| With its advantages of low power consumption,low cost and miniaturization,wireless sensor networks are widely used in forestry fields such as environmental ecological monitoring and forest fire warning.The wireless sensor network is composed of a large number of sensor nodes,and the precise location information of the nodes is very important in network deployment and many application scenarios.The current research on sensor node location technology is mostly based on indoor scenes,and there is a lack of research on node location outdoors in complex environments such as forest areas.In response to this problem,based on forestry wireless sensor networks,this paper proposes a node positioning method based on Received Signal Strength Indication(RSSI),studies and analyzes the impact of complex environmental factors on positioning accuracy,and proposes corresponding optimization methods to improve positioning accuracy.In this paper,the principles of node positioning in wireless sensor networks are first explained,and the advantages and disadvantages of common node distance measurement methods are compared and analyzed.Combined with the forestry application background,the positioning method based on RSSI ranging is finally selected.In order to explore the mapping relationship between RSSI value and distance,the GreenLab wireless sensor network node integrated with a dual-band transceiver module of 2.4GHz and 433MHz designed by ourselves is passed through dense forests,woods,forest paths and grasslands in the experimental forest farm of Northeast Forestry University.The measured RSSI values of the dual bands in four environments,and using Origin software to fit and analyze the data,the results show that the goodness of fit between the dual band RSSI data and the logarithmic distance loss model is above 0.9,indicating that the loss model and the measured data The fitting degree is suitable for the prediction of signal loss in the forest area.Then based on the experimental data,an environmental parameter prediction model is established,which can predict the environmental parameter value of the logarithmic distance loss model according to the environmental complexity of the area to be dropped.The two scene experiments verify that the goodness of fit between the data of the prediction model and the measured data is above 0.9.This shows that the prediction model can be applied to actual sensor network deployment.In order to reduce the accidental error of measurement,a Gaussian filtering model is proposed to filter the collected RSSI data.And study the dual-band fusion algorithm,the fusion of the dual-band ranging results to reduce environmental errors and improve the accuracy of ranging between nodes.Verification by actual measurement shows that the accuracy of the range measurement after Gaussian filtering and dual-band fusion algorithm processing is improved compared to the average filtering of single-band ranging.Based on the traditional positioning algorithm,the improved trilateral positioning algorithm and the improved maximum likelihood positioning algorithm are studied.The MATLAB software is used to simulate and analyze the improved algorithm and the traditional positioning algorithm.The root mean square error value of the positioning results of the two improved algorithms is lower than that of the traditional algorithm.It is verified that the improved trilateration algorithm and the improved maximum likelihood estimation algorithm are in positioning.The accuracy has been improved.Through the research of this subject,it has practical guiding significance for the application of forestry wireless sensor network node positioning technology and network deployment. |
