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| Multi-sensor Data Fusion Based on Consistency Test and Sliding Window Variance Weighted Algorithm in Sensor Networks | |
| Shu, Jian; Hong, Ming; Zheng, Wei; Sun, Li-Min; Ge, Xu | |
| 2013 | |
| Source | COMPUTER SCIENCE AND INFORMATION SYSTEMS
 |
| ISSN | 1820-0214 |
| Volume | 10Issue:1Pages:197-214 |
| English Abstract | In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and the stability is decreasing in wireless sensor networks, a novel algorithm is proposed based on consistency test and sliding-windowed variance weighted. The internal noise is considered to be the main factor of the problem in this paper. And we can use consistency test method to diagnose whether the mean of sensor data is offset. So the abnormal data is amended or removed. Then, the result of fused data can be calculated by using sliding window variance weighted algorithm according to normal and amended data. Simulation results show that the misdiagnosis rate of the abnormal data can be reduced to 3% by using improved consistency test with the threshold set to [0.05, 0.15], so the abnormal sensor data can be diagnosed more accurately and the stability can be increased. The accuracy of the fused data can be improved effectively when the window length is set to 2. Under the condition that the abnormal sensor data has been amended or removed, the proposed algorithm has better performances on precision compared with other existing algorithms.; In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and the stability is decreasing in wireless sensor networks, a novel algorithm is proposed based on consistency test and sliding-windowed variance weighted. The internal noise is considered to be the main factor of the problem in this paper. And we can use consistency test method to diagnose whether the mean of sensor data is offset. So the abnormal data is amended or removed. Then, the result of fused data can be calculated by using sliding window variance weighted algorithm according to normal and amended data. Simulation results show that the misdiagnosis rate of the abnormal data can be reduced to 3% by using improved consistency test with the threshold set to [0.05, 0.15], so the abnormal sensor data can be diagnosed more accurately and the stability can be increased. The accuracy of the fused data can be improved effectively when the window length is set to 2. Under the condition that the abnormal sensor data has been amended or removed, the proposed algorithm has better performances on precision compared with other existing algorithms. |
| Indexed Type | SCI |
| Keyword | Wireless Sensor Networks Data Fusion Consistency Test Sliding Window Variance Weighted |
| Department | [Shu, Jian; Hong, Ming; Zheng, Wei; Ge, Xu] Nanchang Hang Kong Univ, Internet Things Technol Inst, Nanchang, Peoples R China. [Shu, Jian; Sun, Li-Min] Nanchang Hang Kong Univ, Sch Software, Nanchang, Peoples R China. [Hong, Ming; Zheng, Wei; Ge, Xu] Nanchang Hang Kong Univ, Sch Informat Engn, Nanchang, Peoples R China. [Sun, Li-Min] Chinese Acad Sci, Inst Software, Nanchang, Peoples R China. [Sun, Li-Min] Chinese Acad Sci, Software Labs, Nanchang, Peoples R China. |
| Language | 英语 |
| WOS ID | WOS:000316000800009 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16958 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Shu, Jian,Hong, Ming,Zheng, Wei,et al. Multi-sensor Data Fusion Based on Consistency Test and Sliding Window Variance Weighted Algorithm in Sensor Networks[J]. COMPUTER SCIENCE AND INFORMATION SYSTEMS,2013,10(1):197-214. |
| APA | Shu, Jian,Hong, Ming,Zheng, Wei,Sun, Li-Min,&Ge, Xu.(2013).Multi-sensor Data Fusion Based on Consistency Test and Sliding Window Variance Weighted Algorithm in Sensor Networks.COMPUTER SCIENCE AND INFORMATION SYSTEMS,10(1),197-214. |
| MLA | Shu, Jian,et al."Multi-sensor Data Fusion Based on Consistency Test and Sliding Window Variance Weighted Algorithm in Sensor Networks".COMPUTER SCIENCE AND INFORMATION SYSTEMS 10.1(2013):197-214. |
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