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Research On Localization Algorithm For Wireless Sensor Networks Based On Particle Swarm Optimization

Posted on:2018-11-11Degree:MasterType:Thesis
Country:ChinaCandidate:C ZhangFull Text:PDF
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Wireless sensor network is a distributed network system,which is composed of a large number of sensor nodes deployed in the monitoring area with communication function.It have been applied to many areas such as agriculture,medical care,intelligent furniture,environmental monitoring etc.In the application of wireless sensor networks,the location information of network nodes becomes the necessary precondition for the realization of these applications.In addition,the positioning of wireless sensor network is of great help for network topology control,load balancing,network routing mechanism.It can be said that node positioning technology is one of the most important technologies in the whole wireless sensor network knowledge system.Therefore,how to efficiently and accurately locate the target nodes of the network has a profound impact on the theory and application of wireless sensor networks.According to the positioning phase of the need for distance measurement information,the method of locating wireless sensor network is divided into two methods based on ranging and free-ranging.Among them,the method based on distance measurement is widely used in the practical application because of its high positioning accuracy and high efficiency.At present,most research on localization method based on distance measurement mainly focuses on the following aspects: the actual node distance measurement correction,Optimization of location algorithm itself,the nodes' detection and processing method of flip ambiguity.In this paper,we will study the localization of wireless sensor networks from the three aspects mentioned above.The main research work and innovation of this paper are as follows:(1)The correction of the actual measurement distance.In this paper,based on the analysis of the positioning method based on measurement distance,the main reasons for the node localization error in wireless sensor networks are summarized.According to the influence of the distance between the nodes on the evaluation of the node position,this paper uses the measurement distance between the nodes to obey the Gauss distribution,and derives a new objective function for positioning.The new objective function takes full advantage of the mathematical laws of the distance between the nodes and the position constraint of the target node,which greatly reduces the positioning error caused by inaccurate measurement distance.The experimental results show that,compared with the existing correlation methods,the positioning results obtained by using the objective function greatly improve the node localization accuracy.(2)Optimization of positioning method based on particle swarm optimization.Through the analysis of the principle and process of the traditional particle swarm optimization algorithm,it is found that the particle swarm optimization algorithm is easy to fall into local optimum when solving the optimal value problem.Aiming at the problem that the particles fall into local optimum,this paper proposes a new algorithm based on the two direction chaotic search algorithm by introducing the chaos search strategy and the backward learning strategy.To a large extent,the algorithm avoids the local optimum of the particles.Experimental results show that the proposed method is more close to the optimal solution compared with the traditional particle swarm optimization algorithm.(3)Detecting and processing method of flip ambiguity.It can be seen from the research of the localization method of the existing sensor network,the research on the flip-ambiguity phenomenon of the target node in the localization process is not much.Many scholars in the positioning method used to enhance the positioning of the node to anchor nodes,such a simple and crude way,to deal with the flip-ambiguity phenomenon.This method will reduce the positioning accuracy of the whole network node in the case of large error in the evaluation of the location of the target node.In addition,some scholars introduce tricky complex flip-ambiguity detection method in the process of node localization.To solve this problem,using this necessary condition whether neighbor anchor nodes is approximately collinear,this paper put forward flip ambiguity detection method based on linear fitting.Firstly,According to the principle of linear fitting,the anchor node position information are used as input of fitting a straight line,and then calculating the anchor nodes distance to the line is in a given threshold range or not determine whether the target node location is flip.The method is simple,and has high detection efficiency.Simulation results show that,compared with the existing methods,the method has less detection time and greatly improves the efficiency of node localization.
Keywords/Search Tags:wireless sensor network, bidirectional chaos search localization, gauss distribution, particle swarm optimization, reverse learning, linear fitting
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