WSN blind area predictive regression control model based on interpolation algorithm optimization

Abstract

In order to correct the error data contained in the sequential data collected by sensor nodes, in the relatively harsh deployment environment of sensor nodes, limited sensor nodes make WSN (Wireless Sensor Network) have monitoring blind areas. Based on Kriging interpolation method and natural neighbourhood interpolation algorithm, the problems of data specification and spatial interpolation in WSN network are solved. The research results show that the algorithm divides irregular meshes into regions to be interpolated, and then the k-hop neighbour nodes of the prediction points are determined as the parameter set of Kriging interpolation. Finally, the Kriging coefficients are solved, and the prediction data of the points to be interpolated are calculated according to the Voronoi area and the observation values of the neighbour nodes, thus the value of the prediction points is calculated. It can be seen that this saves time and space greatly, and simulation experiments show that the natural neighbourhood interpolation results are closer to the real value.