Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

WLAN Location Sharing through a Privacy Observant Architecture

In the last few years, WLAN has seen immense growth and it will continue this trend due to the fact that it provides convenient connectivity as well as high speed links. Furthermore, the infrastructure already exists in most public places and is cheap to extend. These advantages, together with the fact that WLAN covers a large area and is not restricted to line of sight, have led to developing many WLAN localization techniques and applications based on them. In this paper we present a novel calibration-free localization technique using the existing WLAN infrastructure that enables conference participants to determine their location without the need of a centralized system. The evaluation results illustrate the superiority of our technique compared to existing methods. In addition, we present a privacy observant architecture to share location information. We handle both the location of people and the resources in the infrastructure as services, which can be easily discovered and used. An important design issue for us was to avoid tracking people and giving the users control over who they share their location information with and under which conditions