講演名 2013-07-18
Pattern-based Matrix-size Optimization Algorithm for Compressive Sensing in Real-world Body Sensor Networks
,
PDFダウンロードページ PDFダウンロードページへ
抄録(和)
抄録(英) 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 in Wireless Sensor Networks (WSN). But there are many problems when using CS in a real environment, especially in Body Sensor Network which aims to monitor human health and detect context. We attack the dynamics of sensor data problem that decrease 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 consump- tion. We performed experiments both in real world and simulation and the result show our approach is effective in energy consumption and reliable. The result shows our approach can achieve the improvement of battery life-time by 11.7%.
キーワード(和)
キーワード(英) Compressive Sensing / Information Processing / System Architecture
資料番号 NS2013-41,RCS2013-92,SR2013-29,ASN2013-59
発行日

研究会情報
研究会 ASN
開催期間 2013/7/10(から1日開催)
開催地(和)
開催地(英)
テーマ(和)
テーマ(英)
委員長氏名(和)
委員長氏名(英)
副委員長氏名(和)
副委員長氏名(英)
幹事氏名(和)
幹事氏名(英)
幹事補佐氏名(和)
幹事補佐氏名(英)

講演論文情報詳細
申込み研究会 Ambient intelligence and Sensor Networks(ASN)
本文の言語 ENG
タイトル(和)
サブタイトル(和)
タイトル(英) Pattern-based Matrix-size Optimization Algorithm for Compressive Sensing in Real-world Body Sensor Networks
サブタイトル(和)
キーワード(1)(和/英) / Compressive Sensing
第 1 著者 氏名(和/英) / Akito ITO
第 1 著者 所属(和/英)
Graduate School of Media and Governance, Keio Univ.
発表年月日 2013-07-18
資料番号 NS2013-41,RCS2013-92,SR2013-29,ASN2013-59
巻番号(vol) vol.113
号番号(no) 132
ページ範囲 pp.-
ページ数 6
発行日