Reducing ambiguity in indoor tracking using point of interest

Tracking of indoor wireless devices is gaining more attention from both academia and industry. Geography is different in every indoor map mainly after considering attenuation and space (3-D) factors. Most previous related research publications focus on signal attenuation but neglect other factors (e.g., reflections). Triangulation is a very popular method for device tracking but lacks precision. Graphic methods can provide more accurate results but translating their outcomes into machine-readable data can be challenging. In this paper, we survey most possible factors that affect indoor tracking and present a new deterministic formula to reduce ambiguity for reaching decisions. We propose the concept of Point of Interest that helps in scaling large-map analysis and in finding understudied locations; we present a formula that uses history data to build confidence. To test our formula, we built a test-bed and ran hundreds of experiments. Our results show large improvements in calculating distances between objects as well as making decisions on object locations.