Wireless sensor network (WSN) is usually organized in a particular area for achieving some specific tasks and functions. It has broad application prospects in military, environmental monitoring, disaster relief, and other many commercial areas. Measuring the significance of distributed nodes is the foundation of many applications of WSN. Therefore, this paper studies the distributed node measurement methods and approaches based on multiple attributes in wireless sensor networks to improve the accuracy of distributed node importance measurement. The entropy weight TOPSIS method is used to calculate the importance index of distributed nodes of degree centrality, eigenvector centrality, compactness centrality, betweenness centrality, K-kernel decomposition centrality, and aggregation coefficient; the PR value of each node is given by combining the PageRank algorithm; and then, the parameters α and β are introduced to calculate the comprehensive importance measurement results of each node, to finally obtain the importance measurement results of each node dynamically through the improved node deletion method. The experimental results of the research show that the proposed method can accurately measure the importance of distributed nodes in wireless sensor networks, and the accuracy is as high as 98%.

中文翻译：

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基于多属性的无线传感器网络中分布式节点的测量方法

无线传感器网络（WSN）通常组织在特定区域中，以实现某些特定任务和功能。它在军事，环境监测，救灾和其他许多商业领域具有广阔的应用前景。测量分布式节点的重要性是WSN许多应用程序的基础。因此，本文研究了基于无线传感器网络中多个属性的分布式节点度量方法和方法，以提高分布式节点重要性度量的准确性。采用熵权TOPSIS法计算度中心度，特征向量中心度，紧度中心度，中间度中心度，K核分解中心度和聚集系数的分布节点的重要性指数。通过结合PageRank算法给出每个节点的PR值；然后，参数引入α和β来计算每个节点的综合重要性测量结果，最终通过改进的节点删除方法动态地获得每个节点的重要性测量结果。实验结果表明，该方法能够准确地测量无线传感器网络中分布式节点的重要性，准确率高达98％。