基于人工蜂群的WSN故障数据挖掘算法
The fault data mining algorithm for WSN based on artificial bee colony

为了有效提高无线传感器网络中故障数据的判别能力, 本文结合人工蜂群算法提出了一种新的挖掘算法FDMA(Fault Data Mining Algorithm). 该算法首先利用小波变换降低故障数据的突发性, 以达到对故障数据的标准化处理. 其次, 基于关联系数来划分故障数据分布区间, 并建立了数据挖掘的目标函数, 同时利用人工蜂群算法对目标函数进行优化. 最后, 通过实际传感器样本数据进行仿真实验, 对比研究了FDMA算法与其它算法之间的性能状况(包括吞吐量、延迟时间、丢包率和能耗), 结果发现FDMA算法具有较好的适应性.
In order to effectively improve the identification ability for fault data of wireless sensor network, a new mining algorithm FDMA (Fault Data Mining Algorithm) is proposed by artificial bee colony. In this algorithm, the burst of fault data is reduced to be standardization with wavelet transform, and the distribution range is divided by correlation coefficient. Then, the objective function is built to mining fault data, and it is optimized with artificial bee colony. Finally, a simulation with actual sensors sample data was conducted to study the performance between FDMA and other algorithm, such as throughput, time delay, packet dropping rate and energy consumption. The results show that, FDMA has better adaptability