  This paper presents a method for continuous indoor-outdoor environment
detection on mobile devices based solely on WiFi fingerprints. Detection of
indoor outdoor switching is an important part of identifying a user's context,
and it provides important information for upper layer context aware mobile
applications such as recommender systems, navigation tools, etc. Moreover,
future indoor positioning systems are likely to use Wi-Fi fingerprints, and
therefore Wi-Fi receivers will be on most of the time. In contrast to existing
research, we believe that these fingerprints should be leveraged, and they
serve as the basis of the proposed method. Using various machine learning
algorithms, we train a supervised classifier based on features extracted from
the raw fingerprints, clusters, and cluster transition graph. The contribution
of each of the features to the method is assessed. Our method assumes no prior
knowledge of the environment, and a training set consisting of the data
collected for just a few hours on a single device is sufficient in order to
provide indoor-outdoor classification, even in an unknown location or when
using new devices. We evaluate our method in an experiment involving 12
participants during their daily routine, with a total of 828 hours' worth of
data collected by the participants. We report a predictive performance of the
AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble
learning method. We show that our method can be used for other context
detection tasks such as learning and recognizing a given building or room.
