面向无线传感器网络的自适应数据清洗方法

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%。

Adaptive approach for data cleansing in wireless sensor networks

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%.