Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data

An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. T...

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Autores principales:	Luo Xuegang, Lv Junrui, Wang Juan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/1a7013de5abe4e5f85dbbd278140bd19
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spelling	oai:doaj.org-article:1a7013de5abe4e5f85dbbd278140bd192021-11-22T01:11:25ZMissing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data1563-514710.1155/2021/1336900https://doaj.org/article/1a7013de5abe4e5f85dbbd278140bd192021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1336900https://doaj.org/toc/1563-5147An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. Therefore, it is necessary to exploit a way to reconstruct these lost data with high accuracy. A new data reconstruction method based on spectral k-support norm minimization (DR-SKSNM) is proposed for NB-IoT data, and a relative density-based clustering algorithm is embedded into model processing for improving the accuracy of reconstruction. First, sensors are grouped by similar patterns of measurement. A relative density-based clustering, which can effectively identify clusters in data sets with different densities, is applied to separate sensors into different groups. Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed. Moreover, the alternating direction method of multipliers (ADMM) is used to obtain its optimal solution. Finally, the proposed method is evaluated by using two simulated and real sensor data sources from Panzhihua environmental monitoring station with random missing patterns and consecutive missing patterns. From the simulation results, it is proved that our algorithm performs well, and it can propagate through low-rank characteristics to estimate a large missing region’s value.Luo XuegangLv JunruiWang JuanHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Luo Xuegang Lv Junrui Wang Juan Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data
description	An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. Therefore, it is necessary to exploit a way to reconstruct these lost data with high accuracy. A new data reconstruction method based on spectral k-support norm minimization (DR-SKSNM) is proposed for NB-IoT data, and a relative density-based clustering algorithm is embedded into model processing for improving the accuracy of reconstruction. First, sensors are grouped by similar patterns of measurement. A relative density-based clustering, which can effectively identify clusters in data sets with different densities, is applied to separate sensors into different groups. Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed. Moreover, the alternating direction method of multipliers (ADMM) is used to obtain its optimal solution. Finally, the proposed method is evaluated by using two simulated and real sensor data sources from Panzhihua environmental monitoring station with random missing patterns and consecutive missing patterns. From the simulation results, it is proved that our algorithm performs well, and it can propagate through low-rank characteristics to estimate a large missing region’s value.
format	article
author	Luo Xuegang Lv Junrui Wang Juan
author_facet	Luo Xuegang Lv Junrui Wang Juan
author_sort	Luo Xuegang
title	Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data
title_short	Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data
title_full	Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data
title_fullStr	Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data
title_full_unstemmed	Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data
title_sort	missing data reconstruction based on spectral k-support norm minimization for nb-iot data
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/1a7013de5abe4e5f85dbbd278140bd19
work_keys_str_mv	AT luoxuegang missingdatareconstructionbasedonspectralksupportnormminimizationfornbiotdata AT lvjunrui missingdatareconstructionbasedonspectralksupportnormminimizationfornbiotdata AT wangjuan missingdatareconstructionbasedonspectralksupportnormminimizationfornbiotdata
_version_	1718418266069663744

Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data

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