Improvement Of GSO Algorithm And Its Application Research On Node Location Of Wireless Sensor Network

Wireless Sensor Networks(WSN)is a multihop adhoc network formed by the cooperation of many micro sensor nodes in the monitoring area.The location information of sensor nodes plays an important role in the application of WSN,so the node localization technology becomes the key technology of WSN.At present,theWSN localization technology is divided into range basedlocalization and range free localization technology,range based localization technology has precision positioning,but its energy consumption is large and need to install the high price of the hardware facilities;however,range free localization technology has relatively lower positioning,but the communication nodes with lower energy consumption and without additional hardware support,and due to the advantages of low cost and low the energy consumption of range-free localization technology,which have been not only widely used in practical applications,but also become a hot topic for researchers.At present,the DV-Hop algorithm is easy to realize as a typical range-free localization algorithm is widely used in WSN positioning technology.Aiming at the problem of no high precision positioning algorithm,based on the analysis of the DV-Hop algorithm,this paper introduces the Glowworm Swarm Optimization(GSO)algorithm to improve it.As a kind of intelligent algorithm,GSO algorithm has a high optimization precision and convergence speed,and it is a kind of general-purpose optimization method,which has been successfully applied to function’s optimization.DV-Hop algorithm in the calculation of unknown nodes and beacon nodes spacing,because the unknown node that WSN monitoring area in each of the average distance is the same,which may cause bigger errors,so this paper uses GSO improved algorithm for DV-Hop positioning later positioning error function is optimized to improve the positioning accuracy of unknown nodes.1.Improved glowworm swarm optimization algorithm.In view of the GSO algorithm itself exists on the distribution of the initial population dependence,slow convergence and easy to fall into local optimum,premature convergence and low accuracy of defects,proposed GSO algorithm based on chaotic mutation and GSO algorithm based on chaotic inertia weight location update,finally the two algorithms have been collected,which is based on chaotic mixing the strategy of the glowworm swarm optimization(MC-GSO)algorithm,MC-GSO algorithm is the firefly into local optimum when the surrounding each glowworm chaotic mutation and introducing the moving distance of chaotic inertia weight control of glowworm swarm.The convergence of the six test functions show that the MC-GSO algorithm has better through the Matlab simulation platform,the accuracy is greatly improved,and the local search ability of MC-GSO algorithm can avoid premature convergence of the algorithm.2.Because the average hop distance of DV-Hop positioning algorithm has errors and the accumulation of multiple errors,which makes localization accuracy of DV-Hop algorithm is low.Therefore,this paper improved glowworm swarm optimization algorithm based on the proposed MGDV-Hop localization algorithm,localization using MC-GSO algorithm instead of least squares DV-Hop in the third stage,through the establishment of error fitness function is transformed into the two-dimensional coordinates of the linear combination optimization problem,solve the defects of DV-Hop algorithm positioning accuracy is not high.Simulation results show that the MGDV-Hop algorithm improves the location accuracy,node coverage and residual energy of nodes.