Abstract: Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, Wi-Fi fingerprint-based approaches are the most popular solutions, and the core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner, however the low participation rate blocks the practical use.
In this work, we propose a passive crowdsourcing CSI-based Indoor Localization scheme, C²IL. Despite a crowdsourcing-based approach, our scheme is totally transparent to client except the only requirement is to connect to our 802.11n APs. C²IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n Channel State Information (CSI). Knowing the walking speed of a client and its surrounding APs, a graph-matching algorithm is employed to extract the RSS fingerprints and establish the fingerprint map. In localization phase, we design a trajectory clustering-based localization algorithm to provide precise realtime indoor localization and tracking. We developed and deployed a practical working system of C²IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2m at 80% time in very complex indoor environment.