Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint

Ajib Setyo Arifin, Tomoaki Ohtsuki

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

Abstract

We consider distributed estimation in wireless sensor networks (WSNs). Using linear minimum mean squared error (LMMSE) estimator, we derive mean squared error (MSE) to measure the quality of estimation. We introduce a global signal to noise ratio (SNR), where we can derive capacity of data collection in terms of mutual information as reciprocity of MSE. Based on the global SNR, we derive equal power allocation and optimal power allocation in orthogonal multiple access channel (MAC) models. We also derive asymptotic behavior of the global SNR when the power and the number of sensors become unlimited. We minimize MSE as well as maximize mutual information by considering total and individual power constraints. We show that MSE and mutual information of equal power allocation outperforms optimal power allocation. Moreover, system with individual power constraint is worse than the system without that because the suggested power to be allocated is constrained by maximum transmit power of the sensors.

Original languageEnglish
Title of host publication2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9786163618238
DOIs
Publication statusPublished - 2014 Feb 12
Event2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014 - Chiang Mai, Thailand
Duration: 2014 Dec 92014 Dec 12

Other

Other2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
CountryThailand
CityChiang Mai
Period14/12/914/12/12

Fingerprint

Wireless sensor networks
Signal to noise ratio
Sensors

Keywords

  • distributed estimation
  • individual power constraint
  • mean squared error (MSE)
  • mutual information
  • orthogonal MAC

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems

Cite this

Arifin, A. S., & Ohtsuki, T. (2014). Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint. In 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014 [7041523] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2014.7041523

Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint. / Arifin, Ajib Setyo; Ohtsuki, Tomoaki.

2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7041523.

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

Arifin, AS & Ohtsuki, T 2014, Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint. in 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014., 7041523, Institute of Electrical and Electronics Engineers Inc., 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014, Chiang Mai, Thailand, 14/12/9. https://doi.org/10.1109/APSIPA.2014.7041523
Arifin AS, Ohtsuki T. Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint. In 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 7041523 https://doi.org/10.1109/APSIPA.2014.7041523
Arifin, Ajib Setyo ; Ohtsuki, Tomoaki. / Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint. 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014. Institute of Electrical and Electronics Engineers Inc., 2014.
@inproceedings{1001b1f8b9e04d80b5bcce8b239423a3,
title = "Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint",
abstract = "We consider distributed estimation in wireless sensor networks (WSNs). Using linear minimum mean squared error (LMMSE) estimator, we derive mean squared error (MSE) to measure the quality of estimation. We introduce a global signal to noise ratio (SNR), where we can derive capacity of data collection in terms of mutual information as reciprocity of MSE. Based on the global SNR, we derive equal power allocation and optimal power allocation in orthogonal multiple access channel (MAC) models. We also derive asymptotic behavior of the global SNR when the power and the number of sensors become unlimited. We minimize MSE as well as maximize mutual information by considering total and individual power constraints. We show that MSE and mutual information of equal power allocation outperforms optimal power allocation. Moreover, system with individual power constraint is worse than the system without that because the suggested power to be allocated is constrained by maximum transmit power of the sensors.",
keywords = "distributed estimation, individual power constraint, mean squared error (MSE), mutual information, orthogonal MAC",
author = "Arifin, {Ajib Setyo} and Tomoaki Ohtsuki",
year = "2014",
month = "2",
day = "12",
doi = "10.1109/APSIPA.2014.7041523",
language = "English",
isbn = "9786163618238",
booktitle = "2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Global SNR maximization for distributed estimation and capacity of data collection in wireless sensor networks using individual power constraint

AU - Arifin, Ajib Setyo

AU - Ohtsuki, Tomoaki

PY - 2014/2/12

Y1 - 2014/2/12

N2 - We consider distributed estimation in wireless sensor networks (WSNs). Using linear minimum mean squared error (LMMSE) estimator, we derive mean squared error (MSE) to measure the quality of estimation. We introduce a global signal to noise ratio (SNR), where we can derive capacity of data collection in terms of mutual information as reciprocity of MSE. Based on the global SNR, we derive equal power allocation and optimal power allocation in orthogonal multiple access channel (MAC) models. We also derive asymptotic behavior of the global SNR when the power and the number of sensors become unlimited. We minimize MSE as well as maximize mutual information by considering total and individual power constraints. We show that MSE and mutual information of equal power allocation outperforms optimal power allocation. Moreover, system with individual power constraint is worse than the system without that because the suggested power to be allocated is constrained by maximum transmit power of the sensors.

AB - We consider distributed estimation in wireless sensor networks (WSNs). Using linear minimum mean squared error (LMMSE) estimator, we derive mean squared error (MSE) to measure the quality of estimation. We introduce a global signal to noise ratio (SNR), where we can derive capacity of data collection in terms of mutual information as reciprocity of MSE. Based on the global SNR, we derive equal power allocation and optimal power allocation in orthogonal multiple access channel (MAC) models. We also derive asymptotic behavior of the global SNR when the power and the number of sensors become unlimited. We minimize MSE as well as maximize mutual information by considering total and individual power constraints. We show that MSE and mutual information of equal power allocation outperforms optimal power allocation. Moreover, system with individual power constraint is worse than the system without that because the suggested power to be allocated is constrained by maximum transmit power of the sensors.

KW - distributed estimation

KW - individual power constraint

KW - mean squared error (MSE)

KW - mutual information

KW - orthogonal MAC

UR - http://www.scopus.com/inward/record.url?scp=84949924491&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949924491&partnerID=8YFLogxK

U2 - 10.1109/APSIPA.2014.7041523

DO - 10.1109/APSIPA.2014.7041523

M3 - Conference contribution

AN - SCOPUS:84949924491

SN - 9786163618238

BT - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -