Distributed estimation based on observations prediction in wireless sensor networks

Taha Bouchoucha; Mohammed F A Ahmed; Tareq Y. Al-Naffouri; Mohamed-Slim Alouini

doi:10.1109/LSP.2015.2411852

Distributed estimation based on observations prediction in wireless sensor networks

Taha Bouchoucha, Mohammed F A Ahmed, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.

Original language	English (US)
Pages (from-to)	1530-1533
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	22
Issue number	10
DOIs	https://doi.org/10.1109/LSP.2015.2411852
State	Published - Mar 11 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the King Abdulaziz City of Science and Technology (KACST) under Grant AT-34-345. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Vincenzo Matta.

ASJC Scopus subject areas

Signal Processing
Applied Mathematics
Electrical and Electronic Engineering

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10.1109/LSP.2015.2411852

Cite this

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title = "Distributed estimation based on observations prediction in wireless sensor networks",

abstract = "We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. {\textcopyright} 2015 IEEE.",

author = "Taha Bouchoucha and Ahmed, {Mohammed F A} and Al-Naffouri, {Tareq Y.} and Mohamed-Slim Alouini",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported by the King Abdulaziz City of Science and Technology (KACST) under Grant AT-34-345. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Vincenzo Matta.",

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AU - Bouchoucha, Taha

AU - Ahmed, Mohammed F A

AU - Al-Naffouri, Tareq Y.

AU - Alouini, Mohamed-Slim

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported by the King Abdulaziz City of Science and Technology (KACST) under Grant AT-34-345. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Vincenzo Matta.

PY - 2015/3/11

Y1 - 2015/3/11

N2 - We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.

AB - We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.

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JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

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Distributed estimation based on observations prediction in wireless sensor networks

Abstract

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