王海军, 史毓达, 程宏斌. 一种基于最大-最小贴近度的簇内数据融合机制[J]. 信阳师范学院学报(自然科学版), 2013, 26(3): 453-456. DOI: 10.3969/j.issn.1003-0972.2013.03.037
引用本文: 王海军, 史毓达, 程宏斌. 一种基于最大-最小贴近度的簇内数据融合机制[J]. 信阳师范学院学报(自然科学版), 2013, 26(3): 453-456. DOI: 10.3969/j.issn.1003-0972.2013.03.037
Wang Haijun , Shi Yuda , Cheng Hongbin .

A Data Aggregation Mechanism in Clusters using Max-min Approach Degree

[J]. Journal of Xinyang Normal University (Natural Science Edition), 2013, 26(3): 453-456. DOI: 10.3969/j.issn.1003-0972.2013.03.037
Citation: Wang Haijun , Shi Yuda , Cheng Hongbin .

A Data Aggregation Mechanism in Clusters using Max-min Approach Degree

[J]. Journal of Xinyang Normal University (Natural Science Edition), 2013, 26(3): 453-456. DOI: 10.3969/j.issn.1003-0972.2013.03.037

一种基于最大-最小贴近度的簇内数据融合机制

A Data Aggregation Mechanism in Clusters using Max-min Approach Degree

  • 摘要: 考虑到邻近节点对环境监测的数据往往存在一定的空间相关性,提出一种基于最大-最小贴近度的簇内数据融合机制.簇头节点首先对簇内成员节点的融合数据进行校验,根据置信空间判断数据是否采用,然后再对数据进行融合.仿真实验结果表明,该方法能有效解决由于节点采集的数据的可信程度不一致问题,并能显著延长网络生命周期

     

    Abstract: Taking into account the data correlation between the adjacent nodes of the same region, an intra-cluster data aggregation mechanism based on the max-min approach degree principle was presented. The data from the non-cluster head nodes should be firstly checked in the cluster head to judge whether the data can be adopted according to the confidence interval and then will be aggregated in the next process. The experimental results showed that this method can effectively solve the problem that the collected data are inconsistent in the credibility degree, and can extend the network life

     

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