Research On Energy Efficient Clustering Routing And Data Fusion Of Wireless Sensor Networks In Agriculture

Wireless sensor network has the advantages of small size,low cost,easy deployment and so on.It has important applications in intelligent agricultural environmental monitoring.However,the energy carried by sensor nodes is limited,and farmland monitoring has the characteristics of long operation cycle and complex and changeable environment.These factors make it difficult to ensure continuous and efficient data acquisition and transmission.Clustered routing technology can reasonably divide network nodes into clusters and scientifically plan multi hop transmission paths to prolong the network life cycle.Clustering based data fusion technology can reduce network communication consumption by reducing redundant data transmission of nodes.Therefore,this paper focuses on the goal of improving network energy efficiency,and conducts in-depth research on these two key technologies.The specific contents are as follows:(1)Aiming at the shortcomings of existing clustering routing protocols,such as unreasonable cluster head election and easy to cause "hot spots" in the network,this paper proposes an energy balanced and effective clustering routing protocol EBUCFL based on fuzzy logic theory.The fuzzy inference system is designed to calculate the cluster head election probability by comprehensively considering many factors such as distance,residual energy,node location,node density,communication overhead,etc.The cluster head node is generated by optimizing the competition radius for cluster head competition,then the common node adds the corresponding cluster head to form a cluster according to the cost function,and finally uses the method of range circle to plan the routing mode between clusters under the adjustment of distance,residual energy and other factors,Complete the whole clustering routing process.The simulation results show that the clustering routing method proposed in this paper can scientifically and reasonably select cluster heads and plan data transmission paths between clusters,so as to achieve the goal of balancing network energy consumption and reducing energy consumption.(2)Aiming at the problem that a large number of redundant data are sent and the network bandwidth is occupied due to the integrated development of sensor nodes to monitor various types of environmental parameters,a clustering based GP-CA data fusion algorithm is proposed based on gray prediction and correlation analysis.Firstly,the traditional GM(1,1)model is optimized by sliding unbiased improvement,background value optimization and Markov error correction to ensure the accuracy of prediction and reduce the amount of data sent by nodes in the cluster by data prediction.Then,Pearson correlation coefficient is used to analyze the correlation between the same type parameters and different types of parameters collected by two neighbor nodes in the cluster,and the estimated value of highly correlated data is used to replace the actual value to further reduce the amount of data sent by nodes;Finally,a multiple data fusion model of GP-CA algorithm is proposed.The simulation results show that the proposed data fusion method based on clustering can effectively reduce redundant data,reduce network bandwidth,and significantly extend the network life cycle.