基于最短路径修正的多维定标定位算法

Improved multi-dimensional calibration algorithm based on shortest path correction

当无线传感器网络节点密度较低或节点分布不均匀时,利用多维定标定位算法求出的最短路径与节点实际距离有一定误差。针对这个问题提出了一种基于最短路径修正的改进算法。根据节点的局部密度对无线传感器网络中节点间的边进行重新赋值,计算节点间的距离。结合人工蜂群智能算法选出节点间的最优最短路径,计算出节点间距离矩阵。实验仿真结果表明,该改进算法的定位精度相对于经典集中式多维定标算法提高了11%左右。

The certain error between the shortest path distance of nodes and the actual Euclidean distance of nodes exists when the wireless sensor network node is non-uniform or the node density is low. Aimed at this problem, an improved algorithm based on the shortest path is proposed. The distance between the nodes is calculated by reassigning the edges in the sensor network connection diagram according to the lo- cal density of the nodes. Combined with the intelligent artificial bee colony algorithm to select the optimal shortest path between nodes, the distance matrix between nodes is calculated. Simulation results show that the accuracy of the improved algorithm is about 11% higher than that of MDS_MAP algorithm.