WiFi Hotspot User Time Characteristic Analysis Based On The Wireless Detection

The study to human mobility has caused a lot of attention in many fields,and has significance to the study of human behavior and the social relationship,such as forecasting stock movements,road planning,prevention of the spread of disease,crime behavior monitoring.The time attribute of both group users and individual users seems to be disordered,but actually there are rules to follow.The research on the time attribute of human mobility can be used to explore the characteristics of human behavior,such as the optimization of marketing strategy,intelligent furniture,identity recognition,and criminal behavior prediction.In recent years,with the rapid development of mobile Internet and mobile intelligent terminal,these technologies make it possible to obtain large scale,long time,and high accuracy data.These data collected by mobile terminals reflect not only the user's consumption level,but also the user's certain behavior,preference,time regularity and moving track.To study the human mobility time attribute in a local region,this paper discussed the advantages of Wi-Fi technology,we realized the analysis system for intelligent mobile terminal monitoring wireless reality,and optimized the function module of the system.On the basis of the study of time attribute of human mobility,the further research on the time attributes of both group and the individual user were carried.The analysis results of the model can be applied to urban,transportation and other public services,and can also be used in the identification,crime prediction,prevention of congestion and make reasonable marketing strategy.For the time attribute of individual users,we can extract individual user behavior characteristics;distinguish normal behavior or suspicious behavior,male or female and identity.Finally,with the analysis of human mobility,wireless reality analysis system and time attributes,this paper completed the following work and Innovation:(1)Optimize some functional modules of wireless reality analysis system:optimize the presentation module;optimize the data acquisition module,which can expand the range of data collection,and achieve multi point monitoring;develop some new modules such as the comparative analysis of multi shops,the relationship among multi points analysis function,report and summary function.(2)Analyze the resident time distribution in the view of the group users.Using the AIC,the cumulative residence time in the different scenarios is verified by a power law distribution model,and the daily average residence time is consistent with the exponential truncated power law distribution.(3)Analyze the resident time distribution law in the view of the individual user,and distinguish the identity.It is concluded that the probability distribution of the daily dwell time of the users on time is in line with the Gauss distribution,and the highest peak time is about 13?14 hours.What's more,the shorter the residence times of the laboratory students,the longer the probability distribution of the time in the day.(4)Predict the visit time of certain individual user at the effective monitoring point in the campus using the nonlinear time series method.