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Received:January 23, 2015 Revised:January 25, 2016
Received:January 23, 2015 Revised:January 25, 2016
中文摘要: 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.
Abstract: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.
keywords: wireless sensor network three-dimensional node localization LSSVM artificial fish swarm algorithm
文章编号: 中图分类号: 文献标志码:
基金项目:浙江省教育厅科研项目(Y201431515)
Author Name | Affiliation |
FU Bin | Shaoxing Vocational & Technical College, Shaoxing 312000, China |
Author Name | Affiliation |
FU Bin | Shaoxing Vocational & Technical College, Shaoxing 312000, China |
引用文本:
傅彬.基于AFSA-LSSVM的三维传感器节点定位.计算机系统应用,2016,25(7):137-141
FU Bin.Three-Dimensional Sensor Node Localization Based on AFSA-LSSVM.COMPUTER SYSTEMS APPLICATIONS,2016,25(7):137-141
傅彬.基于AFSA-LSSVM的三维传感器节点定位.计算机系统应用,2016,25(7):137-141
FU Bin.Three-Dimensional Sensor Node Localization Based on AFSA-LSSVM.COMPUTER SYSTEMS APPLICATIONS,2016,25(7):137-141