Interactionless calendar-based training for 802.11 localization

This paper presents our work in solving one of the weakest links in 802.11-based indoor-localization: the training of ground-truth received signal strength data. While crowdsourcing this information has been demonstrated to be a viable alternative to the time consuming and accuracy-limited process of manual training, one of the chief drawbacks is the rate at which a system can be trained. We demonstrate an approach that utilizes users' calendar and appointment information to perform interactionless training of an 802.11-based indoor localization system. Our system automatically determines if a user attended a calendar event, resulting in accuracy comparable to our previously published large-scale crowdsourced deployment. We find that no other user interaction is necessary to train the system to that level of accuracy when calendar data are available. In ideal conditions, this technique can reduce training time by over a factor of six.