基于迁移学习的 VLC 室内定位方法：精度提升 47% 并降低能耗
A Transfer Learning-Driven Methodology for Efficient and Sustainable Indoor Localization Using VLC
博世工厂真实数据验证的 VLC 室内定位研究，用迁移学习把精度提高 47%，高噪声下模型效率从 10.4% 拉到 67.5%，做工业物联网定位的可以看看。
一篇 arXiv 论文提出用迁移学习（TL）改进可见光通信（VLC）室内定位，应对光照波动和物理障碍等环境变化。方法相比不使用 TL 的传统模型，定位精度提升 47%，并降低了计算开销与能耗。作者还提出模型效率（ME）指标，把定位精度、能效、数据效率和迁移增益合并为单一评估标准。在 BOSCH 工厂采集的真实数据集上，高噪声条件下 TL 模型的 ME 达到 67.5%，传统模型仅 10.4%。
A Transfer Learning-Driven Methodology for Efficient and Sustainable Indoor Localization Using VLC
The rapid expansion of the Internet of Things enables seamless collaboration among connected devices, making indoor localization a critical component of industrial automation. However, conventional localization techniques struggle in dynamic environments. These methods depend on extensive, frequent data collection and environment-specific calibration, limiting scalability, interoperability, and effective use of prior research while imposing significant energy penalties that hinder practical deployment. To address these challenges, we propose an energy-efficient transfer learning (TL) based approach for visible light communication based indoor localization. Our method leverages TL to tackle environmental variability, including lighting fluctuations and physical obstacles that typically degrade localization performance, while simultaneously reducing computational overhead and energy consumption compared to conventional approaches. We also introduce a novel model efficiency (ME) metric, designed to integrate localization accuracy, energy efficiency, data efficiency, and transfer gain into a single evaluative measure for comprehensive optimization. Evaluations on a real-world dataset collected from a BOSCH factory demonstrate that our model achieves a 47% improvement in localization accuracy compared to a conventional model that does not utilize TL, and achieves up to 67.5% ME with TL models, compared to only 10.4% in conventional models under high-noise conditions. These results underscore the potential of our approach to deliver a highly efficient and scalable indoor localization system suitable for energy-constrained industrial applications.
