Research On Node Localization In Wireless Sensor Networks

As one of the supporting technologies of wireless sensor network,node localization technology plays an important role in the effectiveness of the application of sensor network.The location of the event or the location of the node obtaining the information is the important information contained in the monitoring message of the sensor node.The monitoring message without the location information is often meaningless.Wireless sensors are randomly placed in a specific environment,and work in a self-organized way.The randomly placed nodes often cannot know their location in advance,so they should be able to locate in real time after placement.WSN node localization can be divided into two types,Localization Based on distance measurement and localization based on non-distance measurement.Localization based on distance measurement is to estimate the location of sensor network by measuring the actual distance between sensor nodes.The localization accuracy of this kind of localization algorithm is relatively high,but it needs hardware as support,so it is not suitable for application in some low-power,low-cost fields.Localization based on non-range mainly uses the connectivity between anchor nodes and unknown nodes in the network to obtain the relative coordinates between nodes,and then transforms the relative coordinates into absolute coordinates through certain methods to achieve the purpose of localization.This kind of localization method does not need specific hardware facilities as support,but the positioning accuracy is relatively low.The main contents of this paper are as follows:In recent years,with the rapid development of meta-heuristic algorithm with evolutionary ideas,it has attracted the attention and research of many scholars at home and abroad.Compared with the traditional deterministic optimization method,swarm intelligence algorithm is more intuitive and effective to solve the optim ization problems in complex computing.In this paper,the group intelligent optimization algorithm is introduced into the research of localization algorithm.Aiming at the problem of low positioning accuracy of DV-Hop algorithm,A new localization algorithm of OSFL-TLBO is proposed,and a reverse learning mechanism is added.It is used to replace the accumulated error of the average hop distance with Euclidean distance and to improve the least square method of sensitive to the initial value and greatly affected by the measurement error when solving the nonlinear equation.The localization problem of wireless sensor network nodes is transformed into the problem of finding the optimal solution.Simulation results show that The simulation results show that the positioning accuracy of the proposed improved by about 10%-25%,the algorithm effectively improves the positioning accuracy.In order to improve the positioning accuracy of sensor network nodes under low anchor node density,a node positioning algorithm based on multi-dimensional scale(MDS)is proposed.The algorithm first obtains the relative distance between nodes by multidimensional scaling technology,then constructs the fitness function,uses the cuckoo search algorithm(CS)to find the optimal soluti on,and adds chaos sequence and adaptive parameters to make it have better search ability.The natural law of "survival of the fittest" is used to effectively improve the loca lization accuracy and make the localization algorithm more robust.The simulation results show that the algorithm effectively improves the positioning accuracy of the nodes,and is more robust in node localization.