基于异常数据驱动的WSN簇内数据融合方法

引入数据驱动的思想,提出了一种基于异常数据驱动的簇内数据融合方法.在节点数据采集过程中,仅当异常数据发生时才发送给簇头,减少了监测网络的数据传输量.在簇头数据融合过程中,建立了各传感器之间的相互支持度矩阵,支持度值较低的监测数据将被剔除,支持度值较高的监测数据进行最优加权融合,从而保证了融合结果的准确性和有效性.仿真实验结果表明,与算术平均值法及自适应加权融合法相比,本文方法能有效去除冗余信息,在融合精度、能量消耗方面具有明显的优势.

A Data Aggregation Method Based on Abnormal Data-driven in Clusters of Wireless Sensor Networks

Aiming at the deficiency of traditional data aggregation methods,a new aggregation method based on abnormal data-driven is proposed by introducing the mechanism of data-driven.In the phase of data acquisition,the sensor nodes only send the abnormal data to cluster head when exceptional event occurs randomly,this can effectively reduce the network traffic.In the phase of data aggregation for cluster head,the support matrix is constructed between sensors,those monitoring data which has lower support values will be eliminated,only the higher support value data is aggregated by cluster head with the method of optimal weight,thus ensuring the accuracy and validity of aggregation results.The simulation experiments show that,compared with the mean value method and the self-adaptive weighted aggregation method,the proposed method can effectively remove redundant information in the period of data transmission,which has obvious advantages in aggregation precision and energy consumption.