Practical Evaluation of a Crowdsourcing Indoor Localization System Using Hidden Markov Models

Abstract

The extensive deployment of wireless infrastructure provides a low-cost approach to tracking of mobile phone users in indoor environments using received signal strength (RSS). Crowdsourcing has been promoted as an efficient way to reduce the labor-intensive site survey process in conventional fingerprint-based localization systems. Despite its stated advantages, use of crowdsourcing for localization has issues of accuracy and reliability in indoor applications, in large part because of multipath propagation. This paper discusses and evaluates a Bayesian approach to localization of mobile users based on a crowdsourced fingerprint, in which environmental constraints as well as dynamic property..