The Localization Algorithm Of Wireless Sensor Network That Fuses Location Mechanism Modification And Population Intelligence Optimization

With the growing scale of Internet of things technology industry,wireless sensor networks constructed by many micro nodes are becoming an increasingly important part of information technology.They collect specific information according to instructions for the monitor to understand and deal with.In many applications,nodes' perception,collection and transmission of required data has become the basic way of various monitoring activities.However,if the monitoring party fails to obtain the specific location of the measured data,it will deviate from the original intention of information collection,even if the environmental information is obtained completely,there is still no available place.Therefore,the necessity of node location research is obvious.Focusing on the positioning research of wireless sensor networks,aiming at improving the positioning accuracy,this paper explains the basic theoretical knowledge involved in node positioning research,the current research status at home and abroad,and the specific description of the relevant positioning principles.In order to better meet the application requirements of the actual environment,the existing unreasonable aspects are improved and improved.The work of this paper is as follows:(1)Aiming at the error accumulation caused by poor universality of RSSI ranging model,a localization algorithm combining RSSI ranging model modification and PSO weight optimization is designed.First of all,based on RSSI ranging model parameter calibration,avoid ranging error accumulation of the influence of the positioning stage,and then use position calculation method for rough localization,get the approximation of the unknown node coordinate,finally,improved PSO algorithm is introduced to optimize the approximate coordinates,based on the convergence factor weight strategy effectively balance the search speed and precision of algorithm,and optimal value node coordinates is obtained.The experimental results show that this algorithm can effectively suppress the error accumulation in the ranging process,and it shows better convergence performance and global optimization ability,and can achieve more accurate positioning effect.(2)In order to solve the problem of poor positioning accuracy of DV-Hop algorithm due to the calculation deviation of hop count,the deviation of average hop distance and the calculation deviation of coordinates of unknown nodes,a correction factor is used to adjust the hop count,and a virtual intersection circle is introduced to estimate the average hop distance,so as to optimize the rationality of the calculation of hop count and hop distance in DV-Hop positioning.In the calculation stage of unknown node position in DV-Hop positioning,the This paper uses the hybrid cuckoo search method to optimize,defines the fitness function and calculates the fitness value,divides the fitness value into three categories and carries on the dynamic calculation,obtains the optimal search formula of three kinds of subdivision,coordinates to achieve the local and the overall optimization of CS,so as to improve the positioning performance of the algorithm.The experimental data show that the algorithm is reliable and effective in the three stages of DV-Hop location.It focuses on avoiding the error prone processing in each step.Compared with the traditional DV-Hop algorithm and the single CS optimized DV-Hop algorithm,the performance of node location is significantly improved.In this paper,through the RSSI ranging model modification,DV-Hop hop count and hop distance modification and population intelligent optimization and other improvement strategies,considering the differences of the actual environment in the node application area,the node parameters were selected adaptively,which effectively reduced the interference of environmental factors and made the positioning effect more ideal.