VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization
Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones. Unfortunately, the heterogeneity of wireless transceivers across diverse smartphones carried by users has been...
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| Vydáno v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
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IEEE
09.07.2023
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| Abstract | Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones. Unfortunately, the heterogeneity of wireless transceivers across diverse smartphones carried by users has been shown to reduce the accuracy and reliability of localization algorithms. In this paper, we propose a novel framework based on vision transformer neural networks called VITAL that addresses this important challenge. Experiments indicate that VITAL can reduce the uncertainty created by smartphone heterogeneity while improving localization accuracy from 41% to 68% over the best-known prior works. We also demonstrate the generalizability of our approach and propose a data augmentation technique that can be integrated into most deep learning-based localization frameworks to improve accuracy. |
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| AbstractList | Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones. Unfortunately, the heterogeneity of wireless transceivers across diverse smartphones carried by users has been shown to reduce the accuracy and reliability of localization algorithms. In this paper, we propose a novel framework based on vision transformer neural networks called VITAL that addresses this important challenge. Experiments indicate that VITAL can reduce the uncertainty created by smartphone heterogeneity while improving localization accuracy from 41% to 68% over the best-known prior works. We also demonstrate the generalizability of our approach and propose a data augmentation technique that can be integrated into most deep learning-based localization frameworks to improve accuracy. |
| Author | Tiku, Saideep Gufran, Danish Pasricha, Sudeep |
| Author_xml | – sequence: 1 givenname: Danish surname: Gufran fullname: Gufran, Danish email: danish.gufran@colostate.edu organization: Colorado State University,Department of Electrical and Computer Engineering,Fort Collins,CO,United States – sequence: 2 givenname: Saideep surname: Tiku fullname: Tiku, Saideep email: saideep@colostate.edu organization: Colorado State University,Department of Electrical and Computer Engineering,Fort Collins,CO,United States – sequence: 3 givenname: Sudeep surname: Pasricha fullname: Pasricha, Sudeep email: sudeep@colostate.edu organization: Colorado State University,Department of Electrical and Computer Engineering,Fort Collins,CO,United States |
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| Snippet | Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to... |
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| SubjectTerms | Buildings Device Heterogeneity Fingerprinting Indoor localization Location awareness Neural networks Transceivers Transformers Uncertainty Vision Transformer Wireless communication |
| Title | VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization |
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