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面向无线传感器网络的自适应数据清洗方法 被引量：1

Adaptive approach for data cleansing in wireless sensor networks
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摘要 针对无线传感器网络(WSN)数据不精确和不可靠的问题,根据感知数据的空间相关性定义了弹性空间模型,并在此基础上提出一种自适应近邻空间清洗方法(ANSA)。该方法根据感知数据波动动态调整近邻空间大小,并通过计算近邻节点测量数据的加权平均对本地数据清洗。实验结果表明,感知数据清洗后误差控制在0.5以内,与经典的加权移动平均(WMA)方法相比,所提方法的精确度更高,同时能量损耗减少约36%。 Since the data gathered in Wireless Sensor Network （WSN） are inaccurate and unreliable, a flexible space model based on the spatial correlation of sensor data was defined, and an adaptive neighbor-space approach for data cleansing （ANSA） was proposed. The approach adjusted neighbor-space dynamically according to sensor data fluctuation and calculated the weighted average of neighbors＇ measurements to clean local raw data. The experimental results show that, the sensor data error after cleansing by the proposed approach is less than 0.5, and compared to the classic Weighted Moving Average （WMA）, it is more accurate and the energy consumption is reduced by about 36%.
作者 夏英 毕海洋 雷建军 裵海英
机构地区 重庆邮电大学空间信息系统研究中心
出处 《计算机应用》 CSCD 北大核心 2014年第8期2145-2147,2154,共4页 journal of Computer Applications
基金 重庆市自然科学基金资助项目(cstc2012jjA4014) 重庆市基础与前沿研究项目(cstc2013jcyjA40023) 重庆邮电大学青年科学研究项目(A2012-90)
关键词 无线传感器网络 空间相关性 数据清洗 孤立点检测 数据可靠性 Wireless Sensor Network （WSN） spatial correlation data cleansing outlier detection data reliability
分类号 TP311 [自动化与计算机技术—计算机软件与理论] TP393.032 [自动化与计算机技术—计算机应用技术]
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参考文献12

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二级参考文献13

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计算机应用
2014年 第8期

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