基于限定域-蜂群的光纤物联网节点定位算法研究

Research on Node Positioning Algorithms for Fiber Internet of Things Based on LD-BC Algorithm

为了实现物联网光纤感知层中待测节点的快速精确定位,提出了基于限定域-蜂群的节点定位算法,建立了针对光纤传感网络数据特点的节点定位模型,采用矩形限定域简化约束条件,从而提高定位精度。实验结果显示,当参考节点数大于3时,对定位平均误差的影响基本不变;当种群数取18时,定位平均误差趋于稳定,3种算法的平均定位精度分别是2.3 m、3.1 m和3.4 m;而达到定位精度需要的迭代次数分别是12次、15次和41次。由此可见,本算法在稳定性、定位精度及收敛速度方面均具有更好的定位性能,其在大范围物联网光纤感知层节点定位领域具有一定的实际应用价值。

In order to realize the fast and accurate location of the nodes to be tested in the fiber-optic sensing layer of the Internet of Things,a node localization algorithm based on LD-BC algorithm is proposed. A node localization model based on the characteristics of the fiber optic sensor network data was established,and the rectangular constrained domain was used to simplify the constraint conditions,thereby improving the localization accuracy. Experimental results show that when the number of reference nodes is greater than 3,the effect on the average positioning error is basically unchanged. When the number of population is 18,the average positioning error tends to be stable,and the average positioning accuracy of the three algorithms is 2.3 m,3.1 m,and 3.4 m,respectively. The three algorithms require 12,15 and 41 iterations to achieve the required positioning accuracy. It can be seen that the algorithm has better positioning performance in terms of stability,positioning accuracy and convergence speed. It has a certain practical application value in the field of large-scale IoT fiber sensing layer node positioning.