Groups and frequent visitors shaping the space dynamics

Abstract

Our research is about a dynamic symbolic space model that is fed with data from the environment by a set of processing modules that receive raw data from sensor networks. For the conducted experiments we have been using data from a WiFi network as it is a widely available infrastructure in our campus. Here we propose two processing modules which will provide more information about the spaces described in the model. The first one tries to implement our human perception of the usual visitors of a place using two measures, the long term and the short term tenant level. The second one detects where groups of users emerge, how many there are and what are their dimensions. Based on this new perspective of the campus we intend to realize how the presence of people shapes the dynamics of a space.