Pattern-based matrix-size optimization algorithm for compressive sensing in real-world wireless sensor networks

Akito Ito, Naoya Namatame, Jin Nakazawa, Hideyuki Tokuda

研究成果: Conference contribution

抄録

Compressive Sensing (CS) is a novel approach for data representation, which can represent signals at a rate below the Nyquist rate with low computation costs on encoder. For these characteristics, CS is very suitable for low power sensor nodes to save power consumption that is a primary problem inWireless Sensor Networks (WSN). But there are many problems when using CS in a real environment. One of these is that pattern of sensor values change dynamically. It decreases the efficiency of power consumption and accuracy of recovery. To solve the problem, we propose Pattern-based Matrix-size Optimization Algorithm (PMOA), which aims to improve the accuracy of exact recovery and power consumption.

本文言語English
ホスト出版物のタイトルSenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems
ページ355-356
ページ数2
DOI
出版ステータスPublished - 2012 12月 1
イベント10th ACM Conference on Embedded Networked Sensor Systems, SenSys 2012 - Toronto, ON, Canada
継続期間: 2012 11月 62012 11月 9

出版物シリーズ

名前SenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems

Other

Other10th ACM Conference on Embedded Networked Sensor Systems, SenSys 2012
国/地域Canada
CityToronto, ON
Period12/11/612/11/9

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信

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