Three-dimensional Localization Algorithm Based On Mobile Beacon In Wireless Sensor Networks

In recent years,the development of Wireless Sensor Network(WSN)has become more and more rapid.It can not be separated from the location technology of the node.The current research on the WSN location technology is in two dimensions,and the corresponding sensor nodes are basically in reality is distributed in three-dimensional space.Therefore,the WSN three-dimensional node location technology studied in this paper has very important practical significance.Firstly,this paper summarizes the research purpose,significance and the technical theory of WSN,summarizes the development status of WSN location technology at home and abroad,representative localization methods in the current location technology and node coordinate calculation methods,and also describes the location technology.Related concepts and performance indicators.Secondly,aiming at the problem of localization accuracy,low success rate and high energy consumption in fixed anchor nodes in three-dimensional space,an improved algorithm is proposed.In the three-dimensional localization of WSN,a moving anchor node is introduced to allow it to move according to the improved trajectory model,and periodically broadcast their own coordinate position information,forming a plurality of virtual anchor nodes,the virtual anchor node together with the fixed anchor node to participate in the calculation of the unknown node position.Based on this,through the research of RSSI and TOA ranging algorithms,it is known that RSSI ranging has the advantages of low hardware requirements and high accuracy in close distance measurement.However,RSSI ranging is easily interfered by environmental factors during the localization process,and the farther the distance is,the more affected the node is.While the TOA ranging localization algorithm has a relatively large error in the close range measurement,it has a higher accuracy time measurement device and has a higher accuracy in the long distance measurement.Aiming at this,the three-dimension localization algorithm of RSSI and TOA fusion ranging is proposed.When the anchor node is within the communication range of the unknown node and the distance to the unknown node is less than 10 m,the RSSI ranging algorithm is used to measure.When the anchor node is within the communication range of the unknown node and the distance to reach it is greater than 10 m,the distance measurement is performed on the node using the TOA ranging algorithm,and the accurate distance value between the anchor node and the unknown node is obtained.Then use the maximum likelihood estimation method to get the estimated coordinates of the node.Third,in order to further reduce the localization error in the localization stage,based on the mobile anchor node,the least squares support vector machine(LSSVR)is studied to know that this method has better generalization in the case of small samples.Ability,and it has strong anti-noise ability.Using LSSVR to establish a three-dimensional localization model,the measured distance value between nodes obtained by RSSI and TOA fusion ranging algorithm is taken as the input vector of LSSVR,thereby improving the accuracy of localization,reducing the networking cost and power consumption of the network,and achieving unknown node coordinate estimate.Finally,a simulation environment that can be applied to multiple algorithms is designed through Matlab.From the aspects of energy consumption,anchor node density,communication radius,measurement distance and other aspects,the results show that under the same three-dimensional experimental conditions,this paper is based on the localization algorithm of RSSI and TOA fusion ranging of mobile anchor nodes.10% of the fixed anchor nodes can achieve the localization accuracy that can be achieved before using 30% of fixed anchor nodes,and the localization energy consumption is greatly reduced.On the basis of the introduction of mobile anchor nodes,the LSSVR localization algorithm based on RSSI and TOA fusion ranging is used to obtain the results,which further improves the localization accuracy of the nodes and obtains a better localization effect.