Clustering Wi-Fi Fingerprints for Indoor-Outdoor Detection

TL;DR

This paper introduces a Wi-Fi fingerprint-based method for continuous indoor-outdoor detection on mobile devices, leveraging machine learning to achieve high accuracy without prior environment knowledge.

Contribution

It proposes a novel, environment-agnostic approach using minimal training data and machine learning for indoor-outdoor detection based solely on Wi-Fi fingerprints.

Findings

Achieved an AUC of 0.94 with gradient boosting.

Effective in unknown environments and with new devices.

Applicable to other context detection tasks like building or room recognition.

Abstract

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.