Citation

Rizwan, Muhammad and Ng, Yin Hoe and Wong, Hin Yong and Tan, Chee Keong (2025) Knowledge Distillation-Driven 3D Convolutional Neural Networks for Efficient and Robust Indoor Localization. IEEE Access, 13. pp. 157764-157779. ISSN 2169-3536

Text Knowledge Distillation-Driven 3D Convolutional Neural Networks for Efficient and Robust Indoor Localization.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Deep learning models have gained prominence in AI research due to their exceptional feature extraction capabilities. In fingerprint-based indoor localization, 1D and 2D convolutional neural networks (CNNs) are frequently utilized for their superior performance and minimal hardware requirements. However, these models primarily extract spatial features and often exhibit degraded real-time performance due to signal inconsistencies caused by shadowing, fading, and multipath effects. To address this, incorporating temporal variations through 3D CNNs can enhance outcomes, albeit with increased computational costs. This study proposes a knowledge distillation (KD) approach using 3D CNNs for indoor localization, to bridge the computational and performance gaps. The proposed KD framework employs a complex teacher model (TM) to train two simpler student models A (SM-A) and B (SM-B). We employ both response-based and feature-based KD methods to optimize the student models’ weights. The proposed techniques are evaluated using the UJILIB dataset collected from two floors of the University of Jaume I library. To enhance model performance, data augmentation is applied, yielding substantial improvements in test accuracy of 10.56% for TM, 16.19% for SM-A, and 16.93% for SM-B. The application of KD results in accuracy improvements of 5.45% for SM-A and 3.35% for SM-B, consequently reducing their average positioning errors (APEs) from 1.070 m and 1.030 m to 0.840 m and 0.944 m, respectively. Compared to KD-based SM-A, TM shows only 3.30% higher positioning accuracy, but with a 65.14% increase in computational complexity. Notably, the KD-enhanced SM-A also achieves a significantly lower APE of 0.84 m compared to the 2D CNN of 1.72 m

Item Type:	Article
Uncontrolled Keywords:	Fingerprint
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology > HV6001-7220.5 Criminology > HV6035-6197 Criminal anthropology Including criminal types, criminal psychology, prison psychology, causes of crime
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 06:41
Last Modified:	05 Oct 2025 09:49
URII:	http://shdl.mmu.edu.my/id/eprint/14581

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