无线传感器网络中数据密度相关度融合算法

Data density correlation degree based aggregation algorithm in wireless sensor network

中文关键词  数据融合  无线传感器网络  相关性  数据密度

英文关键词  data aggregation  wireless sensor networks  correlation degree  data density

中文摘要

      无线传感器网络节点部署在复杂环境时, 节点间相关性无法通过节点间距离来准确描述. 为了克服该缺陷, 本文提出了数据密度相关度公式. 该公式反映了节点数据的\varepsilon邻域内数据的聚集程度, 也反映了该节点数据相对其\varepsilon邻域内数据的相对位置. 同时, 将数据密度相关度公式应用到代表式数据融合算法中, 提出了数据密度相关度融合算法. 该融合算法得到的相关区域具有相关区域内节点数据相关度大, 相关区域间节点数据相关度小的优点. 仿真实验结果表明了该融合算法在数据准确性和能耗方面较基于\alpha--局部空间数据融合算法和基于皮尔森相关系数的数据融合算法优越.

英文摘要

      Distance between sensor nodes can’t reflect their correlation degree in wireless sensor network (WSN) as the sensor nodes are deployed in complex environment. In order to resolve this drawback, data density correlation degree (DDCD) is proposed in this paper. The DDCD is a spatial correlation measurement of a sensor node’s data to its neighbor nodes’ data. It could reflect the concentration degree of neighbor nodes’ data. As well, it could describe relative position of a sensor node’s data to its \varepsilon-neighborhood data. Based on this correlation degree, DDCD aggregation algorithm is presented to highlight that the representative data has a low distortion on the represented data in WSN. Additionally, simulation experiments with a real dataset are presented to evaluate the performance of the DDCD aggregation algorithm. The experimental results show that the resulting representative data achieved by DDCD aggregation algorithm have a lower data distortion than those achieved by the Pearson correlation coefficient based data aggregation algorithm or \alpha–local spatial data aggregation algorithm. Moreover, the energy consuming of DDCD aggregation algorithm is less than those of the other two data aggregation algorithms.