Reproducing dynamics related to an Internet of Things framework: A numerical and statistical approach

In the Cultural Heritage domain, novel fruition and enjoyment approaches, based on Internet of Things (IoT) paradigm, have the effect to change the way people experiencing cultural spaces, such as museums and exhibitions. Within these environments, a main challenge is to classify and forecast the behaviours of visitors by collecting their movements, choices and needs. In this paper, starting from the design of an IoT system for collecting behavioural data, we propose and discuss a model able to reproduce, and then “predict”, the dynamics related to the interactions of the visitors with the exposed artworks and, in particular, with the available technologies. Generally, collected data are affected by many kinds of errors; to address this issue a powerful statistical method to reproduce the dynamics related to the visiting styles of the spectators is proposed. Numerical experiments on real data have been reported in order to assess the proposed methodology.