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

Masood Jan, Wafa Njima, Xun Zhang, Alexander Artemenko

arXiv:2610.29638v1cs.LGeess.SP

TL;DR

Cross-area environmental variation and target-data demands challenge VLC indoor localization. The paper applies transfer learning with data-efficient fine-tuning and TES-based source selection, reporting recovery of nearly full-data performance with reduced target data and strong results on an unseen area.

  • Problem

    Industrial localization faces cross-area robustness and data-collection challenges, with labeling comprehensive datasets potentially requiring days of downtime per area.

    Source: §I · p. 1

  • Method

    The framework fine-tunes source-area models using a subset of target-area data and uses TES to consolidate transfer performance across targets for source selection.

    Source: 2) Fine-Tuning · p. 7, 4) Transfer Evaluation Score (TES) · p. 7

  • Results

    40% of target-area data recovers 97% of full-data performance; TES-guided results include localization errors as low as 40.9 cm with success rates exceeding 86%, and 3.28 cm mean error on an unseen area.

    Source: §I · p. 2, §VII · p. 18

  • Takeaways & Limitations

    The framework reduces labeling requirements by 60% and uses TES to identify promising source models without exhaustive testing.

    Source: §VII · p. 18

    The paper leaves systematic comparison with advanced adaptation methods for future work.

    Source: E. Limitations’ Discussion and Future… · p. 18

Abstract

from arXiv · show

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. This paper presents a transfer learning (TL) framework for VLC-based indoor localization that enhances cross-area robustness while reducing data requirements. 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. We further introduce the Transfer Efficiency Score (TES), a composite metric that identifies the most effective source models for transfer without exhaustive evaluation. Guided by TES, the framework selects the best source model, yielding localization errors as low as 40.9 cm with success rates above 86%. Validation on an unseen area confirms its generalization capability, with a 3.28 cm mean error achieved using the TES-selected source model. 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.

I. INTRODUCTION

The paper addresses cross-area degradation and target-data costs in industrial indoor localization, focusing on VLC-specific adaptation and systematic source-model selection. It proposes VLC transfer learning, reporting strong performance with reduced target data and a TES-based selection approach.

I. INTRODUCTION

  • Indoor localization faces cross-area robustness, data-collection, and energy or infrastructure-cost challenges in industrial deployment.Data collection and labeling can require days of downtime per area.Source: §I · p. 1
  • VLC offers centimeter-level accuracy and EMI immunity, but ambient light, occlusion, and surface reflections can affect optical signal visibility.Its line-of-sight dependence makes adaptation to area-specific optical conditions important.Source: §I · pp. 1–2
  • Standard machine-learning methods can degrade when floorplans, materials, or illumination change, while VLC transfer learning must account for optical propagation differences.The paper identifies illumination geometry, line-of-sight dependence, and ambient-light interference as VLC-specific adaptation challenges.Source: §I · p. 2
  • The paper develops a VLC cross-area transfer-learning framework intended to adapt models to new layouts with minimal retraining.The work is presented as a proof of concept under realistic industrial deployment conditions.Source: §I · p. 2
  • Fine-tuning with 40% of target-area data recovers 97% of full-data performance and reduces data collection requirements by 60%.The paper also introduces TES to select source models without exhaustive testing and demonstrates the approach on an unseen facility layout.Source: §I · p. 2

A. FL for Indoor Localization

  • VLC federated-learning systems face LED line-of-sight limits, uneven client contributions, communication constraints, and generalization challenges on unfamiliar deployments.These limits motivate architectural approaches such as transfer learning for cross-area robustness.Source: A. FL for Indoor Localization · p. 3

B. CL for Indoor Localization

The discussion contrasts adaptation strategies and motivates transfer learning for VLC localization across areas. The system model evaluates source-to-target transfer, fine-tunes when target performance degrades, and uses TES to select a source for unseen-area validation.

