Research On Node Location Technology In Wireless Sensor Networks

In recent years,under the background of rapid development of Internet of Things and Internet of Vehicles,wireless sensor networks have attracted wide attention as the underlying technology of above two networks.Wireless sensor networks are self-assembled intelligent networks composed of a large number of cheap pocket sensor nodes in the monitoring area.Nowadays,wireless sensor networks have been used in a wide range of applications,such as military operations,positioning and navigation,medical care,smart life,animal condition monitoring,etc.Node location information is required in many applications of wireless sensor networks,so the research of node location in wireless sensor networks has very important theoretical and practical significance.This thesis firstly introduces some basic knowledge and key technologies of wireless sensor networks;and then describes the concept,principle and performance criteria of sensor node location;finally,this thesis introduces existing typical node localization algorithms.Combined with these algorithms,this thesis introduces the application principle of Monte Carlo algorithm and Monte Carlo Boxed Localization algorithm,and analyzes the advantages and disadvantages of these two algorithms.For the problems existing in Monte Carlo algorithm,a Monte Carlo node location algorithm based on motion prediction is proposed.The node motion is predicted by Lagrange interpolation theorem,then the sampling region is constructed and the weights are assigned to the sample points.Finally,the simulation results show that the proposed algorithm improves the positioning accuracy of nodes.In order to solve the problem of low sampling efficiency and sample points are not weighted and so on in Monte Carlo Boxed Localization algorithm,this thesis proposes a new localization algorithm based on received signal strength indication ranging(RSMCB).In the prediction stage,in order to optimize the sampling area and improve the efficiency and accuracy of node sampling,the received signal strength indication ranging technique is used to determine the anchor box.In addition,a “communication success rate” model is used to weight the selected sample points,which further improves the accuracy of node location.The simulation results show that compared with typical Monte Carlo Boxed Localization algorithm,the new algorithm can achieve higher positioning accuracy.