Seamless indoor/outdoor location cognition with confidence in wireless systems

This paper proposes a method of real-time seamless location cognition in WLAN systems based on monitoring, learning and recognizing the statistics of received data traffic. It uses the property that statistics such as average and variance of throughput are often correlated with the location. The method also uses a confidence parameter to quantify the confidence in the location identification, based on comparing matches with multiple candidates. The proposed method can support seamless location cognition by WLAN terminals, indoor and outdoor, without depending on any infrastructure service.