无线传感器网络节点三维定位技术

3D Positioning Technology of Wireless Sensor Network Nodes

针对无线传感网络中的节点定位精度低的问题,提出了改进蝗虫算法在三维空间节点定位技术。首先,采用了极大似然估计法构建节点定位模型。其次,对蝗虫算法从以下3个方面改进:(1)在种群中采用伪反向学习初始化,增加了种群多样性;(2)在递减系数中使用自适应因子避免算法陷入局部最优;(3)在迭代中使用正交交叉算子进行个体筛选。最后,将改进后的蝗虫算法用于节点定位。在仿真实验中,将本文算法与蚁群算法,粒子群算法和蝗虫算法进行对比,结果说明本文算法在噪声、锚节点密、未知节点数量和能量消耗具有较好的定位效果。

Aiming at the problem of low node positioning accuracy in wireless sensor networks, an Improved Grasshopper optimization algorithm is proposed to locate nodes in three-dimensional space. Firstly, the maximum likelihood estimation method is used to construct the node location model. Secondly, the Grasshopper optimization algorithm is improved from the following three aspects:(1)The use of pseudo-reverse learning initialization in the population increases the diversity of the population.(3)The use of adaptive in the decreasing coefficient the factor avoids the algorithm from falling into the local optimum.(3)In the iteration, the orthogonal crossover operator is used for individual screening. Finally, the improved locust algorithm is used for node location. In the simulation experiment, the algorithm of this paper is compared with ant colony algorithm, particle swarm algorithm and grasshopper optimization algorithm. The results show that the algorithm of this paper has a better positioning effect in terms of noise, anchor node density, number of unknown nodes and energy consumption.