Research On Outlier Location Algorithm In Sensor Networks

With the continuous improvement of modern communication technology,people's requirements for the reliability and security of path transmission are also increasing.However,traditional communication technologies still have some drawbacks,such as interference between paths,limitations of resource bandwidth,and low energy efficiency.The outlier estimation algorithm proposes to improve the performance of the communication system within a certain range.At the same time,the transmission rate and bandwidth capacity of the communication system are also significantly enhanced,which has caused scholars to have extensive interest in outlier positioning algorithms.This paper mainly studies and improves the outlier-based positioning algorithm in wireless sensor networks.The main contents and innovations are as follows:First of all,from the research history and significance of wireless sensor positioning technology,both sides have a detailed understanding of the term positioning,and have a certain understanding of the development of the positioning system,and introduce the current domestic and international status of positioning technology.Learned the basic principles and basic concepts of network coding.Secondly,in order to improve the positioning accuracy of the optimal hopout outlier algorithm,this paper proposes outlier location algorithm based on optimal hop distance in Wireless Sensor Networks(OIPDV-HOP).The OIPDV-HOP algorithm divides the anchor node according to the number of hops between the anchor node and the unknown node.The anchor nodes in different regions choose different hop distances to estimate the distance.Then,based on the classical anti-outliers,the comprehensive use of the screening method will be used.After the abnormal factor is checked,the system is updated to expand the search range of the anomaly factor,and the global mean solution is updated by considering the position mean of the abnormal factor and the mean value of the individual extremum,and the positioning result is optimized.Simulation experiments show that compared with outlier location and outlier location algorithms,the positioning accuracy of OIPDV-HOP algorithm is greatly improved and has good positioning stability.Finally,in order to reduce the positioning error and computational complexity of the outlier algorithm,this paper proposes outlier location algorithm based on the combination of robust estimation and least squares estimation(RAP-KNN).The implementation process of the algorithm is: when the offline phase is used,the data information to be measured is counted,and the positioning points with lower reproducibility rate or smaller signal strength are eliminated,and the Fisher criterion is used to select the location identification point with strong positioning points for positioning;In the phase,the error value calculated based on the offline data is assigned different weights,and is used to calculate the chi-square distance of the similarity between the po int to be measured and the reference point,and select a plurality of reference points with smaller distances to estimate Measuring point location.The experimental results show that compared with the KNN algorithm,the RAP-KNN algorithm can achieve reliab le position estimation,and improve the real-time performance of the system while reducing the positioning error.