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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