基于入侵杂草优化算法的无线传感网节点定位

在基于测距的无线传感器网络节点定位中,最小二乘法由于定位误差的累积,定位精度不高。针对该问题,提出了一种基于入侵杂草优化算法的定位方法。该算法以定位误差为适应度函数,将定位问题转换为求解非线性方程组最优化问题。在求解的过程中,利用未知节点到锚节点的距离和锚节点可信度对适应度函数进行修正,以实现更高精度的定位。仿真实验表明:改进的定位算法,在不同测距误差、不同通信半径、不同锚节点数和不同节点数下,都能得到更高的定位精度。

Invasive weed optimization based localization algorithm for Wireless Sensor Network

Node localization by least squares method cannot achieve high accuracy in the range-based Wireless Sensor Network because of location error accumulations. This paper proposes an invasive weed optimization based localization algorithm for Wireless Sensor Network, which takes location errors as the fitness function, transforming the node localization problem into a nonlinear equations optimization problem. To increase the node localization accuracy, the presented algorithm employs the distance from the unknown node to the anchor node and the anchor node credibility to revise the fitness function. Simulation results show that the work can achieve higher accuracy under the circumstances of different ranging errors, dif-ferent communication radii, different numbers of anchors, and different number of nodes.