Research On Localization Technology Of WSN Node Based On Markov Chain Optimizing Ranging

Localization technology is the application support technology of wireless sensor network,and is also the research hotspots.In the complex indoor environment,the change of the external environment will cause the ranging error,so reducing the ranging error to improve the positioning accuracy is more important,because the traditional ranging model can not adapt to the changes of the external environment,the paper chose the adaptive ranging model as the foothold to carry out the research on the subject.This paper first analyzed the causes of irregular signal transmission are transmission medium and equipment,and analyzed the characteristics of different attenuation for distance measurement models in different environments.On the basis of the analysis of the adaptive BP range model,this paper proposed a Markov chain to modify its existence error to achieve the purpose of optimizing BP model.Combining neural network and statistics,a distance estimation model based on B_Markov chain was proposed.The validity of the proposed model was verified by the experimental results.Then the MDS-MAP positioning algorithm was used as an example to simulate the location of the nodes,and this paper designed the experiment by controlling the single variable method after analyzing the factors of affecting node localization such as deployment of nodes,and the results are analyzed and compared.It was verified that the localization result of range method is to a certain extent superior to the localization result of non range method,and the deployment of the node should also be determined in combination with the actual situation.The specific contents of this research are as follows:(1)First,the paper analyzed the factors of irregular signal transmission are linked to transmission medium and equipment,combining the characteristics of different ranging models to conclude the causes of the ranging error,different transmission medium will produce the different path loss.Because of the different workload and environment,different devices may result in communication range changes.(2)Second,the paper analyzed the shortcomings that the traditional distance measurement models are not self adaptable,selected the adaptive distance measurement model as the research object.On this basis,the paper proposed that using the Markov chain to analyze the error of adaptive distance measurement to correct the error.A distance measurement model of B_Markov chain was proposed by combining neural network and statistical thinking,and the model was analyzed and verified by using the real datameasured in the experiment.(3)At last,the paper analyzed the characteristics of the typical location algorithm,and took the MDS-MAP positioning algorithm as an example to simulate.Combining with different node deployment methods,the paper compared the results of node localization based on ranging and non ranging.It was verified that the localization result of range method is to a certain extent superior to the localization result of non range method,and the deployment of the node should also be determined in combination with the actual situation.