Multi-sensor Data Fusion Based on Consistency Test and Sliding Window Variance Weighted Algorithm in Sensor Networks

ISCAS OpenIR

	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
发表期刊	COMPUTER SCIENCE AND INFORMATION SYSTEMS
ISSN	1820-0214
卷号	10 期号:1 页码:197-214
摘要	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.
收录类别	SCI
关键词	Wireless Sensor Networks Data Fusion Consistency Test Sliding Window Variance Weighted
部门归属	[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.
语种	英语
WOS记录号	WOS:000316000800009
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16958
专题	中国科学院软件研究所
推荐引用方式 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.