Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks
Decreasing the number of data gathered is the most highly effective way to decrease the power consumption for wireless sensor networks. Compressed Data Gathering, as it known to all, is a data collection method in wireless sensor networks, but it cannot achieve sparse sensing as all data need to be...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9314133/ |
| _version_ | 1818640016312631296 |
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| author | Xiaochao Liu Jianping Zhang Guiling Sun Zhouzhou Li |
| author_facet | Xiaochao Liu Jianping Zhang Guiling Sun Zhouzhou Li |
| author_sort | Xiaochao Liu |
| collection | DOAJ |
| description | Decreasing the number of data gathered is the most highly effective way to decrease the power consumption for wireless sensor networks. Compressed Data Gathering, as it known to all, is a data collection method in wireless sensor networks, but it cannot achieve sparse sensing as all data need to be sensed and then transmitted in most practical applications. At the same time, it has been shown the effectiveness of the total variation and low rank constraints in data restoration. In order to enhance the accuracy of data recovery and decrease energy cost in wireless sensor networks, we propose a Multi-Timeslots Data Collection scheme, which includes two aspects: Structure Random Sparse Sampling method and data restoration algorithm with Low Rank and Modified Second-Order Horizontal Total Variation Constraints. By adopting the proposed sampling method, the number of data sensing and transmission is greatly reduced, thereby prolong the network lifetime. We fully exploit temporal stability and low rank characteristics of wireless sensor networks data, and build a temporal-stability based nuclear norm regularization minimization model. Meanwhile, we apply the alternating direction method to solve the problem. The simulation results present that the proposed sampling method has a corresponding enhancement effect on the matrix-completion based data restoration algorithms. In terms of recovery precision, the proposed scheme outperforms the state-of-the-art methods for different types of data in the network. Moreover, with the compression ratio increasing, the proposed scheme can still exactly recover the lost data and the advantages become increasingly obvious. |
| first_indexed | 2024-12-16T23:04:34Z |
| format | Article |
| id | doaj.art-bbaabd9d89114b9a81eb98f365121720 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:04:34Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bbaabd9d89114b9a81eb98f3651217202022-12-21T22:12:36ZengIEEEIEEE Access2169-35362021-01-0197921792910.1109/ACCESS.2021.30492559314133Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor NetworksXiaochao Liu0https://orcid.org/0000-0003-0390-8092Jianping Zhang1Guiling Sun2https://orcid.org/0000-0001-5283-1760Zhouzhou Li3College of Electronic Information and Optical Engineering, Nankai University, Tianjin, ChinaCollege of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USACollege of Electronic Information and Optical Engineering, Nankai University, Tianjin, ChinaCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin, ChinaDecreasing the number of data gathered is the most highly effective way to decrease the power consumption for wireless sensor networks. Compressed Data Gathering, as it known to all, is a data collection method in wireless sensor networks, but it cannot achieve sparse sensing as all data need to be sensed and then transmitted in most practical applications. At the same time, it has been shown the effectiveness of the total variation and low rank constraints in data restoration. In order to enhance the accuracy of data recovery and decrease energy cost in wireless sensor networks, we propose a Multi-Timeslots Data Collection scheme, which includes two aspects: Structure Random Sparse Sampling method and data restoration algorithm with Low Rank and Modified Second-Order Horizontal Total Variation Constraints. By adopting the proposed sampling method, the number of data sensing and transmission is greatly reduced, thereby prolong the network lifetime. We fully exploit temporal stability and low rank characteristics of wireless sensor networks data, and build a temporal-stability based nuclear norm regularization minimization model. Meanwhile, we apply the alternating direction method to solve the problem. The simulation results present that the proposed sampling method has a corresponding enhancement effect on the matrix-completion based data restoration algorithms. In terms of recovery precision, the proposed scheme outperforms the state-of-the-art methods for different types of data in the network. Moreover, with the compression ratio increasing, the proposed scheme can still exactly recover the lost data and the advantages become increasingly obvious.https://ieeexplore.ieee.org/document/9314133/Low rankmodified second-order horizontal total variationmulti-timeslots data collectionwireless sensor networks |
| spellingShingle | Xiaochao Liu Jianping Zhang Guiling Sun Zhouzhou Li Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks IEEE Access Low rank modified second-order horizontal total variation multi-timeslots data collection wireless sensor networks |
| title | Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks |
| title_full | Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks |
| title_fullStr | Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks |
| title_full_unstemmed | Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks |
| title_short | Multi-Timeslots Data Collection With Low Rank and Modified Second-Order Horizontal Total Variation for Wireless Sensor Networks |
| title_sort | multi timeslots data collection with low rank and modified second order horizontal total variation for wireless sensor networks |
| topic | Low rank modified second-order horizontal total variation multi-timeslots data collection wireless sensor networks |
| url | https://ieeexplore.ieee.org/document/9314133/ |
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