Opportunistic occupancy estimation in museums through wireless sensor networks

A low-cost and scalable solution for the estimation of indoor occupancy in museums (or, more in general, indoor sites) is proposed by exploiting in an opportunistic fashion wireless architectures already deployed for artworks conservation purposes. The information on the presence and the distribution of the visitors is yielded throughout the analysis of easily available environmental parameters. Indeed, the relationships between environmental indexes and museum fruition are inferred with a suitable learning-by-example technique. An experimental application to a real museum site is presented and discussed to give a proof of the effectiveness and reliability of the proposed approach