The Design Of Ble Neighbor Discovery System Model For Contact Tracking Of Public Health Emergencies

The recent COVID-19 pandemic has exposed weaknesses in several areas,including the limited capacity of public health systems for effectively tracking and reporting cases in a timely manner,the research of fast-tracking solution based on Bluetooth Low Energy(BLE)neighbor discovery system has become a current hotspot.The accurate and rapid discovery and identification of these Bluetooth low energy devices from a BLE crowed environment is one of the most challenging tasks.Therefore,a system that can accurately and quickly discover Bluetooth devices has very important practical significance for Bluetooth contact tracking in emergencies.Based on the advertising and scanning working states defined in the BLE standard core specification,the traditional neighbor discovery model,which is established in the ideal environment of scanning and advertising devices,is firstly analyzed in this thesis.The scanning characteristics that the device may have in the real environment are not considered in the traditional model,so that the discovery model cannot accurately obtain the discovery performance index.Therefore,combined with the working principle of the actual BLE chip,the traditional neighbor discovery model is improved in this thesis,added scanning characteristics to the model,and a new optimized neighbor discovery model is obtained in this thesis.Meanwhile,a high-precision test scheme is designed in this thesis for the working characteristics of the scanning device,which verifies the effectiveness of the optimized neighbor discovery model.Then based on the optimized neighbor discovery model,a fast neighbor discovery scheme for BLE-intensive network environment is designed in this thesis,it obtains the discovery performance of the optimized neighbor discovery model through simulation tests.Comparing the traditional model with the optimized neighbor discovery model,the accuracy of the optimized model is verified in this thesis.Finally,an measurement platform is built in this thesis.Through extensive experiments and simulations on the optimized neighbor discovery model and fast neighbor discovery scheme proposed in this thesis,it obtains the discovery performance results of devices in a BLE-intensive network environment.When the advertisement interval is 20 ms,the data packet length is 47 bytes,and the number of surrounding advertisers is 20,the final discovery time of the optimized model in this thesis is 148.8ms,and the accuracy difference is 4.4%;when the advertisement interval is 30 ms,40ms,50 ms,60ms,the accuracy differences of the optimized models in this thesis are 2.7%,2.7%,2.2%and 3.0%.The measured results show the reliability and accuracy of the neighbor discovery model optimized in this thesis in the real environment.