Joint sparse model based data reconstruction algorithm for wireless sensor network

Yi-yin LIU; Guo-rui LI; Li TIAN

doi:10.11959/j.issn.1000-436x.2016269

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Joint sparse model based data reconstruction algorithm for wireless sensor network

Correspondences | 更新时间：2024-06-05

- Joint sparse model based data reconstruction algorithm for wireless sensor network
- Journal on Communications Vol. 37, Issue Z1, Pages: 211-218(2016)
- 作者机构：
  
  东北大学计算机科学与工程学院，辽宁沈阳 110819
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61402094);The Natural Science Foundation of Liaoning Province(201602254);The Natural Science Foundation of Hebei Province(F2016501076)
- DOI：10.11959/j.issn.1000-436x.2016269
  CLC： TP393
- Online First：2016-10，
  
  Published：25 October 2016
- 稿件说明：
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Yi-yin LIU, Guo-rui LI, Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network[J]. Journal on Communications, 2016, 37(Z1): 211-218.
DOI：

Yi-yin LIU, Guo-rui LI, Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network[J]. Journal on Communications, 2016, 37(Z1): 211-218. DOI： 10.11959/j.issn.1000-436x.2016269.

摘要

无线传感器网络中数据具有较强联合稀疏特性，应用压缩感知理论，通过联合编码压缩数据，再使用联合解码进行还原，可实现低采样代价收集传感数据。提出了一种基于联合稀疏模型与压缩感知理论的同步子空间追踪算法，以稀疏特性为先验知识，通过回溯迭代方式，判断并选取合适的联合子空间，用更少量观测值实现原始传感数据的精确重构。与SCoSaMP算法、SP算法在不同稀疏特性和不同采样率下相比较，同步子空间追踪算法具有较好的恢复性能。

Abstract

The data of wireless sensor network has strong joint sparse characteristics

by utilizing compressed sensing theory

compressed data by joint encoding

and then reconstructed the data by joint decoding

the sensed data can be gathered with low computational cost.A synchronous subspace pursuit algorithm based on joint sparse model and com-pressed sensing theory was proposed.By utilizing the sparsity of the sensed data

it selected the correct joint subspace and reconstruct the original signal group accurately with fewer observations in a backtracking iterative manner.Com-pared with SCoSaMP algorithm and SP algorithm

the proposed algorithm presents better data reconstruction perform-ance under the conditions of different sparsity and sampling rate.

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references

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