Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks
Wireless sensor networks (WSNs) have been used extensively in a range of applications to facilitate real-time critical decision-making and situation monitoring. Accurate data analysis and decision-making rely on the quality of the WSN data that have been gathered. However, sensor nodes are prone to...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/709390 |
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| _version_ | 1849471590998212608 |
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| author | Zhiping Kang Honglin Yu Qingyu Xiong Haibo Hu |
| author_facet | Zhiping Kang Honglin Yu Qingyu Xiong Haibo Hu |
| author_sort | Zhiping Kang |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs) have been used extensively in a range of applications to facilitate real-time critical decision-making and situation monitoring. Accurate data analysis and decision-making rely on the quality of the WSN data that have been gathered. However, sensor nodes are prone to faults and are often unreliable because of their intrinsic natures or the harsh environments in which they are used. Using dust data from faulty sensors not only has negative effects on the analysis results and the decisions made but also shortens the network lifetime and can waste huge amounts of limited valuable resources. In this paper, the quality of a WSN service is assessed, focusing on abnormal data derived from faulty sensors. The aim was to develop an effective strategy for locating faulty sensor nodes in WSNs. The proposed fault detection strategy is decentralized, coordinate-free, and node-based, and it uses time series analysis and spatial correlations in the collected data. Experiments using a real dataset from the Intel Berkeley Research Laboratory showed that the algorithm can give a high level of accuracy and a low false alarm rate when detecting faults even when there are many faulty sensors. |
| format | Article |
| id | doaj-art-e87b00e613e54bea9487b61fdd136c53 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-e87b00e613e54bea9487b61fdd136c532025-08-20T03:24:47ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-12-011010.1155/2014/709390709390Spatial-Temporal Correlative Fault Detection in Wireless Sensor NetworksZhiping Kang0Honglin Yu1Qingyu Xiong2Haibo Hu3 Key Laboratory of Ministry of Education for Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing 400044, China Key Laboratory of Optoelectronic Technology and System of Ministry of Education, Chongqing University, Chongqing 400044, China School of Software Engineering, Chongqing University, Chongqing 400044, China Key Laboratory of Ministry of Education for Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing 400044, ChinaWireless sensor networks (WSNs) have been used extensively in a range of applications to facilitate real-time critical decision-making and situation monitoring. Accurate data analysis and decision-making rely on the quality of the WSN data that have been gathered. However, sensor nodes are prone to faults and are often unreliable because of their intrinsic natures or the harsh environments in which they are used. Using dust data from faulty sensors not only has negative effects on the analysis results and the decisions made but also shortens the network lifetime and can waste huge amounts of limited valuable resources. In this paper, the quality of a WSN service is assessed, focusing on abnormal data derived from faulty sensors. The aim was to develop an effective strategy for locating faulty sensor nodes in WSNs. The proposed fault detection strategy is decentralized, coordinate-free, and node-based, and it uses time series analysis and spatial correlations in the collected data. Experiments using a real dataset from the Intel Berkeley Research Laboratory showed that the algorithm can give a high level of accuracy and a low false alarm rate when detecting faults even when there are many faulty sensors.https://doi.org/10.1155/2014/709390 |
| spellingShingle | Zhiping Kang Honglin Yu Qingyu Xiong Haibo Hu Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks International Journal of Distributed Sensor Networks |
| title | Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks |
| title_full | Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks |
| title_fullStr | Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks |
| title_full_unstemmed | Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks |
| title_short | Spatial-Temporal Correlative Fault Detection in Wireless Sensor Networks |
| title_sort | spatial temporal correlative fault detection in wireless sensor networks |
| url | https://doi.org/10.1155/2014/709390 |
| work_keys_str_mv | AT zhipingkang spatialtemporalcorrelativefaultdetectioninwirelesssensornetworks AT honglinyu spatialtemporalcorrelativefaultdetectioninwirelesssensornetworks AT qingyuxiong spatialtemporalcorrelativefaultdetectioninwirelesssensornetworks AT haibohu spatialtemporalcorrelativefaultdetectioninwirelesssensornetworks |