Abstract
People spend most of their time in a few significant places and often indoors in a small number of select rooms and locations. Indoor localization in terms of a user’s current place, related to a user’s daily life, routines or activities, is an important context. We implemented an automatic approach DCCLA (Density-based Clustering Combined Localization Algorithm) to automatically learn the Wi-Fi fingerprints of the significant places based on density-based clustering. In order to accommodate the influence of the signal variation, clustering procedure separately works on a list of RSSIs (Received Signal Strength Indicators) from each AP (Access Point). In this paper, the approach is experimentally investigated in a laboratory setup and a real-world scenario in an office area with adjacent rooms, which is a key challenge to distinguish for place learning and recognition approaches. From these experiments, we compare and identify the most suitable parameters for the unsupervised learning.
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Xu, Y., Lau, S.L., Kusber, R., David, K. (2013). DCCLA: Automatic Indoor Localization Using Unsupervised Wi-Fi Fingerprinting. In: Brézillon, P., Blackburn, P., Dapoigny, R. (eds) Modeling and Using Context. CONTEXT 2013. Lecture Notes in Computer Science(), vol 8175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40972-1_6
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DOI: https://doi.org/10.1007/978-3-642-40972-1_6
Publisher Name: Springer, Berlin, Heidelberg
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