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Article

Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices

by
Anargul Shaushenova
1,
Oleksandr Kuznetsov
2,3,*,
Ardak Nurpeisova
1,* and
Maral Ongarbayeva
4
1
Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical Research University, Astana 010000, Kazakhstan
2
Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
3
Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University, 4 Svobody Sq., 61022 Kharkiv, Ukraine
4
Department of Information and Communication Technologies, Faculty of Natural Sciences, International Taraz University Named After Sherkhan Murtaza, Taraz 080000, Kazakhstan
*
Authors to whom correspondence should be addressed.
Technologies 2025, 13(4), 155; https://doi.org/10.3390/technologies13040155
Submission received: 17 March 2025 / Revised: 30 March 2025 / Accepted: 11 April 2025 / Published: 12 April 2025
(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)
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Abstract

This research investigates the implementation of Kolmogorov–Arnold networks (KANs) for image processing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant advantages over traditional deep learning approaches, particularly in applications where computational resources are limited. Our study demonstrates the efficiency of KAN-based solutions for image analysis tasks in IoTs environments, providing comparative performance metrics against conventional convolutional neural networks. The experimental results indicate substantial improvements in processing speed and memory utilization while maintaining competitive accuracy. This work contributes to the advancement of AI-driven IoTs applications by proposing optimized KAN-based implementations suitable for edge computing scenarios. The findings have important implications for IoTs deployment in smart infrastructure, environmental monitoring, and industrial automation where efficient image processing is critical.
Keywords: Kolmogorov–Arnold networks; person detection; visual wake words; lightweight neural networks; TinyML; resource-constrained computing; computer vision; efficient inference; hybrid neural architectures Kolmogorov–Arnold networks; person detection; visual wake words; lightweight neural networks; TinyML; resource-constrained computing; computer vision; efficient inference; hybrid neural architectures

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MDPI and ACS Style

Shaushenova, A.; Kuznetsov, O.; Nurpeisova, A.; Ongarbayeva, M. Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices. Technologies 2025, 13, 155. https://doi.org/10.3390/technologies13040155

AMA Style

Shaushenova A, Kuznetsov O, Nurpeisova A, Ongarbayeva M. Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices. Technologies. 2025; 13(4):155. https://doi.org/10.3390/technologies13040155

Chicago/Turabian Style

Shaushenova, Anargul, Oleksandr Kuznetsov, Ardak Nurpeisova, and Maral Ongarbayeva. 2025. "Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices" Technologies 13, no. 4: 155. https://doi.org/10.3390/technologies13040155

APA Style

Shaushenova, A., Kuznetsov, O., Nurpeisova, A., & Ongarbayeva, M. (2025). Implementation of Kolmogorov–Arnold Networks for Efficient Image Processing in Resource-Constrained Internet of Things Devices. Technologies, 13(4), 155. https://doi.org/10.3390/technologies13040155

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