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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Veröffentlicht in:2023 60th ACM/IEEE Design Automation Conference (DAC) S. 1 - 6
Hauptverfasser: Gufran, Danish, Tiku, Saideep, Pasricha, Sudeep
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 09.07.2023
Schlagworte:
Buildings
Device Heterogeneity
Fingerprinting
Indoor localization
Location awareness
Neural networks
Transceivers
Transformers
Uncertainty
Vision Transformer
Wireless communication
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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.
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
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  givenname: Danish
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  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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StartPage 1
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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