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%.
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