基于AFSA-LSSVM的三维传感器节点定位

摘要

为了提高三维无线传感器的定位精度，针对最小二乘支持向量机(LSSVM)参数优化问题，提出了一种人工鱼群算法(AFSA)优化LSSVM的传感器点定位方法(AFSA-LSSVM)。首先构建三维无线传感器定位模型的学习样本，然后采用 LSSVM 构建三维节点定位模型，并采用 AFSA 模拟鱼群的觅食、聚群及追尾行为找到最优LSSVM参数，最后采用仿真实验测试节点的定位性能。结果表明，相对于其它定位方法， AFSA-LSSVM提高了传感器节点的定位精度，具有一定的实际应用价值。%In order to improve location precision of three-dimensional wireless sensor nodes, a novel three dimensional node location method of wireless sensor network is proposed in this paper based on least squares support vector machine (LSSVM) which parameters are optimized by artificial fish algorithm (AFSA). Firstly, the study samples are constructed for three-dimensional nodes localization model, and then LSSVM is used to build three-dimensional node localization model in which fish feeding behavior, cluster and rear end behavior are simulated to find the optimal parameters of LSSVM, and finally the performance is tested by simulation experiment. The results show that, compared with other localization methods, the proposed method can improve the precision of the sensor nodes and it has some practical application values.