Improved DV-Hop model based on the application of variable scenarios

Abstract

This work proposes an improved DV-Hop model based on function analysis and simulation parameter determination (named FuncDV-Hop) to address the problems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step function segmentation experiment, weight function strategy with equivalent points, and maximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distance estimation, and least square calculation of the DV-Hop model. The experiment is designed using the control variable method. The total number of nodes, the ratio of beacon nodes, the communication radius, the number of beacon nodes, and the number of unknown nodes are designed as variables to control the experiment. Finally, the experiments of two stages of simulation parameter determination and integration optimization are carried out. The simulation positioning error of all scenarios is reduced by 0.0630–0.2472, and the positioning accuracy is increased by 23.81%–75.85%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios compared with the other models, the localization error is reduced by more than 0.1, and the optimization rate is increased by more than 10% in the record parameters of the existing wireless sensor network system.