A Portable Indoor Localization Framework for Smartphone Heterogeneity Resilience

Abstract

Indoor localization is an emerging application domain that promises to enhance the way we navigate in various indoor environments, as well as track equipment and people. Wireless signal-based fingerprinting is one of the leading approaches for indoor localization. Using ubiquitous Wi-Fi access points and Wi-Fi transceivers in smartphones has enabled the possibility of fingerprinting-based localization techniques that are scalable and low-cost. However, the heterogeneity of Wi-Fi hardware modules and software stacks used in smartphones nowadays introduces problems when employing Wi-Fi-based fingerprinting methodologies across devices, hence diminishing the accuracy of localization. Through this chapter we propose a framework called SHERPA that enables efficient porting of indoor localization techniques across mobile devices, to maximize accuracy. An in-depth analysis comparing two variants of our proposed framework proves that it can deliver up to 8× more accurate results as compared to state-of-the-art localization techniques for a variety of environments.