数据融合技术在无线传感器网络中的应用

在大规模的无线传感器网络中,传输数据量巨大,必然存在着数据传输可靠性、拥塞以及能耗等问题,高效的数据融合技术能够有效的解决这些问题。本文结合分簇路由算法的特征,采用两层融合技术,首先簇内节点与簇首节点的融合,簇内节点根据阈值来判断是否需要发送数据,簇首节点根据接收到的数据,进行数据一致性检验,剔除异常数据,第二层采用BP神经网络算法对簇首节点与基站的融合,得到所需要的结果。实验表明,进行融合后的数据可靠性高,较大减少了数据的传输量与冗余度、降低了能量的消耗,从而提高了整个网络的性能。

Data Confusion Algorithm in Wireless Sensor Networks

In the large-scale wireless sensor networks,which need to transfer large amounts of data,there must be some problems,such as,transmission reliability,congestion and energy consumption.High-efficiency data fusion technology can effectively solve these problems.In this paper,we combined the characteristics of clustering routing algorithm,and adopted two-layer data fusion technology.The first layer is about the data fusion between cluster node and cluster-head node.Cluster node determines whether to send data according to threshold.In terms of received data,cluster-head conducts consistency check to the data,excluding abnormal data.At second layer,BP neural network algorithm is adopted to fuse head-cluster node and base station.Experiment results show that data reliability is high after fusion,which greatly decreases transmission quantity and redundancy of data,reduce energy loss,and thereby improves the performance of entire network.