一种改进的BP神经网络WSN数据融合方案

摘要

In order to reduce the number of invalid and redundant data in wireless sensor network,improve the convergence speed of wireless sensor network,prolong the life cycle and improve the accuracy of fire report,a new data fusion method based on neural network is proposed.This method can reduce the data from multiple sensors,and the real-time processing capability of the reference nodes can improve the convergence rate of the neural network,and reduce the energy consumption.The experimental results show that this method can be applied to fire monitoring sensor network,improve the monitoring accuracy,reduce the energy consumption of the nodes,so that the capacity of wireless sensor network for forest fire monitoring is greatly improved.%在监测森林火灾时,为了达到减少无线传感网里大量无效和冗余的数据、提高无线传感网络的收敛速度、延长节点的生命周期、改善火灾报告准确度的目标,提出了一种基于BP神经网络的改进型数据融合方法.该方法在节点上可以对多种传感器产生的数据进行融合,参考节点的实时处理能力来改善BP神经网络的收敛速度,在很大程度上降低能耗.实验结果表明,该方法能够较好地应用于火灾监测传感网,改善了监测精度,减少了节点能耗,使得无线传感网对森林火灾监测的能力大幅度提高.