Research On Fingerprint Location Algorithm Based On IBeacon In Smart Classroom

With the rapid development of information technology and its in-depth application in the field of education,multimedia classroom has gradually developed towards the direction of cloud-based integration,interactive diversification,mode diversification,behavior visualization,intelligent management and control,and energy-saving green intelligent classroom,and has become an important place for large data collection in education and teaching.Location data is one of the key data in multi-source heterogeneous data of classroom teaching.Aiming at the method oflocation data acquisition,this paper combines the application scenarios of intelligent classroom,studies the location problem in intelligent classroom based on Beacon(Bluetooth Low Energy),and proposes a location algorithm with high location accuracy,low algorithm complexity and strong environmental adaptability.Firstly,the development status of intelligent classroom and indoor positioning technology is analyzed,and the necessity of this research is also discussed.Comparing and analyzing common indoor positioning technologies,such as WiFi(Wireless Fidelity),ZigBee and Bluetooth positioning technotogy,combined with the application of smart terminal(mobile phone or flat panel)in smart classroom,this paper emphatically introduces the positioning algorithm based on signal attenuation model,smart phone sensor model and fingerprint model,and determines the basis and direction of this research on location algorithm in smart classroom.Secondly,theprinciple of fingerprint localization algorithm based on KNN(k-Nearest Neighbor)is deeply studied,which is analyzed in three stages:data analysis,off-line sampling and on-line tocalization.Based on the framework of KNN fingerprint localization algorithm,the three stages mentioned above are improved and optimized,and an improved KNN fingerprint localization algorithm based on Beacon is proposed.Gauss filtering model is used to filter and remove the dryness.K-means clustering algorithm with initial value optimization is used to divide the large localization area into nany small areas.Weight exponents ? and ? are introduced into fingerprint database matching and weighted centroid algorithm to improve the localization accuracy and environmental adaptability,and reduce the complexity of the algorithm.Finally,in order to verify the correctness and availability of the algorithm,the iBeacon networking system is built in the smart classroom,the iBeacon AP device is deployed.the background server and Android mobile program are developed,and the experimental data are collected.The experimental data are modeled and simulated in MATLAB environment to determine the parameters of the algorithm,and the effect ofthe Gaussian filter de-drying effect and the weight index on the positioning error is analyzed in detail.The performance of the improved algorithm is compared with that of the traditional algorithm.The experimental results show that the algorithm in this paper improves positioning accuracy,algorithm corrplexity,environmental adaptability and so on.This paper uses iBeacon technology of low-power Bluetooth to study the indoor location algorithm,and obtains accurate location information of personnel in intelligent classroom,so as to construct a panoramic view of education and teaching for individuals and the whole.Results of the research can provide data support for teaching process analysis,education monitoring and evaluation,education intelligent decision-making,and provide services for teaching management,teaching guidance and teaching research.