基于聚集共线度和节点连通度的无线传感器网络定位算法

发布时间：2019-05-19 22:41

【摘要】：为进一步提高无线传感器网络(WSN)的定位精度,对锚节点分布与网络定位精度之间的关系进行研究,提出一种新的基于"聚集-共线度"(DAC)和"节点度"(ND)的锚节点选择算法——DAC-ND。首先,通过实验分析得出锚节点在共线分布和集中分布时对定位精度影响较大;然后,经过对基于共线度的锚节点选择算法进行分析和比较,发现现有的基于最小角和最小高的两类锚节点共线度算法(DC-A和DC-H)均存在不足;最后,综合这两类算法的优势提出一种新的基于"聚集-共线度"的概念,并结合"节点度"提出DAC-ND锚节点选择算法。通过Matlab仿真实验得出,与锚节点随机选择算法相比,DAC-ND算法可大幅降低平均定位误差(54%~73%);与基于最小角和最小高的共线度选择算法等相比,采用DAC-ND算法平均定位误差可分别降低15%~23%和12%~23%。实验结果表明,DAC-ND算法相比DC-A和DC-H能够获得更高的定位精度,从而验证了DAC-ND算法的有效性。
[Abstract]:In order to further improve the positioning accuracy of wireless sensor network (WSN), the relationship between anchor node distribution and network positioning accuracy is studied. A new anchor node selection algorithm based on "aggregation collinearity" (DAC) and "node degree" (ND) is proposed. DAC-ND. First of all, through the experimental analysis, it is concluded that the anchor node has a great influence on the positioning accuracy when the collinear distribution and centralized distribution are used. Then, through the analysis and comparison of the anchor node selection algorithms based on collinearity, it is found that the existing two kinds of anchor node collinearity algorithms (DC-A and DC-H) based on minimum angle and minimum height have shortcomings. Finally, based on the advantages of these two algorithms, a new concept based on "aggregation-collinearity" is proposed, and a DAC-ND anchor node selection algorithm is proposed combined with "node degree". The Matlab simulation results show that compared with the random anchor node selection algorithm, the DAC-ND algorithm can greatly reduce the average positioning error (54% 鈮，

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