Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
On the empirical performance of self-calibrating WiFi location systems
LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
Locating in fingerprint space: wireless indoor localization with little human intervention
Proceedings of the 18th annual international conference on Mobile computing and networking
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Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as the unique annotations (like Fingerprints) of recorded locations. In this study, we investigate the characteristics of RSS (e.g., how the RSS values change as time goes on and between consecutive positions?). On this basis, we design LuPI (Locating using Prior Information) that exploits the characteristics of RSS: with user motion, LuPI uses novel sensors integrated in smartphones to construct the RSS variation space (like radio map) of a floor plan as prior information. The deployment of LuPI is easy and rapid since little human intervention is needed. In LuPI, the calibration of ``radio map'' is crowd-sourced, automatic and scheduled. Experimental results show that LuPI achieves comparable location accuracy to previous approaches, even without the statistical information of site survey.