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Precise indoor localization using a lightweight 2D-CNN with adaptive temperature guided iterative self-knowledge

Muhammad Rizwan1, Yin Hoe Ng2, Hin-Yong Wong1

  • 1Faculty of Artificial Intelligence and Engineering, Multimedia University, 63100, Cyberjaya, Malaysia.

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|November 7, 2025
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Summary
This summary is machine-generated.

This study introduces a new lightweight Convolutional Neural Network (CNN) for accurate indoor localization using Wi-Fi and Bluetooth signals. The method improves positioning accuracy by over 8% and reduces errors to 2.24m, making it ideal for devices with limited resources.

Keywords:
3D convolutional neural networkComputational complexityIndoor localizationKnowledge distillationReceiver signal strength indicator

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Fingerprint-based indoor localization using Wi-Fi and Bluetooth RSS is crucial but faces accuracy challenges under deployment constraints.
  • Deep Convolutional Neural Networks (CNNs) offer enhanced accuracy but are too complex for resource-limited devices.
  • Knowledge Distillation (KD) enables transferring knowledge from complex to simpler models, but typically requires separate teacher-student architectures.

Purpose of the Study:

  • To propose a novel lightweight 2D CNN architecture for infrastructure-less indoor localization.
  • To integrate Squeeze-and-Excitation (SE) modules and adaptive temperature guided iterative Self-Knowledge Distillation (SKD) for improved accuracy and efficiency.
  • To enable practical, real-time indoor localization on resource-constrained platforms.

Main Methods:

  • Developed a lightweight 2D CNN incorporating SE modules for dynamic feature recalibration.
  • Implemented an iterative SKD strategy within a single model, eliminating the need for a separate teacher model.
  • Evaluated the proposed architecture on the HDLC public datasets for indoor positioning accuracy.

Main Results:

  • The CNN architecture without SKD improved positioning accuracy by 8.32% over conventional CNNs, achieving a 3D Average Positioning Error (APE) of 2.60m.
  • Integration of iterative SKD further enhanced positioning accuracy by 1.66%, reducing the 3D APE to 2.24m.
  • The proposed method demonstrates significant efficiency and accuracy gains for indoor localization.

Conclusions:

  • The novel lightweight CNN with SE modules and iterative SKD provides a resource-efficient and practical solution for accurate indoor localization.
  • The single-model KD approach significantly reduces computational overhead compared to traditional KD methods.
  • The framework facilitates real-time indoor positioning applications on devices with limited computational power.