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Open AccessArticle

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

1
Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany
2
Advanced Community Information Systems Group (ACIS), RWTH Aachen University, Lehrstuhl Informatik 5, Ahornstr. 55, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1443; https://doi.org/10.3390/s20051443
Received: 15 January 2020 / Revised: 2 March 2020 / Accepted: 3 March 2020 / Published: 6 March 2020
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
The accuracy of fingerprinting-based indoor localization correlates with the quality and up-to-dateness of collected training data. Perpetual crowdsourced data collection reduces manual labeling effort and provides a fresh data base. However, the decentralized collection comes with the cost of heterogeneous data that causes performance degradation. In settings with imperfect data, area localization can provide higher positioning guarantees than exact position estimation. Existing area localization solutions employ a static segmentation into areas that is independent of the available training data. This approach is not applicable for crowdsoucred data collection, which features an unbalanced spatial training data distribution that evolves over time. A segmentation is required that utilizes the existing training data distribution and adapts once new data is accumulated. We propose an algorithm for data-aware floor plan segmentation and a selection metric that balances expressiveness (information gain) and performance (correctly classified examples) of area classifiers. We utilize supervised machine learning, in particular, deep learning, to train the area classifiers. We demonstrate how to regularly provide an area localization model that adapts its prediction space to the accumulating training data. The resulting models are shown to provide higher reliability compared to models that pinpoint the exact position. View Full-Text
Keywords: indoor localization; area localization; crowdsourcing; fingerprinting; deep learning indoor localization; area localization; crowdsourcing; fingerprinting; deep learning
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Laska, M.; Blankenbach, J.; Klamma, R. Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection. Sensors 2020, 20, 1443.

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