Wireless Reality Monitoring Technology And Application Research

With the continuous development of wireless communication technology, it is possible to get the user information based on the wireless terminal, which provides new approaches to research user behavior and population mobility. We will use wireless communication technologies like cellular mobile networks, Global Positioning System (GPS), Wireless Local Area Networks(WLAN), etc. to get the user information, which combine the user surrounding information (location information) to analyze behavior of users in the real world called wireless reality monitoring technology. To analyze reality through wireless communication technology, it is of great value for users, advertisers and venue operators (similar to online analysis of" publisher "), so that users will benefit from the personalized information, advertisers will advertise more effectively, venue operators will allow users to maximize the participation of their place.Wireless local area networks have deployed conveniently, flexible topologies and mobility features, which make the WLAN network is widely used in schools, hospitals, commercial and residential quarters and other places. With the large number of users, it becomes more widespread to access the WLAN anytime and anywhere through the intelligent terminals. So WLAN signal generated by monitoring the user’s smart terminal can be used to obtain user information. Through the analysis of the acquired WLAN Received Signal Strength Indication (RSSI) we can get the users’location information, by analyzing the length of time the user access to the WLAN we can get the users’ dwell time, by long-term observation we can further analyze the frequency of a user’s appear in a place, and we can even analyze personal preferences, habits and other information of users.For wireless reality monitoring technology and its applications, the research of this thesis described as follows:1) We carry out a detailed investigation of background and current situation of wireless reality monitoring technology, and summarize the content and features of the wireless reality monitoring technology. Data acquisition technology (GPS, cellular mobile network, WLAN) commonly used in wireless reality monitoring technology are summarized and compared. We do a lot of research work on wireless reality monitoring technology, and design and implement a wireless reality monitoring system based on WLAN.2) Acquiring the user’s location information is the most critical factor in the wireless reality monitoring technology and user reality research. RSSI is the primary method to obtain position information. Combining traditional wireless signal propagation properties and the wireless signal loss model, we actual measure and analysis signals for indoor and outer reality monitoring technology based on the WLAN. Through mass measurement data, we obtained relationship between outdoor WLAN signal strength and distance, for indoor situation, we propose a new model of the relationship between the distance of the WLAN signal strength.3) For research on wireless reality monitoring technology application, we propose three algorithm models of wireless reality monitoring technology statistical indoor crowd based on WLAN. Firstly, through the relationship of indoor WLAN signal strength and distance judgments made indoor and outdoor WLAN signal strength based model. Then, in order to avoid the practical application of the model to determine the decision threshold through a lot of testing, we also raised the indoor and outdoor judgment based on statistical distribution model, the method by training samples to determine the indoor and outdoor decision threshold. We found that the decision error is mainly caused by the behavior of users mutations generated RSSI value in practical applications, based on this, we proposed improved statistical decision distribution model (based on the judgment of indoor and outdoor models of user behavior). By applied to the actual scene, the three judgment models are compared.4) Improved statistical distribution judgment model will be applied to actual shops, we realize the indoor crowd flow real-time statistics, and analysis of new and old customers, and the statistics of daily flow and customer access frequency.