SELF-CALIBRATING PARTICIPATORY WIRELESS INDOOR LOCALIZATION

Abstract

Despite significant research progress in the past two decades, an indoor localization system that can be easily deployed on a large scale remains a challenge. The first challenge that hinders the large-scale deployment of indoor localization systems is the labor-intensive and time-consuming site surveys. The other challenge is the lack of efficient performance evaluation approach for indoor localization systems.

To tackle these challenges, we first design and implement PiLoc, a self-calibrating active indoor localization system, which infers the indoor floor plans and builds radio maps for localization automatically and greatly reduces the system calibration costs.

Next, to enable indoor localization without explicit cooperation of mobile devices, we then design and implement SpiLoc, a self-calibrating passive indoor localization system for mobile devices. SpiLoc achieves fine-grained localization performance while requiring no explicit cooperation of mobile devices.

Finally, we propose A2Loc, which achieves accuracy awareness for generic wireless fingerprint-based indoor localization systems.

To sum up, the three works proposed in this thesis exploit participatory sensing to achieve accuracy-aware self-calibrating indoor localization, which significantly reduce the calibration and evaluation costs for indoor localization systems.