Wi-Fi fingerprinting through active learning using smartphones
Citations Over TimeTop 10% of 2013 papers
Abstract
Indoor positioning is one of the key components enabling retail-related services such as location-based product recommendations or in-store navigation. In the recent years, active research has shown that indoor positioning systems based on Wi-Fi fingerprints can achieve a high positioning accuracy. However, the main barrier of broad adoption is the labor-intensive process of collecting labeled fingerprints. In this work, we propose an approach for reducing the amount of labeled data instances required for training a Wi-Fi fingerprint model. The reduction of the labeling effort is achieved by leveraging dead reckoning and an active learning-based approach for selecting data instances for labeling. We demonstrate through experiments that we can construct a Wi-Fi fingerprint database with significantly less labels while achieving a high positioning accuracy.
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