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An event-driven high-precision data fusion algorithm based on double-layer clustering

In order to ensure the high accuracy of data fusion in the real-time online monitoring of environmental parameters, an event-driven, high-precision data fusion algorithm based on double-layer clustering is proposed in wireless sensor network (EDDCHA). EDDCHA algorithm based on feature extraction in depth learning model and traditional structure clustering routing, established the general event and emergency event double-layer cluster routing model, distinguished the cluster priority order, set the emergency threshold, and realized double-layer cluster Head synchronization data fusion. The simulation results show that EDDHA algorithm can obtain more accurate fusion data in real time under the condition of similar energy consumption and SAEMDA algorithm.