Cross-Area Transfer Learning for VLC-based Indoor Localization with a Transfer Efficiency Score

Key points

• Visible Light Communication (VLC)-based systems offer advantages such as immunity to electromagnetic interference, high accuracy, and energy efficiency, but their performance is hindered by environmental variability across deployment areas.1
• This paper presents a transfer learning (TL) framework for VLC-based indoor localization that enhances cross-area robustness while reducing data requirements.1
• Using real-world measurements from four distinct areas of a Bosch manufacturing facility, we show that fine-tuning a deep neural network (DNN) pre-trained on source area data with only 40% of target-area measurements recovers 97% of full-data performance, thereby reducing the amount of target data needed.1
• Overall, the proposed framework provides a scalable and data-efficient pathway for industrial VLC localization, substantially reducing deployment costs and computational burden while maintaining high accuracy in diverse and dynamic environments.1

Sources (1)

• [1]Cross-Area Transfer Learning for VLC-based Indoor Localization with a Transfer Efficiency Score
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:18 PM

  Indoor localization systems are critical for Industry-4.0 applications.

  Visible Light Communication (VLC)-based systems offer advantages such as immunity to electromagnetic interference, high accuracy, and energy efficiency, but their performance is hindered by environmental variability across deployment areas.

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