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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) International Journal of Evaluation and Research in Education (IJERE) Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies
Moldamurat, Khuralay
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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AI-Enhanced High-Speed Data Encryption System for Unmanned Aerial Vehicles in Fire Detection Applications Moldamurat, Khuralay; Spada, Luigi La; Zeeshan, Nida; Bakyt, Makhabbat; Kuanysh, Absalyam; Zhanibek, Kazybek bi; Tilenbayev, Alzhan
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26275

Abstract

Small unmanned aerial vehicles (UAVs) are increasingly used for wildfire detection, where they must not only identify fire events rapidly but also transmit large volumes of sensor data securely to ground stations. Achieving both fast on-board analysis and high-speed encrypted data transmission within the size, weight, and power limits of UAV platforms remain a major technical challenge. In this study, we introduce a compact, FPGA-based system that simultaneously performs real-time fire detection and high-throughput data encryption. Our system integrates a programmable logic chip (FPGA), deep-learning models for visual recognition, and AES-256 cryptographic cores onto a single hardware module. A key innovation is a shared scheduling mechanism that coordinates these two functions efficiently. Furthermore, we demonstrate how artificial intelligence contributes beyond image classification: a lightweight neural network monitors input data streams and dynamically adjusts encryption key parameters, thereby improving security without compromising performance. The hardware supports encrypted data transfer rates of 800 megabits per second at a latency of just 2 microseconds, while identifying fire signatures at 30 frames per second. Extensive testing, including cross-validation on a 50,000-frame dataset and environmental stress testing from –20 °C to 55 °C, confirms robust performance under real-world conditions. While the current memory footprint limits multi-camera input, this work offers a foundational design for future systems that aim to combine edge computing, secure communications, and AI-driven perception in autonomous aerial platforms.
Co-Authors Akhmetov, Kairat Atanov, Sabyrzhan Bakyt, Makhabbat Belgibekov, Niyaz Galymzhan, Anuar Kalmanova, Dinara Kuanysh, Absalyam Kyzyrkanov, Abzal Maidanov, Adil Moldamurat, Aibek Nagymzhanova, Karakat Peryakina, Lyudmila Ryspaev, Askhat Sapabekov, Abylaikhan Seitbattalov, Zhexen Seitkulov, Yerzhan Shabayev, Yuriy Spada, Luigi La Tazhikenova, Sapiya Tilenbayev, Alzhan Tulembayeva, Aigul Yergaliyev, Dastan Yergaliyeva, Banu Yessilov, Assylkhan Zeeshan, Nida Zhakupov, Altynbek Zhanibek, Kazybek bi Zhanzhigitov, Syrym Zhumabayeva, Assel Zhumazhanov, Berik Zinagabdenova, Dariga