Wi-Fi Fingerprint Based Indoor Localization without Indoor Space Measurement

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service. Fingerprint-based approach is one of most popular and inexpensive solution. In terms of constructing the fingerprint database, there have to be a synchronized measurement for both indoor space(e.g. by labor-intensive site-survey or sensor-based crowdsensing) and fingerprint space, by this means the fingerprints database is established. It is the indoor space measurement hinders the usability of fingerprint-based localization system. In this work, we propose a sensor-free crowdsensing indoor localization scheme, SENIL. The main contribution of our protocol is that we don't need indoor space measurement. Floor plan and RSS samples temporal sequence is the only requirement. The core of our method is a graph matching based manifold alignment process, which automatically finds the best correspondence between floor plan and wireless fingerprint transition structure. With no more need of indoor space measurement, the system deployment complexity and cost are significantly reduced. We implement SENIL at AP-end and deploy it in a 2000m(2) office environment. The evaluation has shown that SENIL can handle complex environment mapping and achieve high localization & tracking accuracy.