Let's forget about exact signal strength: Indoor positioning based on access point ranking and recurrent neural networks

Positioning is a key task in many different contexts. In the last decades, it has considerably evolved, but, while there are a lot of systems that offer a quite good performance in outdoor scenarios, the indoor realm is still under exploration. Among existing technologies and techniques for indoor positioning, the most popular one makes use of WiFi fingerprints. Such an approach has many advantages; however, its adoption as a standard for everyday life is limited due to issues like the (time) costly radio map construction, and radio signal strength fluctuations in indoor environments. In this paper, we present a novel solution for indoor positioning based on deep learning, that ignores as much as possible signal strengths, in order to reduce the adverse effects associated with their usage. It exploits signal strength only to generate a ranking-based representation of the access points associated with a fingerprint. By developing and testing two recurrent neural network models, we show that the proposed approach is able to achieve a positioning performance, based on access point ranking, comparable to the one achieved by state-of-the-art algorithms on multiple publicly available indoor datasets. As additional benefits, compared to existing ones, the developed solution is considerably more robust to signal fluctuations and simpler in terms of the considered data.