Data fusion algorithms for wireless sensor networks based on deep learning model

In order to reduce the energy consumption and prolong the lifetime of wireless sensor networks (WSN), a data fusion algorithm based on deep learning model is proposed. Firstly, the algorithm completes training and clustering at the sink node, transfers the trained parameters to each cluster node, and then transfers the collected data to the sink node after feature classification, extraction and fusion. In order to make the distribution of cluster heads more uniform, the clustering method is improved on the basis of estimating the optimal number of cluster heads, which reduces the number of clusters and saves the energy consumption of the network. The simulation results show that the WSN data fusion algorithm based on deep learning model reduces the network energy consumption, prolongs the network lifetime, and is more suitable for large-scale telecommunication.