Deviation maps for robust and informed indoor positioning services.

The ability to position and track people and assets has become increasingly widespread and important in business and personal life. The prevalent means for such tasks is signal-strength-based, prominently WiFi-based, positioning, together with GNSS positioning. The latter, however, is insufficient for the majority of indoor environments in which most of our work and personal lives takes place. Signal-strength-based positioning, though, too, is error-prone in real-life building environments, suffering from large biases induced by the often many and complex attenuating elements in the environment. Additionally, in the prevalent signal-strength-based positioning methods, which rely solely on signal pattern matching, such biases and errors are hard to assess and thus positioning quality and glitches hard to predict. We present an approach for assessing, visualizing, and counter-acting positioning biases and impairments in signal-strength-based positioning. This approach, centered around the notion of deviation maps, aim at improving positioning quality and predictability/reliability and, at the same time, at gaining knowledge and understanding of tracking quality. We seek to understand how the tracking quality is influenced by both positioning installation and building environment, and how the former may be altered to better suit the latter. We discuss results from applying our approach in a real-world large-scale work environment, a major hospital spanning 160,000 square meters, as well as lessons learned from the underlying experimentation-driven and use-centric design process. From these lessons we also derive directions for future work.