Research On Feedback Trust Mechanism Data Fusion Based On Clustering In Wireless Sensor Networks

Wireless Sensor Network(Wireless Sensor Network,WSN)is a network system formed by wireless communication,multi-hop self-organization,which is composed of a large number of nodes with computing,storage and communication capabilities distributed within a specified monitoring range.Wireless sensor networks are widely used in many fields and have significant advantages.For example,in wildlife habitat environment monitoring,wireless sensor networks can provide users with real-time and accurate data information.However,due to the limitation of the sensor's own resources,the continuous energy consumption of collecting data is too large,and the node dies quickly;In addition,the nodes are usually scattered randomly in the bare environment,which is vulnerable to external to interference to affect the data transmission.Error data is easy to produce.Data fusion technology can ensure the reliability and accuracy of data,so data fusion technology is one of the key research points in wireless sensor networks.In order to improve the accuracy of data fusion results and reduce network energy consumption,this paper makes innovative research on data fusion technology and clustering technology.The details of this paper are as follows:Firstly,this paper introduces the background of the wireless sensor network,the architecture,the network characteristics,and the key technology of the wireless sensor network which is data fusion technology,and analyzes the classification,performance and limitations of the data fusion algorithm.Secondly,in view of the fact that most of the existing data fusion algorithms do not consider the data inaccuracy which is caused by data reliability,and a feedback trust model is proposed.That is the feedback trust weighted for data fusion algorithm(FTWDF).The feedback trust model includes the intra-cluster level and the inter-cluster level.In the intra-cluster level,the cluster head judges the trust state of the node by the feedback trust value,and the base station judges whether the cluster head is trusted or not by the feedback trust value in the inter-cluster level.The model ensures the reliability and the accuracy of the data and can eliminate the untrusted nodes in time.Thirdly,in view of the shortage of clustering structure of data fusion algorithm in the previous chapter,the energy consumption of a large number of nodes and the randomness of cluster head election in practical application are not considered.An energy-efficient fuzzy unequal clustering algorithm(EEFA)is proposed.The algorithm improves the cluster head election method,solves the "hot zone" problem,and saves energy.Finally,the simulation software OMNET++ is used to simulate the algorithm.Experimental results show that the proposed feedback trust model and the energy efficient fuzzy non-uniform data collection algorithm can improve data reliability,improve the accuracy of data fusion,and greatly improve the energy utilization ratio.