B. CL for Indoor Localization

  • Continuous learning supports incremental updates but faces catastrophic forgetting, computational and memory costs, and demands for labeled data in VLC settings.VLC channel changes and real-time adaptation can intensify these constraints.Source: B. CL for Indoor Localization · p. 3

C. Alternative Adaptation Techniques for Indoor Localization

  • Domain-adversarial and meta-learning approaches can require complex training, extra optimization, and diverse environments or substantial target data.The paper identifies a systematic comparison with advanced adaptation techniques as future work.Source: C. Alternative Adaptation Techniques… · p. 4

D. TL for Indoor Localization

  • Transfer learning is positioned as a lightweight alternative, with prior VLC work addressing within-area lighting variation while this paper targets cross-area adaptation and source-model selection.The authors report that their earlier within-area study improved accuracy by up to 47% and reduced energy consumption and computational time by over 30% relative to conventional training approaches.Source: D. TL for Indoor Localization · p. 4

III. SYSTEM MODEL AND PROBLEM FORMULATION

  • The paper presents a DNN as its regression architecture for mapping received signal-strength measurements to spatial coordinates and introduces TES to guide source-model selection.The DNN is described as capturing nonlinear relationships in VLC measurements.Source: §III · p. 4

A. System Model

  • The system partitions a Bosch factory hall into four areas with differing layouts and equipment, rotating one as an unseen deployment area.This supports assessment across varied spatial contexts.Source: A. System Model · p. 5
  • The framework evaluates a source model on target areas, fine-tunes it when performance degrades, and assesses selected models on an unseen area.The system model uses areas A, B, and C for transfer assessment before validation on area D, rotating the unseen area across subareas.Source: A. System Model · p. 5

B. Problem Formulation

The problem formulation combines cross-area localization with source-model selection: train on one area, adapt using labeled target data, and use TES to guide transfer. The described source-training procedure fits comparable DNNs independently across areas and evaluates them on other areas.

B. Problem Formulation

  • The objective is a DNN regressor that achieves low localization error and high transferability after training on a source area and fine-tuning on a small labeled target subset.The set of source subareas is defined as A = {A, B, C}.Source: B. Problem Formulation · p. 5

1) Source Training & Direct Evaluation

  • Fine-tuning follows target-area evaluation when performance substantially degrades relative to source-area validation, using a small randomly sampled labeled target subset.The sampling ratio ρ is defined relative to the number of target samples.Source: B. Problem Formulation · p. 5

B. Problem Formulation

  • TES consolidates localization accuracy and success rate into a unified metric for selecting a reliable source area.The accompanying workflow covers TES assessment and validation.Source: B. Problem Formulation · p. 6, Figure 2 · p. 6

1) Source Training & Direct Evaluation

  • Each source area receives an independently trained DNN with identical architecture and hyperparameters to support comparison across source areas.The models are trained on the full source dataset using a regression loss, specified as MSE.Source: 1) Source Training & Direct Evaluation · p. 6
  • The source model parameters θ_s are learned weights and biases, and the DNN maps d-dimensional RSS vectors to predicted 2D coordinates.The described loss is MSE over the source dataset and labels.Source: 1) Source Training & Direct Evaluation · pp. 6–7
  • The pretrained source model is directly evaluated on each target area before any fine-tuning.This measures how well the model generalizes to a target area.Source: 1) Source Training & Direct Evaluation · p. 7

2) Fine-Tuning:

The method fine-tunes a source model when direct target-area evaluation shows substantial degradation, using a sampled subset of target data. TES combines normalized localization error and success rate to rank source models by transfer performance across target areas.

2) Fine-Tuning:

  • Fine-tuning uses 40% of target data in subsequent experiments, after testing sampling ratios from 10% to 100%.The fine-tuning subset is randomly sampled; only the final k DNN layers are updated while lower layers remain fixed.Source: 2) Fine-Tuning · p. 7

3) Transfer Directions:

  • The six ordered source–target transfer directions produce a fine-tuned model and corresponding error for each pair.Fine-tuning is triggered when direct target evaluation shows substantial degradation relative to source-area validation.Source: 3) Transfer Directions · p. 7, 2) Fine-Tuning · p. 7

4) Transfer Evaluation Score (TES):

  • TES combines normalized localization error and success rate, with α = 0.5 weighting the metrics equally in the experiments.Min–max values are computed independently for each transfer scenario; scores for each source are averaged across its target scenarios, and the highest-scoring source is selected.Source: 4) Transfer Evaluation Score (TES) · p. 7

5) Validation on Unseen Areas:

The selected TES source model is evaluated on a held-out area excluded from training and model selection. Rotating the unseen area across available areas tests the selection strategy’s consistency in a new-environment setting.

