Floor Classification on Crowdsourced Data for Wi-Fi Radio Map Construction

Utilizing implicitly crowdsourced data is a popular approach for a Wi-Fi radio map construction for indoor positioning. The main advantage of implicit crowdsourcing is demanding less effort. A Wi-Fi radio map is constructed in an automated way by analyzing crowdsourced data. However, some of the studies working on the crowdsourcing approach do not consider a multifloor environment, making their methods less practical. In this paper, we propose a method separating implicitly crowdsourced data by floor. The proposed method assumes that the crowdsourced data include sequences of barometer data and that the information of the building where the data were collected is given. The proposed method can transform the crowdsourcing-based method for single-floor environments into a method for multifloor environments.