Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks
The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, lit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/10/3369 |
| _version_ | 1811187580536881152 |
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| author | Jian Chen Jie Jia Yansha Deng Xingwei Wang Abdol-Hamid Aghvami |
| author_facet | Jian Chen Jie Jia Yansha Deng Xingwei Wang Abdol-Hamid Aghvami |
| author_sort | Jian Chen |
| collection | DOAJ |
| description | The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms. |
| first_indexed | 2024-04-11T14:04:50Z |
| format | Article |
| id | doaj.art-69a0cdccc10e4413a33e1500c704bd48 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:04:50Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-69a0cdccc10e4413a33e1500c704bd482022-12-22T04:19:55ZengMDPI AGSensors1424-82202018-10-011810336910.3390/s18103369s18103369Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor NetworksJian Chen0Jie Jia1Yansha Deng2Xingwei Wang3Abdol-Hamid Aghvami4Computer Science and Engineering, Northeastern University, Shenyang 110819, ChinaComputer Science and Engineering, Northeastern University, Shenyang 110819, ChinaDepartment of Informatics, King’s College London, London WC2R 2LS, UKComputer Science and Engineering, Northeastern University, Shenyang 110819, ChinaDepartment of Informatics, King’s College London, London WC2R 2LS, UKThe development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms.http://www.mdpi.com/1424-8220/18/10/3369adaptive compressed sensingdata recoverystep size determinationwireless sensor networks |
| spellingShingle | Jian Chen Jie Jia Yansha Deng Xingwei Wang Abdol-Hamid Aghvami Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks Sensors adaptive compressed sensing data recovery step size determination wireless sensor networks |
| title | Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks |
| title_full | Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks |
| title_fullStr | Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks |
| title_full_unstemmed | Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks |
| title_short | Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks |
| title_sort | adaptive compressive sensing and data recovery for periodical monitoring wireless sensor networks |
| topic | adaptive compressed sensing data recovery step size determination wireless sensor networks |
| url | http://www.mdpi.com/1424-8220/18/10/3369 |
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