Symbolic space modeling based on WiFi network data analysis

Access point usage patterns reflect to a certain extent characteristics of spaces where they are deployed, and allow for the identification of similarities between those spaces. Our analysis of the WiFi network as a proxy to space usage was conducted aiming to use it as a means for the characterization of physical spaces and, consequently, as a source of information for a dynamic symbolic model representing those spaces. Our objective is to connect the WiFi data acquisition system to the world model through a set of processing modules that will be able to extract useful information from the available WiFi network data and trigger the automatic updating and expansion of the model. For each type of analysis, we identify possible outputs for the space model and propose a set of rules for its update.