Energy-saving data collection based on compressed sensing in chain wireless sensor networks

GU Yi-chen; HUANG Ru

doi:10.13873/J.1000-9787(2015)11-0069-03

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Energy-saving data collection based on compressed sensing in chain wireless sensor networks

更新时间：2025-09-11

- Energy-saving data collection based on compressed sensing in chain wireless sensor networks
- Vol. 34, Issue 11, Pages: 69-71(2015)
- 作者机构：
  
  华东理工大学信息科学与工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2015)11-0069-03
  CLC： TN929.5;TP212.9
- Published：2015
- 稿件说明：
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[1]顾逸宸,黄如.基于压缩感知的链型无线传感器网络节能数据收集[J].传感器与微系统,2015,34(11):69-71.

GU Yi-chen, HUANG Ru. Energy-saving data collection based on compressed sensing in chain wireless sensor networks[J]. 2015, 34(11): 69-71.
[1]顾逸宸,黄如.基于压缩感知的链型无线传感器网络节能数据收集[J].传感器与微系统,2015,34(11):69-71. DOI： 10.13873/J.1000-9787(2015)11-0069-03.

GU Yi-chen, HUANG Ru. Energy-saving data collection based on compressed sensing in chain wireless sensor networks[J]. 2015, 34(11): 69-71. DOI： 10.13873/J.1000-9787(2015)11-0069-03.

摘要

无线传感器网络（WSNs）中节点受体积、功率、成本等限制而导致了节点能量、生命周期有限的问题。提出一种基于压缩感知算法的无线传感器网络节能优化方法

并结合无线传感器网络中的链型拓扑网络模型

给出基于压缩感知理论的节能网络数据传输模型。通过理论分析比较表明压缩感知方法在节能方面的优越性

然后在得出的网络能耗模型的基础上进行仿真。仿真结果表明:压缩感知方法有效减少了网络能耗。

Abstract

Node in wireless sensor networks is limited by volume

power

cost

etc

so that energy and lifetime of system are limited. Propose an optimal method based on compressed sensing for energy-saving of wireless sensor networks（ WSNs）

combining chain topology network model

propose energy-saving network data transmission model based on compressed sensing theory. Advantage of compressed sensing method in energy-saving is verified by theoretical analysis and comparison

and then simulate on the basis of energy consumption model of the network. Simulation results show that the compressed sensing method effectively reduces network energy consumption.

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references

基于证据理论的多传感器加权融合改进方法 [J]. 李军,锁斌,李顺. 计算机测量与控制 . 2011(10)

基于压缩传感理论的随机等效采样信号的重构 [J]. 赵贻玖,戴志坚,王厚军. 仪器仪表学报 . 2011(02)

Iterative hard thresholding for compressed sensing [J] . Thomas Blumensath,Mike E. Davies. Applied and Computational Harmonic Analysis . 2009 (3)

CoSaMP: Iterative signal recovery from incomplete and inaccurate samples [J] . D. Needell,J.A. Tropp. Applied and Computational Harmonic Analysis . 2008 (3)

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