Journal article

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

Shuai Sun, Yan Li, Wayne ST Rowe, Xuezhi Wang, Allison Kealy, Bill Moran

IEEE Sensors Journal | Institute of Electrical and Electronics Engineers | Published : 2019


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..

View full abstract