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

Abstract

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 multi-floor 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 crowd-sourced 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.