Nasir, M.; Esguerra, K.; Faye, I.; Tang, T.B.; Yahya, M.; Tumian, A.; Ho, E.T.W. HyTra: Hyperclass Transformer for WiFi Fingerprinting-Based Indoor Localization. Transactions on Energy Systems and Engineering Applications 2024, 5, 1–24, doi:10.32397/tesea.vol5.n1.542.
Nasir, M.; Esguerra, K.; Faye, I.; Tang, T.B.; Yahya, M.; Tumian, A.; Ho, E.T.W. HyTra: Hyperclass Transformer for WiFi Fingerprinting-Based Indoor Localization. Transactions on Energy Systems and Engineering Applications 2024, 5, 1–24, doi:10.32397/tesea.vol5.n1.542.
Nasir, M.; Esguerra, K.; Faye, I.; Tang, T.B.; Yahya, M.; Tumian, A.; Ho, E.T.W. HyTra: Hyperclass Transformer for WiFi Fingerprinting-Based Indoor Localization. Transactions on Energy Systems and Engineering Applications 2024, 5, 1–24, doi:10.32397/tesea.vol5.n1.542.
Nasir, M.; Esguerra, K.; Faye, I.; Tang, T.B.; Yahya, M.; Tumian, A.; Ho, E.T.W. HyTra: Hyperclass Transformer for WiFi Fingerprinting-Based Indoor Localization. Transactions on Energy Systems and Engineering Applications 2024, 5, 1–24, doi:10.32397/tesea.vol5.n1.542.
Abstract
Indoor localization is an active area of research dominated by traditional machine-learning techniques. Deep learning-based systems have shown unprecedented improvements and have accomplished exceptional results over the past decade, especially the Transformer network within natural language processing (NLP) and computer vision domains. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer with multiple classification heads (one per class) and learnable embeddings, to investigate the effectiveness of Transformer-based models for received signal strength (RSS) based WiFi fingerprinting. HyTra leverages learnable embeddings and the self-attention mechanism to determine the relative position of the wireless access points (WAPs) within the high-dimensional embedding space, improving the prediction of user location. From an NLP perspective, we consider a fixed order sequence of all observed WAPs as a sentence and the captured RSS value(s) for every given WAP at a given reference point from a given user as words. We test our proposed network on public and private datasets of different sizes, proving that the quality of the learned embeddings and overall accuracy improves with increments in samples.
Keywords
Deep Learning; Transformer; WiFi Fingerprinting; Indoor Localization; Classification; Received Signal Strength
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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