5) Validation on Unseen Areas:

  • The TES-selected source model is tested on a held-out subarea excluded from training and model selection.Localization error and success rate are computed on that area.Source: 5) Validation on Unseen Areas · p. 8
  • The unseen-area evaluation uses predicted coordinates, ground-truth locations, and a predefined success threshold δ.A prediction counts as successful when it falls within δ of the ground truth.Source: 5) Validation on Unseen Areas · p. 8
  • The unseen-area role rotates among all available subareas, with TES computed from the remaining areas in each iteration.This procedure is used to assess the robustness and consistency of TES-based model selection.Source: 5) Validation on Unseen Areas · p. 8

IV. ALGORITHM DEVELOPMENT AND IMPLEMENTATION

The framework couples cross-area model evaluation with TES-based source selection and unseen-area validation. It fine-tunes when direct transfer falls below a source-area benchmark, then selects a source based on aggregated transfer scores.

IV. ALGORITHM DEVELOPMENT AND IMPLEMENTATION

  • The framework has two components: cross-area model training and evaluation, and TES-based selection and validation of transfer configurations.It is designed to support cross-area generalization while minimizing computational overhead.Source: §IV · p. 8
  • Fine-tuning is triggered when target-area error exceeds the source baseline or target success rate falls below it.A randomly sampled fraction ρ of target data updates only the top k layers; lower layers remain frozen.Source: §IV · p. 8
  • The algorithm normalizes transfer errors and success rates, averages pairwise TES values by source, and selects the source with the highest aggregate score.Only the selected source model is then validated on the unseen-area dataset using localization error and success rate.Source: §IV · p. 9

V. EXPERIMENTAL SETUP AND DATA COLLECTION

The experimental setup uses measurements collected in a realistic industrial environment. The section describes the VLC transceiver, measurement infrastructure, and signal-strength data collection process.

V. EXPERIMENTAL SETUP AND DATA COLLECTION

  • The framework is based on measured data collected in a realistic industrial environment.These measurements provide a basis for model training and evaluation under practical deployment conditions.Source: §V · p. 9
  • The experimental setup includes a VLC transceiver design and measurement infrastructure deployed within the BOSCH facility.The passage does not specify further infrastructure details.Source: §V · p. 9
  • The data collection process gathers signal-strength readings for training and evaluation.The measurements are described as representative of practical deployment conditions.Source: §V · p. 9

1) Architecture:

The VLC transceiver combines power, transmitter, and receiver subsystems, using equalization on both sides to preserve signal quality. Its optical and detection specifications are designed for signal-strength measurements across industrial line-of-sight conditions.

1) Architecture:

  • A regulated 5 V supply and dedicated DC-DC converters provide the rails required by the transceiver subsystems, including photodetector biasing.The unit accepts 12 V at 1.2 A, for approximately 14.4 W total power consumption.Source: 1) Architecture · p. 9, 2) Specifications · p. 9
  • Pre-equalization compensates for LED bandwidth limits before transmission, while post-equalization corrects receiver-chain distortions after photodetection and amplification.The receiver path converts photodiode current to voltage with a transimpedance amplifier, then filters and amplifies the signal.Source: 1) Architecture · p. 9

2) Specifications:

  • The transmitter uses a 450 nm peak-wavelength LED with 200 mA signal amplitude and 500 mW emitted optical power, each with stated operating tolerances.The LED has a 60° full angular emission width at half maximum power.Source: 2) Specifications · p. 10
  • Four parallel photodiodes provide a total effective sensitive area of 600 mm² and 0.3 A/W photosensitivity at 450 nm.Their spectral sensitivity spans 380–780 nm.Source: 2) Specifications · p. 10
  • The stated transceiver specifications support high-fidelity signal-strength measurement across varying line-of-sight conditions for industrial localization.The unit connects through USB-C for power and uses an SMA interface at the transmitter output.Source: 2) Specifications · pp. 9–10

B. VLC Measurement System

The measurement campaign collected frequency-dependent VLC signal-strength fingerprints in a structurally varied Bosch factory area. Its evaluation examines cross-area transfer, target-data ratios, TES-based source selection, and validation on an unseen area.

