| Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements | |
| Alternative Title | Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements |
| Wang Kai1; Liu Yulin2; Wan Qun3; Jing Xiaojun4 | |
| 2015 | |
| Source Publication | CHINESE JOURNAL OF ELECTRONICS
 |
| ISSN | 1022-4653 |
| Volume | 24Issue:2Pages:388-392 |
| Abstract | In Wireless sensor networks (WSNs), missed measurements may be caused by the sensor malfunction and interruption of communication between sensor nodes. The feasibility of exact recovery of WSNs data with missed measurements is analyzed in the framework of compressed sensing. A new incomplete measurement model was developed and the data reconstruction algorithm was proposed. The required number of the missing measurements and the sparsity condition of network data are found for exact compressed sensing of WSNs data. Theoretical derivation shows that a WSNs data of length N with no more than M/(log(N/M)+1) nonzero coefficients can be exactly recovered with M Gaussian measurements, provided that fraction of the missed measurements is less than a quarter of the Restricted isometry property (RIP) constant squared. Simulation results validate the theoretical results. |
| Other Abstract | In Wireless sensor networks (WSNs), missed measurements may be caused by the sensor malfunction and interruption of communication between sensor nodes. The feasibility of exact recovery of WSNs data with missed measurements is analyzed in the framework of compressed sensing. A new incomplete measurement model was developed and the data reconstruction algorithm was proposed. The required number of the missing measurements and the sparsity condition of network data are found for exact compressed sensing ofWSNs data. Theoretical derivation shows that aWSNs data of length N with no more than M/(log(N/M)+1) nonzero coefficients can be exactly recovered with M Gaussian measurements, provided that fraction of the missed measurements is less than a quarter of the Restricted isometry property (RIP) constant squared. Simulation results validate the theoretical results. |
| Keyword | Wireless sensor networks Compressed sensing Missed measurements Data reconstruction |
| Indexed By | CSCD |
| Language | 英语 |
| Funding Project | [Program for New Century Excellent Talents in University] ; [Program for Innovative Research Team in University of Chongqing] ; [Key Project of Chongqing Natural Science Foundation] ; [Program for Fundamental and Advanced Research of Chongqing] |
| CSCD ID | CSCD:5555360 |
| Citation statistics |
Cited Times:3[CSCD]
[CSCD Record]
|
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/154723 |
| Collection | 中国科学院金属研究所 |
| Affiliation | 1.Chongqing Commun Institute, Chongqing 400035, Peoples R China 2.中国科学院金属研究所 3.University Elect Sci & Technol China, Chengdu 610054, Peoples R China 4.Beijing University Posts & Telecommun, Beijing 100876, Peoples R China |
| Recommended Citation GB/T 7714 | Wang Kai,Liu Yulin,Wan Qun,et al. Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements[J]. CHINESE JOURNAL OF ELECTRONICS,2015,24(2):388-392. |
| APA | Wang Kai,Liu Yulin,Wan Qun,&Jing Xiaojun.(2015).Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements.CHINESE JOURNAL OF ELECTRONICS,24(2),388-392. |
| MLA | Wang Kai,et al."Compressed Sensing of Wireless Sensor Networks Data with Missed Measurements".CHINESE JOURNAL OF ELECTRONICS 24.2(2015):388-392. |
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