摘要
为了改善传感器节点定位性能,提出了人工蜂群优化神经网络的无线传感器节点定位算法。首先测量3个锚节点与定位传感器节点之间的参数,然后采用人工蜂群优化神经网络对测距误差进行建模与预测,并根据检测结果确定权重,最后根据三边定位算法进一步提高定位精度,并采用仿真实验测试其有效性。结果表明,该文算法提高了定位的精度,加快了定位的速度,定位实时性优异。
In order to obtain the ideal results of node localization for wireless sensor network, a novel node localization of wireless sensor network based on an artificial bee colony algorithm optimizing neural network is proposed. Firstly, three anchor nodes are selected and parameters between localization sensor nodes are obtained according to the measurement model; secondly, an artificial bee colony algorithm optimizing neural network is used to predict ranging errors, and location errors are corrected to determine the weight; finally, node localization results are obtained according to the three-edge location algorithm. The performance is analyzed through the specific test experiment. The results show that the proposed model improves the node localization accuracy and has better node lo-calization and real-time performance.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2017年第4期466-471,共6页
Journal of Nanjing University of Science and Technology
基金
国家自然科学基金(61401526)
河南省教育技术装备与实践教育研究项目(GZS309)
关键词
室内定位
无线传感器网络
人工蜂群优化算法
神经网络
锚节点
仿真实验
indoor localization
wireless sensor network
artificial bee colony optimization algorithm
neural network
anchor nodes
simulation experiment
