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              <p>This article presents a comparative study of a simple localisation architecture and a hierarchical architecture. The studied solutionsaim to localize a user in an indoor setting using RSS (Received Signal Strength) fingerprints of BLE (Bluetooth Low Energy) by using machinelearning techniques. Two algorithms were implemented for each of the strategies, the K nearest neighbour algorithm and a multi-layer perceptron. Experimentation was carried out in an indoor environment showcasing the feasibility of the evaluated strategies as well as their similarperformance regarding localization accuracy wich stays within a 2-meter range on average.</p>
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              <p>-Cet article présente une étude comparative entre une architecture de localisation simple et une architecture hiérarchisée. Les solutions étudiées ont pour objectif une localisation intérieure basée sur l'empreinte RSS (Received Signal Strength) de la technologie BLE (Bluetooth Low Energy) en utilisant l'apprentissage automatique. Deux algorithmes ont été implémentés pour chaque stratégie, il s'agit du K plus proches voisins et le perceptron multi couches. Des expériences ont été menées dans un environnement intérieur montrant la faisabilité des stratégies étudiées et leurs performances similaires en termes de précision de localisation ne dépassant pas les 2 mètres d'erreur en moyenne.</p>
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