吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

面向无线传感器网络的自适应模糊神经拓扑控制算法

胡黄水1, 沈玮娜1, 王宏志1, 张邦成2   

  1. 1. 长春工业大学 计算机科学与工程学院, 长春 130012; 2. 长春工业大学 机电工程学院, 长春 130012
  • 收稿日期:2016-12-23 出版日期:2018-03-26 发布日期:2018-03-27
  • 通讯作者: 王宏志 E-mail:wanghongzhi@mail.ccut.edu.cn

Adaptive Fuzzy Neural Topology Control Algorithm for Wireless Sensor Networks

HU Huangshui1, SHEN Weina1, WANG Hongzhi1, ZHANG Bangcheng2   

  1. 1. School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China;2. School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China
  • Received:2016-12-23 Online:2018-03-26 Published:2018-03-27
  • Contact: WANG Hongzhi E-mail:wanghongzhi@mail.ccut.edu.cn

摘要: 为有效控制无线传感器网络节点能耗, 提出一种自适应模糊神经控制系统, 并设计基于自适应模糊神经拓扑控制算法——AFNTC. 该算法中模糊控制器参数由人工神经网络训练后获得, 且通过反馈、 循环的方式, 不断调整节点的通信范围控制节点传输功率, 从而使节点实际能耗接近预设的期望值, 延长网络生命周期. 实验结果表明, AFNTC算法能达到节点能耗可控的目的, 相比模糊控制的拓扑控制(FCTP)算法和局部平均(LMA)算法, 具有更低、 更稳定的节点平均耗能.

关键词: 能耗可控, 人工神经网络, 模糊控制, 拓扑控制

Abstract: In order to effectively control the energy consumption of wireless sensor network nodes, we proposed an adaptive fuzzy neural control system, and designed an adaptive fuzzy neural topology control algorithm[CD2]AFNTC. In this algorithm, the parameters of fuzzy controller were obtained after training by artificial neural network, and the communication range of the nodes was constantly adjusted through the feedback and circulation to control the transmission power of the node, so that the actual energy consumption of the node was close to the preset expected value, and the network life cycle was prolonged. Experimental results show that AFNTC algorithm can achieve the purpose of controlling energy consumption of nodes. Compared with FCTP and LMA algorithms, the nodes have lower and more stable average energy consumption in AFNTC algorithm.

Key words: artificial neural network, topology control, energy consumption control, fuzzy control

中图分类号: 

  • TP393.11