基于深度学习的无线传感器网络数据融合

在无线传感器网络数据融合算法中，BP神经网络被广泛用于节点数据的特征提取和分类。为了解决BP神经网络收敛慢，易陷入局部最优值且泛化能力差从而影响数据融合效果的问题,提出一种将深度学习技术和分簇协议相结合的数据融合算法SAESMDA。SAESMDA用基于层叠自动编码器(SAE)的深度学习模型SAESM取代BP神经网络,算法首先在汇聚节点训练SAESM并对网络分簇，接着各簇节点通过SAESM对采集数据进行特征提取，之后由簇首将分类融合后的特征发送至汇聚节点。仿真实验表明,和采用BP神经网络的BPNDA算法相比，SAESMDA在网络能耗大致相同的情况下具有更高的特征提取分类正确率。

Data aggregation in wireless sensor networks based on deep learning

Data fusion algorithms widely use BP neural network to extract and classify the node data features in wireless sensor networks. In order to overcome the shortcomings of BP neural network leading to poor performance for data fusion, such as low convergence speed, local optimal and bad generalization ability, this paper proposed a data fusion algorithm SAESMDA combined with deep learning technology and wireless sensor network clustering routing protocol. SAESMDA used deep learning model SAESM based on stacked autoencoder(SAE) instead of the BP neural network, algorithm firstly trained SAESM in sink node and generated clusters for network, then used SAESM to exacted node data features in cluster nodes, finally the data features in the same class would be fused and sent to sink node by cluster heads. Simulation experiments show that compared with BPNDA based on the BP neural network , SAESMDA has a higher feature extraction and classification accuracy with the similar network energy consumption.