| In location-based service applications,wireless sensor network(WSN)collects data through sensor nodes.If the node localization information is missing,the collected relevant data will not play its due role.It can be seen that accurate localization is the premise of location-based service application.Therefore,the research on WSN localization algorithm has very important application value.WSN knows the measured distance between the nodes and establishes a set of localization equations.Usually,intelligent optimization method is used to solve these equations to obtain the location of the node to be located.Among the intelligent optimization methods,the chicken swarm optimization(CSO)algorithm has become one of the hot spots in the research of WSN localization algorithm because of its good optimization performance and fast convergence speed.However,the algorithm does not make full use of the measured distance between the nodes to be located,resulting in incomplete measured distance,which limits the improvement of location accuracy.Therefore,it is necessary to further study the localization algorithm based on chicken swarm optimization to ensure that the ranging information between the nodes to be located is fully used and improve the location accuracy.This paper focuses on this problem and the main work is as follows:(1)Aiming at the problem that the measured distance used by the traditional CSO localization algorithm is not complete,resulting in the limited improvement of its location accuracy,a CSO cooperative localization algorithm is proposed.Firstly,the generalized 2D polynomial chaotic map is used to initialize the chicken population which makes the individual distribution of the population more uniform and is conducive to the search of the optimal location.Then a new objective function is constructed,which not only considers the measured distance between the node to be located and the reference node,but also considers the measured distance between the nodes to be located to ensure that all the measurement distances are fully used,thus improving the location accuracy.The simulation results show that compared with the traditional CSO localization algorithm,the CSO cooperative localization significantly enhances the location accuracy and improves the localization performance.(2)The objective function used in the CSO cooperative localization algorithm changes the dimension of the solution space from low dimension to high dimension,which not only reduces the convergence speed of the algorithm,but also makes the location result easy to fall into local optimization at the later stage of the algorithm.To solve this problem,an improved CSO cooperative localization algorithm is proposed.The algorithm uses the good initial population generated by MDS method,which can accelerate the population convergence,and improve the convergence speed of the algorithm.At the same time,Levy flight strategy is introduced to help search the optimal location.The simulation results show that the improved CSO cooperative localization algorithm can not only accelerate the convergence of location results,but also improve the location accuracy.(3)In order to verify the performance of CSO localization algorithm,a localization platform is built with Matlab tools.The localization platform is composed of two parts: the localization program and the localization platform interface.It can achieve the following functions: the comparison of location results,the implementation of CSO localization algorithm,and the setting of CSO parameters. |
