Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data
Due to the dynamic network topology and limit of resources, fault diagnosis for wireless sensor networks is difficult. The existing diagnostic methods consume a lot of communication bandwidth and node resources, which lead to heavy burden of the resources limited network. This paper presents a passi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-06-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/590430 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850209943796318208 |
|---|---|
| author | Lufeng Mo Jinrong Li Guoying Wang Liping Chen |
| author_facet | Lufeng Mo Jinrong Li Guoying Wang Liping Chen |
| author_sort | Lufeng Mo |
| collection | DOAJ |
| description | Due to the dynamic network topology and limit of resources, fault diagnosis for wireless sensor networks is difficult. The existing diagnostic methods consume a lot of communication bandwidth and node resources, which lead to heavy burden of the resources limited network. This paper presents a passive diagnosis method used for fault detection and fault classification based on the time domain features of sensing data (TDSD). Firstly, the feature extraction and analysis of the sensing data are carried out using one-dimensional discrete Gabor transform, and then the data are diagnosed and classified with Self-Organizing Maps (SOM) neural network; finally the current network status and identifying the fault cause are determined. The results show that, comparing with other methods, this method has fewer burdens in network communication, better diagnostic accuracy rate and classification results, and so forth, and it has a high diagnostic accuracy especially for both node fault and network fault. |
| format | Article |
| id | doaj-art-96bc20e3458e43b09b7bb02edd37e06d |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-96bc20e3458e43b09b7bb02edd37e06d2025-08-20T02:09:52ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-06-011110.1155/2015/590430590430Passive Diagnosis for WSNs Using Time Domain Features of Sensing DataLufeng Mo0Jinrong Li1Guoying Wang2Liping Chen3 Joint Laboratory on Internet of Things and Global Climate Change, Zhejiang A&F University, Zhejiang, Lin'an 311300, China Joint Laboratory on Internet of Things and Global Climate Change, Zhejiang A&F University, Zhejiang, Lin'an 311300, China Department of Computer Science, Xi'an Jiaotong University, Xi'an 710049, China Joint Laboratory on Internet of Things and Global Climate Change, Zhejiang A&F University, Zhejiang, Lin'an 311300, ChinaDue to the dynamic network topology and limit of resources, fault diagnosis for wireless sensor networks is difficult. The existing diagnostic methods consume a lot of communication bandwidth and node resources, which lead to heavy burden of the resources limited network. This paper presents a passive diagnosis method used for fault detection and fault classification based on the time domain features of sensing data (TDSD). Firstly, the feature extraction and analysis of the sensing data are carried out using one-dimensional discrete Gabor transform, and then the data are diagnosed and classified with Self-Organizing Maps (SOM) neural network; finally the current network status and identifying the fault cause are determined. The results show that, comparing with other methods, this method has fewer burdens in network communication, better diagnostic accuracy rate and classification results, and so forth, and it has a high diagnostic accuracy especially for both node fault and network fault.https://doi.org/10.1155/2015/590430 |
| spellingShingle | Lufeng Mo Jinrong Li Guoying Wang Liping Chen Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data International Journal of Distributed Sensor Networks |
| title | Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data |
| title_full | Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data |
| title_fullStr | Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data |
| title_full_unstemmed | Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data |
| title_short | Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data |
| title_sort | passive diagnosis for wsns using time domain features of sensing data |
| url | https://doi.org/10.1155/2015/590430 |
| work_keys_str_mv | AT lufengmo passivediagnosisforwsnsusingtimedomainfeaturesofsensingdata AT jinrongli passivediagnosisforwsnsusingtimedomainfeaturesofsensingdata AT guoyingwang passivediagnosisforwsnsusingtimedomainfeaturesofsensingdata AT lipingchen passivediagnosisforwsnsusingtimedomainfeaturesofsensingdata |