Wireless indoor localization systems and especially signal strength fingerprinting techniques have been the subject of significant research efforts in the last decades. However, most of the proposed solutions require a costly site-survey to build the radio map which can be used to match radio signatures with specific locations. We investigate a novel indoor localization system that addresses the data collection problem by progressively and semi-autonomously creating a radio-map with limited interaction cost. Moreover, we investigate how spatiotemporal and hardware properties-based variations can affect the RSSI values collected and significantly influence the resulting localization. We show the impact of these fluctuations on our system and discuss possible mitigations.
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