@inproceedings{ab9e1b083bfd4337b6d6a7a228897dbf,
title = "On optimal sensor collaboration for distributed estimation with individual power constraints",
abstract = "In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.",
keywords = "Distributed estimation, bilinear relaxation, convex-concave procedure, networks, sensor collaboration, ℓ1 norm",
author = "Sijia Liu and Swarnendu Kar and Makan Fardad and Varshney, {Pramod K.}",
note = "Funding Information: This work was supported by U.S. Air Force Office of Scientific Research (AFOSR) under grant FA9550-10-1-0458, and National Science Foundation under awards CNS-1329885 and CMMI-0927509.; 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 08-11-2015 Through 11-11-2015",
year = "2016",
month = feb,
day = "26",
doi = "10.1109/ACSSC.2015.7421194",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "571--575",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015",
address = "United States",
}