@inproceedings{d03abe639c9b46b492462f706b16d432,
title = "Energy-efficient joint estimation in sensor networks: Analog vs. Digital",
abstract = "Sensor networks in which energy is a limited resource so that energy consumption must be minimized for the intended application are considered. In this context, an energy-efficient method for the joint estimation of an unknown analog source under a given distortion constraint is proposed. The approach is purely analog, in which each sensor simply amplifies and forwards the noise-corrupted analog observation to the fusion center for joint estimation. The total transmission power across all the sensor nodes is minimized while satisfying a distortion requirement on the joint estimate. The energy efficiency of this analog approach is compared with previously proposed digital approaches with and without coding. It is shown in our simulation that the analog approach is more energy-efficient than the digital system without coding, and in some cases outperforms the digital system with optimal coding.",
author = "Shuguang Cui and Xiao, {Jin Jun} and Goldsmith, {Andrea J.} and Luo, {Zhi Quan} and Poor, {H. Vincent}",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 ; Conference date: 18-03-2005 Through 23-03-2005",
year = "2005",
doi = "10.1109/ICASSP.2005.1416116",
language = "English (US)",
isbn = "0780388747",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "745--748",
booktitle = "2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions",
address = "United States",
}