一种新的WSN故障数据挖掘算法

为了有效提高无线传感器网络故障数据的判别能力,在以往的研究基础上,本文结合菌群优化算法提出了一种新的挖掘方法FDMBFO(Fault Data Mining algorithm based on Bacteria Foraging Optimization).该算法首先通过小波变换和关联系数给出了故障数据分布区间的划分方法,建立了目标挖掘函数,同时利用菌群优化算法实现对目标函数的求解.最后,通过实际样本数据进行仿真实验,深入分析了影响FDMBFO算法的关键因素,并对比研究了FDMBFO算法与其它算法之间的性能状况,结果发现FDMBFO算法具有较好的适应性.

A New Fault Data Mining Algorithm of WSN

In order to effectively improve the identification ability for fault data of wireless sensor network, a novel mining algorithm FDMBFO (Fault Data Mining algorithm based on Bacteria Foraging Optimization) is proposed by bacteria foraging optimization. In this algorithm, the division method of distribution range is given with wavelet transform and correlation coefficient, and the objective mining function is built. Then, the solving of function is presented by bacteria foraging optimization. Finally, a simulation with actual sample data was conducted to study the key factors of FDMBFO. Compared to performance of other algorithm, the results show that, FDMBFO has better adaptability.