Formula-based Architectural Framework of the SecuDroneComm Platform for Unmanned Aerial Vehicle Communications

Rexhep Mustafovski

doi:10.31181/msa21202525

Authors

Rexhep Mustafovski University "St. Cyril and Methodius" – Skopje, Faculty of Electrical Engineering and Information Technologies, Skopje, Republic of North Macedonia Author https://orcid.org/0009-0000-3257-0989

DOI:

https://doi.org/10.31181/msa21202525

Keywords:

SecuDroneComm, UAV Communication, Tactical Operations Center, YOLOv8, Hybrid Server Architecture, Real-Time Processing, Secure Communication Platform

Abstract

The SecuDroneComm platform is designed to provide secure, reliable, and efficient communication between drones and the tactical operations center. Its architecture integrates data integrity, encryption efficiency, energy optimization, collision avoidance, and real-time processing through a mathematical framework of dedicated formulas. The platform ensures that critical mission data is protected from tampering, transmitted with minimal delay, and prioritized based on urgency and network stability. By applying specialized formulas, such as the drone data integrity formula, encrypted data transmission efficiency formula, and data prioritization index, the platform strengthens communication security and improves decision-making in operational environments. Additional formulas address challenges of battery optimization, multi-drone coordination, network congestion, and system reliability, ensuring resilient operations during missions. The integration of YOLOv8 within the platform enhances object detection by balancing accuracy and inference speed, supported by GPU load analysis and bandwidth allocation models. The hybrid server structure optimizes latency, resource distribution, and encryption key management, creating a unified solution for real-time unmanned aerial vehicle surveillance. This paper presents the complete formula-based framework of SecuDroneComm, demonstrating its capability to improve operational efficiency, cybersecurity resilience, and mission sustainability in dynamic and high-risk environments.

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References

Sigholm, J. (2016). Secure Tactical Communications for Inter-Organizational Collaboration: The Role of Emerging ICT, Privacy Issues, and Cyber Threats on the Digital Battlefield. University of Skövde, Sweden.

Ryan, M., & Frater, M. (2018). Combat SkySat Tactical Communication System. Land Warfare Studies Centre Working Papers.

Jones, D. O., Gates, A. R., Huvenne, V. A., Phillips, A. B., & Bett, B. J. (2019). Autonomous marine environmental monitoring: Application in decommissioned oil fields. Science of the Total Environment, 668, 835-853. https://doi.org/10.1016/j.scitotenv.2019.02.310.

Raghuram, Y., & Leon, E. C. (2017). Building the Infrastructure for Cloud Security. Apress Media.

Roberts, E., & Smith, L. (2020). Real-Time Latency Simulation in Encrypted UAV Communication. IEEE MILCOM.

Ryan, M., & Frater, M. (2020). Tactical Communications System for Future Land Warfare. Journal of Battlefield Technology. Land Warfare Studies Centre, Working Papers, No. 109.

Miller, J., Taylor, B., & White, J. (2018). Key Performance Indicators for Evaluating UAV Communication Systems, Systems Engineering Quarterly.

Bardis, N. G., Doukas, N., & Ntaikos, K. (2008). Design and development of a secure military communication based on AES prototype crypto algorithm and advanced key management scheme. WSEAS Transactions on Information Science & Applications, 10(5), 1501-1510.

Talib, M., Al-Noori, A. H., & Suad, J. (2024). YOLOv8-CAB: Improved YOLOv8 for Real-time object detection. Karbala International Journal of Modern Science, 10(1), 5. https://doi.org/10.33640/2405-609X.3339.

Safaldin, M., Zaghden, N., & Mejdoub, M. (2024). An improved YOLOv8 to detect moving objects. IEEE Access, 12, 59782-59806. https://doi.org/10.1109/ACCESS.2024.3393835.

