WSN中基于分簇的模糊加权数据融合算法

摘要

为在数据收集和传输中保证数据的准确性和实时性,提出一种基于分簇的模糊加权数据融合算法(FWADF).在簇内利用模糊逻辑控制器分析节点数据的可信度,确保数据的可信性,同时加入对数据优先级的考虑,减少网络时延.在簇间采用模糊加权矩阵方法提高数据的准确性.在NS-2仿真工具上的实验结果表明,在同等数据流量的前提下,采用FWADF算法时数据到达基站的时间延迟最短,在节点收集相同数据量的情况下,与Proposed DF、VWFFA、FIM等算法相比,基站获得数据的平均准确率分别提高5.0％、16.1％、9.5％.%In order to ensure the accuracy and real-time of data in data collection and transmission,a Fuzzy-Weighted Algorithm for Data Fusion (FWADF) based on clustering is proposed.Fuzzy logic controller is used to analyze the reliability of node data in the cluster to ensure the credibility of the data.At the same time,the priority of data is added to reduce the network delay.In the cluster,fuzzy-weighted matrix method is used to improve the accuracy of the data.Experimental result with NS-2 simulation tool shows that,the time delay of arriving at the base station is the shortest under the same data traffic,when the nodes collect the same amount of data,compared with the algorithms such as Proposed DF and VWFFA,FIM,the average accuracy rate of the data obtained by base stations is increased by 5.0％,16.1％ and 9.5％ respectively.