B. VLC Measurement System

  • The VNA generates a swept sinusoid at Port 1 and records transmitted and received signals to obtain frequency-dependent attenuation fingerprints for localization.Converter boards combine signal and power on the Tx side and separate RF and DC components on the Rx return path.Source: B. VLC Measurement System · pp. 10–11

C. Data Collection

  • The campaign sampled a 46 m × 42 m × 11 m portion of the 74 m × 171 m × 11 m Bosch hall, containing a production line and storage area.The hall includes steel, concrete, glass, aluminum, and dense ceiling-level metallic infrastructure.Source: C. Data Collection · p. 11
  • Receivers were sampled on a 0.2 m × 0.2 m grid with Tx and Rx heights of 2.9 m and 1.1 m, respectively.Around 160 receiver positions were sampled per area; each position was recorded for 5 seconds and averaged to reduce short-term disturbances.Source: C. Data Collection · p. 11
  • Ten transmitter deployments span Areas A–C, while deployment point 7 is reserved as unseen Area D for assessing TES-framework generalization.The four areas were selected to reflect differences in layout, ceiling structures, lighting, reflective surfaces, and equipment density.Source: C. Data Collection · p. 11

VI. PERFORMANCE EVALUATION AND ANALYSIS

  • The evaluation covers cross-domain transfer, selection of a target-data ratio, TES-guided source-model selection, and validation on an unseen area.The supplied evaluation passage states these analysis aims but provides no outcome values.Source: §VI · pp. 11–12

A. Impact of TL on Cross-Domain Localization

Transfer learning restores cross-area VLC localization performance, while fine-tuning with 40% of target data offers a practical performance–data tradeoff. The reported 40% guideline is empirical for this industrial dataset, and success-rate comparisons remain consistent across tested distance thresholds.

A. Impact of TL on Cross-Domain Localization

  • 40.90 cm on Area B and 51.55 cm on Area C were the fine-tuned Area A model’s localization errors, versus 134.91 cm and 101.08 cm without adaptation.The Area A model’s own validation error was 57.73 cm; after fine-tuning on Area C, its success rate was 98.48%, above its original 80.00% on Area A.Source: A. Impact of TL on Cross-Domain… · p. 12, Table IV · p. 14
  • Success rates rose monotonically as the distance threshold increased from 0.5 m to 2.0 m, while relative transfer-pair trends stayed consistent.This supports using success rate as a comparative indicator; the study adopts δ = 1 m as its benchmark.Source: Table V · p. 14, A. Impact of TL on Cross-Domain… · p. 14

B. Fine-Tuning Data Ratio Justification

  • 40% fine-tuning data yielded 40.90 cm error and 86.02% success for A→B, and 51.55 cm error and 98.48% success for A→C.For A→B, error and success rate were 47.34 cm and 75.63% at 10%, and 44.43 cm and 81.36% at 20%; gains flattened beyond 40%.Source: Figure 8 · p. 14, B. Fine-Tuning Data Ratio Justification · p. 14, Table VI · p. 15
  • 40% was selected as the standard fine-tuning ratio after comparisons at 10%, 20%, 40%, 60%, and 100%, balancing performance with data-collection effort.The comparison covered all six source–target combinations; Fig. 8 shows representative A→B and A→C scenarios.Source: B. Fine-Tuning Data Ratio Justification · p. 14
  • The 40% recovery finding is empirical for this industrial dataset; the needed calibration ratio may vary with environmental complexity, occlusion, and noise.The authors describe it as a guideline, not a universal requirement.Source: B. Fine-Tuning Data Ratio Justification · p. 14

C. TES-Driven Selection of the Optimal TL Source Model and Validation on an Unseen Area

The TES framework ranks source models for transfer using post-fine-tuning performance, and Area A achieved the highest mean TES in this study. In unseen-area validation, the TES-selected model from Area A outperformed the other tested models, while TES rankings were stable under moderate weighting changes and tracked feature-space similarity.

