GraphLoc: a graph-based method for indoor subarea localization with zero-configuration.

        Indoor subarea localization remains an open problem due to existing studies face two main bottlenecks, one is fingerprint-based methods require time-consuming site survey, another is triangulation-based methods is lack of scalability in large-scale environment. In this paper, we aim to present a graph-based method for indoor subarea localization with zero-configuration, which can be directly employed without offline manually constructing fingerprint map or pre-installing additional infrastructure. To accomplish this, we first utilize two unexploited characteristics of WiFi radio signal strength to generate logical floor graph,, then formulate the problem of constructing fingerprint map in terms of a graph isomorphism problem between logical floor graph, physical floor graph. Then, a Bayesian-based approach is utilized to estimate the unknown subarea in online localization. The proposed method has been implemented in a real-world shopping mall, extensive experimental results show that our method can achieve competitive performance comparing with existing methods.