Research On WSN Node Localization Technology For Environment Monitoring

With the increasing awareness of environmental importance from people,environment monitoring is of great significance.Forest resources are scarce,forest fires occur frequently,so its environment monitoring is in need.The emergence of WSN technology provides convenience for the collection of random data in the environment,node localization is as important as the acquisition of perception data.In forest,sensor nodes are usually distributed randomly,if there is no location information,data collected by node will be meaningless.Some nodes in WSN are configured with GPS,which is called anchor nodes,while the remaining unknown nodes obtain coordinates by certain algorithms.According to different scene characteristics and application requirements of forest environment,the goal of this theses is to design reasonable position scheme and improve location precision.In this theses,WSN location technology is studied in depth,The research contents are as follows:(1)For large-scale forest environment monitoring,this theses proposes a hybrid DV-Hop algorithm based on weighted factor called HDV-Hopw,which is improved by two methods:Firstly,the average per-hop distance of beacon node is weighted to reduce the error caused by itself.Then,Position estimation of unknown nodes are transformed into objective optimization,GA-PSO algorithm is used to optimize the coordinates of unknown nodes.The feasible region of the initial population is restricted and initial population quality is improved to ameliorate position accuracy.The simulation results show that compared with the DV-Hop algorithm,average localization error of HDV-Hopw is reduced by about 11% without increasing the hardware cost,the position accuracy is obviously improved in the irregular network topology.(2)For local precious environment monitoring which is relatively small in scale and higher in location accuracy compared with large-scale forest monitoring,this theses proposes FCDC-CL algorithm based on RSSI.It will lead to high error when mapping RSSI signal strength into distance directly,more gross errors exist,more interference will be to final positioning.To improve location accuracy,the measurement distance is preprocessed firstly,then Fuzzy Clustering algorithm is used to choose and remove coarse errors according to Data Consistency principle.Finally,the improved Weighted Centroid algorithm is used to locate unknown nodes.The simulation results show that the average positioning error of the FCDC-CL algorithm is around 9.4%,while the average positioningerror of the WCL algorithm is around 25.5%,and the error of the FCDC-CL algorithm is significantly lower than that of the WCL algorithm.