Aimed at the WSN's problems, such as limited node's energy, low precision of measurement and short effective lifetime of network, etc., this paper proposed a data fusion algorithm based on abnormal data preprocessing and adaptive estimation, ADAEWF. In order to improve the algorithm's reliability, this paper firstly proposed the data preprocessing mechanism based on abnormal data detection, simple majority principle and node's comprehensive support function. To reduce the measurement error's effect on the fusion precision, this paper proposed an adaptive estimation weighted data fusion algorithm. Then, this paper established the WSN's simulation model and obtained the mean square error and network's effective lifetime under ADAEWF, AFWDF and arithmetic mean value algorithm, respectively. Simulation shows that, in terms of both fusion accuracy and network's effective lifetime, ADAEWF outperforms AFWDF and arithmetic mean value algorithm, and ADAEWF algorithm has superiority in improving the effectiveness of data fusion, network lifetime and fusion accuracy.

[2019-09-05] Printed Article /doc/2018.07.0345/v2