Wireless Reality Analytics System And Application Research

The popularity of wireless communication technology and the development of intelligent terminals for the user's work and study entertainment provides a great convenience, the vast majority of mobile smart terminals can be WiFi connection, by monitoring the user intelligent terminal WiFi signal, you can get the user phone And the mobile terminal device, so that the mobile user behavior analysis can be performed using the collected data.User behavior analysis includes online analysis and offline analysis.Online analysis refers to the user to visit the website of the data statistics and analysis, which found the user access to the site of the law, and these laws applied to the network marketing, network marketing activities to improve the existing problems, to further amend the network marketing strategy. Similar to online user behavior analysis, offline analysis also needs to be analyzed from the user's spatial information, stays, distribution,and return visits.User behavior analysis in many areas are of great significance, such as economic forecasting, urban planning, prevention of disease transmission. Merchants can obtain customer's information to analyze customer preferences and provide better merchandise for customers. In some specific locations, users can suspicious behavior judgments to prevent crime and emergencies.The main work of this paper is as follows:(1) A review of human mobility research. Summed up and compared the GPS, mobile cellular network, WiFi three wireless data access to the characteristics of the user behavior analysis of some of the applications were introduced.(2) The three functional modules of the wireless reality analysis system are described, including the data acquisition module, the data analysis module and the web display module. Wireless reality analysis system of the overall design, bottom-up, including the base layer, data layer,application layer.(3) Introduce the contents and methods of user behavior analysis, and summarize the clustering algorithm and apply it to group user behavior analysis. Cluster analysis of normalized different buildings can be used to classify the buildings, compare the flow changes of different types of buildings, and analyze the differences of user behavior from the perspective of human traffic.(4) Combining the user group behavior analysis, the PageRank algorithm for calculating the importance of web pages is applied to calculate the importance of buildings and get the rankings of campus buildings.