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文章摘要
王茜楠,白勇.面向物联网的稀疏采样与近似重构技术研究[J].海南大学学报编辑部:自然科学版,2019,37(2):.
面向物联网的稀疏采样与近似重构技术研究
Research on Sparse Sampling and Approximate Reconstruction for Internet of Things
投稿时间:2019-01-09  修订日期:2019-04-03
DOI:10.15886/j.cnki.hdxbzkb.2019.0018
中文关键词: 时空相关性;稀疏采样;矩阵填充;采样比率;重构精度
英文关键词: Spatial-temporal correlation, Sparse sampling, Matrix filling, Sampling ratio, Reconstruction accuracy
基金项目:国家自然科学基金(61561017);海南省科技厅重大科技计划(ZDKJ2016015)
作者单位E-mail
王茜楠 海南大学 wang_q_n@163.com 
白勇 海南大学 bai@hainu.edu.cn 
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中文摘要:
      通过建立具有时空相关性的物联网无线传感器网络节点数据模型,对由数据样本构成的矩阵进行稀疏采样及矩阵填充的近似重构技术来实现数据的恢复,从而得到矩阵重构可行性(即采样比率和重构精度)与数据相关性的关系。仿真显示节点数据之间的时空相关性与矩阵重构可行性之间存在着密切的关系。同一采样比率条件下,时空相关性越大,重构精度越高;同样重构精度下,时空相关性越大,需要的采样比率越低。
英文摘要:
      By establishing the data model of wireless sensor network nodes with spatial-temporal correlation, and using the approximate reconstruction technique of sparse sampling and matrix filling to restore the data, the relationship between the feasibility of matrix reconstruction (i.e. sampling ratio and reconstruction accuracy) and the data correlation is obtained. Simulation results show that there is a close relationship between the spatial-temporal correlation of node data and the feasibility of matrix reconstruction. Under the same sampling ratio, the greater the spatial-temporal correlation, the higher the reconstruction accuracy; under the same reconstruction accuracy, the greater the spatial-temporal correlation, the lower the sampling ratio required.
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