Research On Indoor Positioning Method Based On Social Internet Of Things

Social Internet of Things(SIo T)technology is not only a new technology that can perceive the real environment,but also a new technology that can establish social relations with objects.Social Internet of Things(SIo T)technology enables the discovery of objects and services through the interaction of objects and the connection between commodity networks.In order to solve the problem that the traditional sensor-based indoor positioning technology requires high hardware,this paper proposes a novel indoor positioning method based on SIo T.This method associates the semantic fingerprint data set with the logical method to obtain the discrimination in the sub-scenario.Therefore,this method can obtain a more accurate positioning position.Aiming at the problem that the WIFI signal is easily interfered by objective factors such as the environment,this paper proposes a filtering method based on a weighted median Gaussian filter.For the problem of updating the fingerprint database,this paper designs a semantic extraction model based on WIFI data information extraction.For the locatinginsmall scenes,this paper proposes a sub-scenario localization model based on multi-dimensional spatial similarity.In particular,in the sub-scenario model,this paper uses an indoor positioning method based on multi-dimensional spatial similarity.This indoor positioning method based on multi-dimensional spatial similarity can effectively improve positioning efficiency.The proposed indoor positioning mechanism based on the social Internet of Things has experimentally evaluated the performance of the algorithm.The experimental results prove the effectiveness of the indoor positioning method proposed in this paper.The main contents and contributions of this paper are as follows:I The basic principle of indoor positioning based on social internet of things is deeply studiedThis paper firstly systematically analyzes the principle of traditional indoor positioning technology,and also deeply studies the principle of social Internet of things and its application prospect in the field of indoor positioning.Secondly,this paper also studies the RSSI ranging model and indoor location algorithm based on RSSI fingerprint data set.Based on the above research,this paper summarizes their advantages and defects,and proposes effective solutions to the defects while retaining the advantages.II An indoor location algorithm based on the social internet of things with the similarity of multi-dimensional space is proposedIn order to overcome the influence of random errors on indoor positioning results,this paper proposes a method to divide the acquisition stage of RSSI fingerprint data set into signal acquisition stage and cloud preprocessing stage.In the signal acquisition stage,thecentrifugal direction(CD)method is used to receive signals from eight directions at the reference position.In the stage of cloud preprocessing,the weighted median gaussian filter is used to process the signal data in order to reduce the influence of signal error interference.In order to improve the positioning accuracy,this paper uses MDSS to obtain a high correlation between the signal and the distance to conduct indoor positioning,so as to improve the accuracy of the positioning results.In order to overcome the problem of random error caused by directly predicting the position of the measured points with the subset of the fingerprint data set with the highest spatial similarity,a method to improve the accuracy based on the signal option value is proposed in this paper.Based on the social Internet of things,the multi-dimensional spatial similarity indoor positioning algorithm can not only effectively reduce the workload,but also reduce the data preprocessing time.III A multi-scene fusion indoor positioning method based on social internet of things is proposedIn order to improve the indoor positioning model,to upgrade and preprocess the data set of fingerprint scene,this paper proposes a new semantic extraction model(SEM).Based on the semantic information extraction technology,the SEM model can effectively extract useful scene information data in the social Internet of things and store the scene signal data in the scene data set.The model can realize the problem of fingerprint data set upgrade and solve the problem of data set update effectively.In order to obtain more reliable and accurate sub-scenes,based on the principle of artificial neural network(ANN),this paper proposes a Subscene identification(SSD)model that uses the semantic information of fingerprint database to filter sub-scenes.The SSD model can make full use of the data information of the scene data set to realize the function of selecting subscenes accurately.In order to improve the accuracy of indoor positioning results and realize the positioning conversion from large scene to sub-scene,this paper proposes a positioning optimization model based on WIFI information of sub-scene.This model uses specific sub-scene fingerprint signal to achieve more fine-grained indoor positioning function.In addition,by processing the fingerprint signal of a specific scene,this paper can obtain a sequence of reference positions in order.In the multi-scene fusion indoor positioning method based on social internet of things,the sequence of sub-scenes can be taken as the weight factor,by which the paper can obtain more accurate indoor positioning results.The experimental results show that the indoor positioning method based on the social internet of things(SIo T)proposed in this paper solves the indoor positioning problem that the weighted KNN positioning algorithm and the triangle positioning method cannot be well realized in a variety of scenarios.In addition,this paper also performs experiments to prove the effectiveness of the proposed sub-scenario identification and sub-scenario location optimization model in modifying positioning errors.