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The IoT team has made important achievements

At present, large and medium cities around the world have formed a large number of crowded public places such as subway stations, high-speed rail stations, airports, theaters, stadiums, commercial complexes, etc., resulting in frequent crowds during travel and tourism peaks. , Resulting in various problems in urban management planning, public safety early warning, business intelligence decision-making, security prevention to traffic diversion, passenger flow statistics to preference analysis, etc. There is an urgent need to support population statistics, density measurement, individual identification, and trajectory tracking. And other intelligent crowd analysis functions.

 

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The Internet of Things team of the School of Computer Science of our school, together with the University of Technology Sydney, Beijing Jiaotong University, and other cooperative units, proposed the use of WiFi sniffing technology to solve the problem of crowd analysis, and achieved important research results, and used "Pedestrian Flow Estimation Through Passive WiFi Sensing" as the Title, published in the top international journal "IEEE Transactions on Mobile Computing" in the field of wireless networks and mobile computing (volume number: 20, issue number: 4, page number: 1529-1542, download link https://ieeexplore.ieee.org/abstract /document/8936381). "IEEE Transactions on Mobile Computing" is a category A journal recommended by the Chinese Computer Society. It is also a TOP journal of the Chinese Academy of Sciences SCI journal with an impact factor of 5.112.

 

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This paper reveals the relationship between WiFi sniffing data and the attributes of mobile pedestrian flow and establishes a theoretical model of mobile pedestrian flow perception based on WiFi sniffing. On this basis, a Rao-Blackwellized particle filter (RBPF) based on Rao-Blackwellized Particle Filter (RBPF) is proposed. The nonlinear filtering algorithm uses real-time sniffing data to simultaneously estimate the speed of pedestrian flow and the number of pedestrians (as shown in the figure above). To verify this research, the author deployed a pedestrian analysis experimental system based on WiFi sniffing (as shown in the figure below) on the transfer channel of a subway station in Guangzhou, which verified the validity of the theoretical model and the superiority of the algorithm.

 

Huang Baoqi, a professor and doctoral supervisor of the School of Computer Science of our school, is the first author and corresponding author of the paper, and Inner Mongolia University is the first signing unit of the paper. The research was funded by the National Natural Science Foundation of China (41871363, 61461037), the Inner Mongolia Natural Science Foundation (2017JQ09), the Inner Mongolia Autonomous Region "Grassland Talents" Project (CYYC5016), and the China Scholarship Council (CSC).

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