Research On Data Fusion Algorithm Of Low Energy Consumption And High Accuracy In WSN

Wireless sensor networks have a wide range of applications.Due to the limitations of hardware and software devices and their operating environments,there are always problems such as excessive node energy consumption and data accuracy degradation,which leads to a decline in the network life cycle.Therefore,the data fusion algorithm of wireless sensor networks has become a research hotspot for scholars.In the data processing process of the wireless sensor network,the data collected by the adjacent nodes uses reasonable and efficient fusion technology to eliminate redundant or erroneous information to improve the accuracy of the data;at the same time,reduce the data transmission amount and traffic.To reduce the energy consumption of nodes,thereby extending the life cycle of the network.Most of the fusion algorithms in the redundant data culling process,the accuracy of the data is always increased with the increase of node energy consumption.Aiming at the problem of equalization between node energy consumption and data accuracy,a data fusion algorithm based on cellular network structure is proposed in a large-scale environment with more than 500 nodes.In a small-scale environment with less than 500 nodes,A data fusion algorithm based on game theory is proposed.The main research contents of this paper are as follows:(1)In a data fusion algorithm based on a cellular network structure,a cellular network structure and an energy consumption model are combined.Firstly,in the clustering model based on the cellular network structure,according to the distance between the nodes in the cluster and the energy consumption of communication between the nodes,the cluster head selectes as the node with the smallest energy consumption;then the data accuracy rate reduction rate.The calculation of the energy consumption rate and the packet loss rate of the data determines the size of the fusion factor to determine the number of member nodes.Finally,the cluster head uses the Bayesian estimation formula to fuse the data collected by the member nodes in the cluster and transmit the data.Go to the Sink node.(2)In the data fusion algorithm based on game theory,it mainly selects the nodes in the cluster that participate in data fusion,the filtering of reliable data,and the final integration of data.Firstly,the cluster head node screens the member nodes of the smallest energy consumption through game theory to participate in the data fusion processing;then combines the game theory and the Bayesian estimation formula through the confidence distance,and simultaneously selects the most suitable data for fusion.As reliable data,the Sink node uses the Bayesian estimation formula to fuse the data collected by the member nodes in the cluster.(3)In the OPNET simulation environment,the two data fusion algorithms are compared with other fusion algorithms to verify that the data fusion algorithm proposed in this paper has high data accuracy while having low energy consumption.The simulation results show that the proposed algorithm has higher data accuracy and lower node energy consumption,which prolongs the life cycle of the network.