基于T分布的WSN网络数据融合算法

Data Fusion Algorithm for WSN Network Based on Distribution

由于WSN的组成是大量廉价的传感器节点,因而节点的计算、通信和感知能力均有限,所以,数据传输网络的最小化之间的提高传感器的使用寿命和整体的带宽利用率是非常重要的。在无线传感器网络之中,组合和组合传感器数据的过程就是数据融合。在文献中,该算法通常使用模糊集和主成分分析来构建相似矩阵来计算每个传感器的权重。使用统计分布,将整个收集的数据作为整体处理,并且所有测量值均由T分布加权。通过实验仿真该融合算法在某些场景优于一些常见的方法。

Since WSN is composed of a large number of inexpensive sensor nodes,the calculation,communication,and perception capabilities of the nodes are limited.Therefore,it is very important to minimize the data transmission network to improve the service life of the sensor and the overall bandwidth utilization.of.In wireless sensor networks,the process of combining and combining sensor data is data fusion.In the literature,this algorithm usually uses fuzzy sets and principal component analysis to construct a similarity matrix to calculate the weight of each sensor.Using statistical distribution,the entire collected data is treated as a whole,and all measured values are weighted by the T distribution.The fusion algorithm is better than some common methods in some scenarios through experimental simulation.