Research And Improvement On Range-free DV-Hop Localization Algorithm

Wireless Sensor Network(WSN)has been widely applied in many fields such as military,life,industrial production,and agriculture.These applications are often inseparable from the location information of the nodes,so the node location technology has become the hot research.The node location technology can be divided into range-based and range-free algorithms based on ranging.This paper focuses on the DV-Hop algorithm based on range-free,then it analyzes and improves the errors.The content of this paper mainly includes the following aspects:1.This paper introduces several typical range-free positioning algorithms and range-based positioning algorithms,and also introduces the basic calculation methods of three kinds of node positioning and the performance evaluation criteria of these algorithms.2.The paper focuses on the DV-Hop algorithm and analyzes the error.On this basis,it is proposed to introduce multiple communication radii to refine the minimum hop value,and divide the network based on the adjacent area.Select the optimal average hop distance according to the hop count to calculate the hop distance.Through the simulation experiment,the node density and the anchor node density and communication radius verify the performance of improved algorithm.3.DV-Hop algorithm based on adaptive particle swarm optimization algorithm is proposed.When the traditional DV-Hop algorithm uses the matrix equation Ax=b to estimate the unknown node position in the last step,the singular matrix is often generated when the matrix is inversion,resulting in great errors between the estimated coordinates and the actual coordinates.This paper uses particle swarm optimization algorithm instead of the common position calculation method to iteratively find the optimal value,but the standard particle swarm optimization algorithm is easy to fall into the local iterative loop in the iterative process,so that the final convergence result is quite far from the real value.Therefore,the DV-Hop algorithm based on the adaptive particle swarm optimization algorithm based on adaptive weight and change learning factor is proposed.The three important factors inertia weight w,learning factor c1 and learning factor c2 are used to optimize the particle.Through simulation experiments,the performance of the improved algorithm is also tested from the node density,anchor node density,and communication radius.The experimental results show that the improved algorithm can effectively improve the positioning accuracy.