Research Of Data Fusion In Wireless Sensor Network Based On Prediction

The continuous improvement of modern information technology has promoted the rapid development of Wireless Sensor Networks and has played an important role in a wide range of fields.Wireless Sensor Networks continuously acquires collected data through micro sensors randomly deployed in the monitoring area,processes it and transmits it to the user-end device,and completes the task.Therefore,the sensor node needs to consume a large amount of energy.The sensor nodes carry limited energy and are not easily replaced.In order to reduce the energy consumption of the nodes,researchers have applied many techniques.Data Fusion technology based on prediction is one of the important technologies.By sending the predicted data that meets the requirements instead of the collected data,the Data Fusion technology based on prediction reduces the number of data transmissions between nodes and plays an important role in extending the network operating time.This paper proposes a Data Fusion algorithm based on grey prediction in Wireless Sensor Networks.The algorithm deeply studies the improvement of topology-based routing based on prediction-based Data Fusion technology.It proposes an improved cluster-based routing protocol,optimizes cluster head election and clustering,and uses double cluster heads.The model reduces the energy consumption of cluster heads.At the same time,an improved scheme for the Grey Prediction Model is proposed.The current prediction data is added to the original data sequence of the next prediction period,so that the Gray Prediction Model can continuously predict data in Wireless Sensor Networks.Based on the above two improvements,a Wireless Sensor Networks Data Fusion algorithm based on grey prediction is proposed.Simulation experiments show that the algorithm effectively reduces the amount of data transmission in the network and improves the network runtime.