C. TES-Driven Selection of the Optimal TL Source Model and Validation on an Unseen Area

  • Source-area rankings remained largely stable across α values from 0.3 to 0.7, with minor shifts at the extremes and consistent identification under moderate changes.Low α slightly favored B; higher α gave A a clearer advantage, supporting α = 0.5 as a balanced operating point.Source: Table VIII · p. 15, C. TES-Driven Selection of the Optimal… · pp. 15–16
  • 0.7132 was Area A’s mean TES, the highest among source models, after averaging TES across its outgoing transfer paths.The source model with the highest aggregated TES is identified as the most transferable in this study.Source: C. TES-Driven Selection of the Optimal… · p. 15
  • Area pairs with lower Bhattacharyya distance consistently had higher bidirectional average TES, linking transfer effectiveness with RSS feature-distribution similarity.The distance accounts for differences in feature means and covariance structure.Source: Table IX · p. 16, C. TES-Driven Selection of the Optimal… · p. 16
  • 3.28 cm mean localization error and 100% success were achieved on unseen Area D by the TES-TL model from Area A, outperforming all baseline and other-area TL models.Area A and D share structural characteristics and moderate equipment density, with comparable propagation patterns, limited occlusion, and moderate multipath.Source: Validation on an Unseen Area · p. 16
  • Across rotated unseen-area evaluations of Areas A, B, C, and D, TES consistently selected the source with the closest structural and environmental characteristics.The Area C model captured multipath and temporal variability but transferred less effectively to Area D’s structurally simpler layout.Source: Validation on an Unseen Area · p. 16

D. Motivation and Impact of Fine-Tuning Strategy

The fine-tuning strategy’s effectiveness depends on target-data availability: partial fine-tuning is more stable with little data, while full fine-tuning performs better with more. The paper reports strong transfer results alongside limitations in evaluation scope and adaptation choices.

D. Motivation and Impact of Fine-Tuning Strategy

  • With 10%–40% target data, partial fine-tuning is more stable; with 60%–100%, full fine-tuning is more effective.Partial fine-tuning freezes lower layers to preserve source representations, while full fine-tuning lets all layers adapt; full fine-tuning in low-data settings can increase overfitting and instability.Source: D. Motivation and Impact of… · pp. 16–17
  • At 100% target data, full fine-tuning lowers B→A error from 63.90 cm to 57.38 cm and C→A error from 61.74 cm to 57.45 cm.The comparisons concern representative transfers to Area A; Fig. 10 compares partial and full fine-tuning across target-data ratios.Source: D. Motivation and Impact of… · p. 17, Figure 10 · p. 17

E. Limitations’ Discussion and Future Work

  • Evaluation covered one industrial facility, so validation across additional facilities remains outstanding despite testing diverse zones.The paper attributes the single-facility scope to safety, scheduling, and confidentiality constraints.Source: E. Limitations’ Discussion and Future… · p. 17
  • Fixed transmitter and receiver heights leave mobile-robot and handheld scenarios unresolved, where height variation may distort RSS patterns and affect accuracy.Proposed future approaches include height-perturbed training data, height as an input, or joint position-and-height estimation.Source: E. Limitations’ Discussion and Future… · pp. 17–18
  • The framework uses supervised transfer with a limited labeled target subset; semi-supervised adaptation using unlabeled measurements remains future work.The paper identifies pseudo-labeling, consistency regularization, and representation-preserving adaptation as possible extensions.Source: E. Limitations’ Discussion and Future… · p. 18
  • TES evaluation uses labeled data across source–target pairs, so its computational cost may grow as deployment areas expand.The paper frames TES as an offline commissioning metric and suggests estimating it with unsupervised domain-similarity indicators as future work.Source: E. Limitations’ Discussion and Future… · p. 18
  • The current framework transfers from one source at a time and lacks a systematic comparison with advanced adaptation methods.Multi-source strategies may capture complementary knowledge but bring model-selection complexity and potential negative-transfer interactions; comparisons with advanced techniques are future work.Source: E. Limitations’ Discussion and Future… · p. 18

VII. CONCLUSION

  • 40.9 cm localization error with success rates above 86%, and 3.28 cm mean error on an unseen area, are reported for the TES-guided framework.The conclusion also reports that the framework retains nearly full performance while reducing labeling requirements by 60%.Source: §VII · p. 18
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