Range-free Wireless Sensor Network Node Localization Algorithm Based On Intelligent Optimization

Wireless sensor network(WSN)is a self-organizing network system deployed in a certain area to monitor various environmental parameters(such as temperature,soil salinity,sound,biological behavior,humidity and so on)in the area.After the sensor node captures the information,it sends the corresponding data to the terminal to help users make policies.However,in most application scenarios,the information sent back by the node must be combined with the location of the node itself to make sense,sometimes even requiring the node to simply send its own coordinates.Therefore,the location of nodes is one of the essential information in the whole sensor network.On the basis of studying many sensor node localization methods,this paper improves and optimizes some existing results,and proposes two new localization algorithms.The main research contents of this paper include:(1)It is found that the degree of collinearity based on minimum height(DC-H)and minimum cosine of interior angle(DC-A)has shortcomings: the DC-H cannot reflect the shape of the triangle;when the unknown node is far away from the beacon node,the localization accuracy based on DC-A is low.According to these two collinearity algorithms,all unknown nodes are located by the same group of localization unit,and coordinate information of other beacon nodes is not used,which affects the final localization accuracy.In addition,some localization algorithms directly use intelligent optimization algorithm to optimize the node group.Although the overall localization error is reduced,the location deviation of individual unknown nodes will be large,and the large number of iterations will cause a large energy consumption of nodes.Aiming at the above problems,this paper proposes a two-stage WSN localization algorithm based on degree of K-value collinearity(DC-K)and improved Grey Wolf Optimization(GWO).The first stage: aiming at the defects of the existing collinearity algorithm,a new collinearity based on K-value is proposed to carry out the initial location in the first stage.The second stage: the GWO algorithm is used to optimize the location results obtained in the first stage to obtain more accurate location results.The experimental results show that the algorithm can obtain better localization accuracy,has high robustness,and has fewer iterations in the optimization process,which greatly reduces the energy consumption of nodes.(2)At present,range-free localization algorithm is the mainstream of node localization method,which has made tremendous achievements.However,there are few algorithms that can be used in concave regions,and the existing algorithms have defects such as hop distance error,excessive time complexity and so on.To solve these problems,this paper proposes a two-stage Particle Swarm Optimization(PSO)algorithm for wireless sensor nodes localization in“concave regions”.In the first stage,it proposes a distance estimation method based on similar path search and intersection ratio,and completes the initial localization of unknown nodes based on maximum likelihood estimation.In the second stage,the PSO algorithm is used to optimize the initial localization results in the previous stage.Experiments show that the proposed algorithm can obtain high localization accuracy in wireless sensor networks with concave region,and it has low requirements on computing power and relatively low energy consumption,which greatly prolongs the service life of sensor nodes.