Sohan, M., Sai Ram, T., & Rami Reddy, C. V. (2024). A review on yolov8 and its advancements. In: International Conference on Data Intelligence and Cognitive Informatics (pp. 529-545). Springer, Singapore. https://doi.org/10.1007/978-981-99-7962-2_39.

Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464-7475).

Maktab Dar Oghaz, M., Razaak, M., & Remagnino, P. (2022). Enhanced single shot small object detector for aerial imagery using super-resolution, feature fusion and deconvolution. Sensors, 22(12), 4339. https://doi.org/10.3390/s22124339.

Roy, A. M., & Bhaduri, J. (2023). DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism. Advanced Engineering Informatics, 56, 102007. https://doi.org/10.1016/j.aei.2023.102007.

Yong, P., Li, S., Wang, K., & Zhu, Y. (2022). A real-time detection algorithm based on nanodet for pavement cracks by incorporating attention mechanism. In: 2022 8th international conference on hydraulic and civil engineering: deep space intelligent development and utilization forum (ICHCE) (pp. 1245-1250). IEEE. https://doi.org/10.1109/ICHCE57331.2022.10042517.

Safaldin, M., Zaghden, N., & Mejdoub, M. (2023). Moving object detection based on enhanced Yolo-V2 model. In: 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-8). IEEE. https://doi.org/10.1109/HORA58378.2023.10156680.

Ammar, S., Bouwmans, T., Zaghden, N., & Neji, M. (2020). From moving objects detection to classification and recognition: a review for smart environments. Towards Smart World, pp. 289-316.

Ibrahim, E. M., Mejdoub, M., & Zaghden, N. (2022). Semantic analysis of moving objects in video sequences. In: International Conference on Emerging Technologies and Intelligent Systems (pp. 257-269). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-20429-6_25.

Ma, H., Celik, T., & Li, H. (2021). Fer-yolo: Detection and classification based on facial expressions. In: International Conference on Image and Graphics (pp. 28-39). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-87355-4_3.

Tong, K., Wu, Y., & Zhou, F. (2020). Recent advances in small object detection based on deep learning: A review. Image and Vision Computing, 97, 103910. https://doi.org/10.1016/j.imavis.2020.103910.

Singh, S. A., & Desai, K. A. (2023). Automated surface defect detection framework using machine vision and convolutional neural networks. Journal of Intelligent Manufacturing, 34(4), 1995-2011. https://doi.org/10.1007/s10845-021-01878-w.

Du, L., Zhang, R., & Wang, X. (2020). Overview of two-stage object detection algorithms. Journal of Physics: Conference Series, 1544(1), 012033. https://doi.org/10.1088/1742-6596/1544/1/012033.

Sultana, F., Sufian, A., Dutta, P. (2020). A Review of Object Detection Models Based on Convolutional Neural Network. Advances in Intelligent Systems and Computing, 1157. https://doi.org/10.1007/978-981-15-4288-6_1.

Hussain, M. (2023). YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11(7), 677. https://doi.org/10.3390/machines11070677.

Mustafovski, R., Risteski, A., & Shuminoski, T. (2025). State-of-the-Art Comparison of the SecuDroneComm Platform with Existing Secure Drone Communication Systems. In: Proceedings of the International Conference "Annual conference on Challenges of Contemporary Higher Education, Kopaonik, Serbia, 3-7 February 2025.

Mustafovski, R., & Shuminoski, T. (2025). Integrating Computer Vision with YOLOv8 Algorithm for PID: A State-of-the-Art Analysis. International Scientific Journal "Contemporary Macedonian Defence", Ministry of Defence of the Republic of North Macedonia.

Mustafovski, R. (2025). The Use of Communication Platforms in Military Operations: Enhancing Strategic and Tactical Effectiveness. Database Systems Journal, 16.

Mustafovski, R. (2025). Evaluating the Operational Impact of SecuDroneComm: Simulation-Based Assessment of Secure UAV Communication in Military Environments. System, 75(1), 11-18. https://doi.org/10.5937/str2500002M.