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

Akito Ito, Naoya Namatame, Jin Nakazawa, Hideyuki Tokuda

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

Abstract

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.

Original languageEnglish
Title of host publicationSenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems
Pages355-356
Number of pages2
DOIs
Publication statusPublished - 2012 Dec 1
Event10th ACM Conference on Embedded Networked Sensor Systems, SenSys 2012 - Toronto, ON, Canada
Duration: 2012 Nov 62012 Nov 9

Publication series

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

Other

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

Keywords

  • Compressive sensing
  • Information processing

ASJC Scopus subject areas

  • Computer Networks and Communications

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