Abstract
The numerous possible applications for Bluetooth Low Energy (BLE) Beacons constantly motivate researchers to come up with new methods to optimize the utilizations of beacons and improve the positioning accuracy while maintaining the cost to benefit balance. In this study, a novel method was proposed to optimize the localization of an indoor BLE positioning system with low beacon density in a real-world environment. The proposed method combines three major machine learning concepts (Semi-Supervised Learning, Informed Machine Learning, and Soft Computing) in its analysis pipeline and presents a different perspective to approaching Genetic Fuzzy Systems (GFSs). The presented method was competitive when benchmarked against a fully connected neural network and a kNN model.
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Manasreh, D., Swaleh, S., Cohen, K., Nazzal, M. (2023). Semi-supervised Physics-Informed Genetic Fuzzy System for IoT BLE Localization. In: Dick, S., Kreinovich, V., Lingras, P. (eds) Applications of Fuzzy Techniques. NAFIPS 2022. Lecture Notes in Networks and Systems, vol 500. Springer, Cham. https://doi.org/10.1007/978-3-031-16038-7_15
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