基于异常数据预处理和自适应估计的WSN数据融合算法

针对无线传感器网络（wireless sensor network，WSN）存在节点能量受限、测量精度低、生存期短等问题，提出一种基于异常数据预处理和自适应估计加权融合算法（abnormal data-preprocessing adaptive estimation weighting fusion，ADAEWF）。为了提高算法可靠性，提出了基于异常数据检测、简单多数原则和节点综合支持度函数的数据预处理机制；为了减小测量误差对融合精度的影响，基于分批估计和自适应理论对节点测量值进行自适应估计加权数据融合；然后，建立了WSN仿真模型，并分别获得了ADAEWF、自适应预测加权数据融合算法（adaptive forecast weighting data fusion，AFWDF）和算术平均值法下融合结果的均方误差和网络有效生存期。仿真结果显示：ADAEWF算法融合精度和网络有效生存期均优于AFWDF和算术平均值法，表明ADAEWF算法在提高融合数据有效性、网络有效生存期和融合精度方面具有优越性。

Data fusion algorithm based on abnormal data-preprocessing and adaptive estimation in WSN

This paper aimed at the WSN''s problems, such as limited node''s energy, low precision of measurement and short effective lifetime of network, etc, 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.