Kriging weights. This yields a combinatorial optimization problem, which is intractable in general. To solve the proposed non-convex optimization problem, we employ the alternating direction method of multipliers (ADMM) and the reweighted ℓ1 minimization method, respectively. Numerical results are provided to illustrate the effectiveness of our proposed approaches that provide a balance between the estimation accuracy and the number of selected sensors.",
keywords = "Field estimation, alternating direction method of multipliers, convex optimization, sensor networks, sparsity",
author = "Sijia Liu and Engin Masazade and Makan Fardad and Varshney, {Pramod K.}",
year = "2014",
doi = "10.1109/ICASSP.2014.6854342",
language = "English (US)",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3948--3952",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}