Signal model based compressed sampling for wireless sensor array network

K. Ai Yu; Ming Yin; Wu Liantao; Zhi Wang

doi:10.1109/ICICS.2013.6782799

Signal model based compressed sampling for wireless sensor array network

K. Ai Yu
, Ming Yin
, Wu Liantao
, Zhi Wang^*

^*Corresponding author for this work

Zhejiang University

Research output: Contribution to conference › Paper › peer-review

Abstract

High sampling rate signal acquisition is challenging for wireless platform in terms of energy supply and transmission delay. Instead of performing compression at sensor node or having in-network processing for data been sampled at Nyquist rate, Compressive Sensing (CS) is applied to enable real time wireless sensor network with strict energy and processing constraints by significantly reducing the sensor data volume that needs to be transmitted over wireless channels. This is accomplished by random sampling at sensor nodes without extra processing and a mixture model based collaborative signal reconstruction in the fusion centre. This method increases signal reconstruction performance while reducing the volume of transmission data. Analysis of data from experiment and simulation are provided, and the performance are evaluated by implementing a prototype wireless platform.

Original language	English
DOIs	https://doi.org/10.1109/ICICS.2013.6782799
State	Published - 2013
Externally published	Yes
Event	9th International Conference on Information, Communications and Signal Processing, ICICS 2013 - Tainan, Taiwan, Province of China Duration: 10 Dec 2013 → 13 Dec 2013

Conference

Conference	9th International Conference on Information, Communications and Signal Processing, ICICS 2013
Country/Territory	Taiwan, Province of China
City	Tainan
Period	10/12/13 → 13/12/13

Keywords

compressed sensing
direction of arrival estimation
sensor arrays
wireless sensor networks

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10.1109/ICICS.2013.6782799

Cite this

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title = "Signal model based compressed sampling for wireless sensor array network",

abstract = "High sampling rate signal acquisition is challenging for wireless platform in terms of energy supply and transmission delay. Instead of performing compression at sensor node or having in-network processing for data been sampled at Nyquist rate, Compressive Sensing (CS) is applied to enable real time wireless sensor network with strict energy and processing constraints by significantly reducing the sensor data volume that needs to be transmitted over wireless channels. This is accomplished by random sampling at sensor nodes without extra processing and a mixture model based collaborative signal reconstruction in the fusion centre. This method increases signal reconstruction performance while reducing the volume of transmission data. Analysis of data from experiment and simulation are provided, and the performance are evaluated by implementing a prototype wireless platform.",

keywords = "compressed sensing, direction of arrival estimation, sensor arrays, wireless sensor networks",

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doi = "10.1109/ICICS.2013.6782799",

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note = "9th International Conference on Information, Communications and Signal Processing, ICICS 2013 ; Conference date: 10-12-2013 Through 13-12-2013",

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T1 - Signal model based compressed sampling for wireless sensor array network

AU - Yu, K. Ai

AU - Yin, Ming

AU - Liantao, Wu

AU - Wang, Zhi

PY - 2013

Y1 - 2013

N2 - High sampling rate signal acquisition is challenging for wireless platform in terms of energy supply and transmission delay. Instead of performing compression at sensor node or having in-network processing for data been sampled at Nyquist rate, Compressive Sensing (CS) is applied to enable real time wireless sensor network with strict energy and processing constraints by significantly reducing the sensor data volume that needs to be transmitted over wireless channels. This is accomplished by random sampling at sensor nodes without extra processing and a mixture model based collaborative signal reconstruction in the fusion centre. This method increases signal reconstruction performance while reducing the volume of transmission data. Analysis of data from experiment and simulation are provided, and the performance are evaluated by implementing a prototype wireless platform.

AB - High sampling rate signal acquisition is challenging for wireless platform in terms of energy supply and transmission delay. Instead of performing compression at sensor node or having in-network processing for data been sampled at Nyquist rate, Compressive Sensing (CS) is applied to enable real time wireless sensor network with strict energy and processing constraints by significantly reducing the sensor data volume that needs to be transmitted over wireless channels. This is accomplished by random sampling at sensor nodes without extra processing and a mixture model based collaborative signal reconstruction in the fusion centre. This method increases signal reconstruction performance while reducing the volume of transmission data. Analysis of data from experiment and simulation are provided, and the performance are evaluated by implementing a prototype wireless platform.

KW - compressed sensing

KW - direction of arrival estimation

KW - sensor arrays

KW - wireless sensor networks

UR - https://www.scopus.com/pages/publications/84899084410

U2 - 10.1109/ICICS.2013.6782799

DO - 10.1109/ICICS.2013.6782799

M3 - 论文

AN - SCOPUS:84899084410

T2 - 9th International Conference on Information, Communications and Signal Processing, ICICS 2013

Y2 - 10 December 2013 through 13 December 2013

ER -

Signal model based compressed sampling for wireless sensor array network

Abstract

Conference

Keywords

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