SavedAdd your journal

Journals / Journal of Aviation / 2026 / Volume: 10 Issue: 2

Distributed Artificial Intelligence and Edge Computing Architectures in Resource-Constrained Environments: A PRISMA-Based Systematic Literature Review on Autonomous UAV Systems

Metin Taşkın(TEKIRDAG NAMIK KEMAL UNIVERSITY)0009-0005-4950-5194
Pages
—
Publication Date
2026-06-27
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study concludes with an algorithmic taxonomy and a set of open design problems targeting autonomous UAV platforms in adversarial settings.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We report a systematic literature review examining distributed artificial intelligence algorithms and edge computing architectures for autonomous UAV platforms operating under resource and communication constraints, conducted within the PRISMA protocol framework.
📊
Key Findings & Data:Of 50 candidate studies retrieved from the Web of Science database, 43 were included in full-text analysis following scope and quality assessment.
💡
Conclusion & Impact:The study concludes with an algorithmic taxonomy and a set of open design problems targeting autonomous UAV platforms in adversarial settings.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Distributed Artificial Intelligence · Federated Learning · Edge Computing

Abstract

We report a systematic literature review examining distributed artificial intelligence algorithms and edge computing architectures for autonomous UAV platforms operating under resource and communication constraints, conducted within the PRISMA protocol framework. Existing reviews on UAV systems focus predominantly on physical-layer security and jamming; none provides a unified architectural analysis integrating the CAP Theorem, computational complexity, and hardware-software co-design perspectives. This review addresses that gap by offering a structured design reference for distributed systems researchers working on autonomous UAV platforms. Of 50 candidate studies retrieved from the Web of Science database, 43 were included in full-text analysis following scope and quality assessment. The studies were classified along three axes: (1) distributed AI/ML paradigm (FL, DRL, MARL, CNN, Transformer), (2) system architecture decision (MEC, hierarchical FL, Gossip-based learning), and (3) optimized metric (latency, energy consumption, model size, detection accuracy). FedAvg-based FL and DRL emerge as the leading approaches in distributed swarm learning and online resource allocation, respectively. CAP Theorem analysis shows that the majority of the examined architectures prioritize availability and partition tolerance over consistency, a design decision consistent with adversarial operating conditions. Byzantine-fault tolerant FL and model compression remain under addressed in the reviewed literature. The study concludes with an algorithmic taxonomy and a set of open design problems targeting autonomous UAV platforms in adversarial settings.

🔖

Cite This Article

Taşkın, M. (2026). Distributed Artificial Intelligence and Edge Computing Architectures in Resource-Constrained Environments: A PRISMA-Based Systematic Literature Review on Autonomous UAV Systems. Journal of Aviation, Volume: 10 Issue: 2. https://doi.org/10.30518/jav.1914164

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (45)

  1. Agnew, D., Del Aguila, A., & McNair, J. (2024). Enhanced network metric prediction for machine learning-based cyber security of a software-defined UAV relay network. IEEE Access, 12, 54202–54219.
  2. Aich, N., Oubrahim, Z., Talount, H. A., & Abbou, A. (2025). Bi-scale Mahalanobis detection for reactive jamming in UAV OFDM links. Future Internet, 17(10), Article 474.
  3. Al-Syouf, R., Aljarrah, O. Y., Bani-Hani, R., & Alma'aitah, A. (2025). Ensemble machine learning models utilizing a hybrid recursive feature elimination (RFE) technique for detecting GPS spoofing attacks against unmanned aerial vehicles. Sensors, 25(8), Article 2388.
  4. Aldosari, W. (2023). Received power based unmanned aerial vehicles (UAVs) jamming detection and nodes classification using machine learning. CMC-Computers, Materials & Continua, 75(1), 1253–1269.
  5. Allak, A. S. H., Yi, J. J., Al-Sabbagh, H. M., & Chen, L. W. (2025). Siamese neural networks in unmanned aerial vehicle target tracking process. IEEE Access, 13, 24309–24322.
  6. Alsumayt, A., Nagy, N., Alsharyofi, S., Al-Rabie, R., Alahmadi, R., Alesse, R. A., & Alahmadi, A. A. (2024). Detecting denial of service attacks (DoS) over the internet of drones (IoD) based on machine learning. SCI, 6(3), Article 56.
  7. Chen, Y. T., Ding, Y., Chen, B. Y., Si, P. Y., Lu, W. D., Lin, D., Yang, Z. T., & Hu, S. (2025). Covert communication towards a flying warden in UAV-assisted MEC system. Chinese Journal of Aeronautics, 38(10), Article 103568.
  8. Cheepurupalli, S., Egu, D. K., Upadhyay, P. K., Salhab, A. M., Moualeu, J. M., & Nardelli, P. H. J. (2025). Deep learning-enabled secrecy performance analysis of UAV-aided reconfigurable intelligent surfaces with non- orthogonal multiple access. IEEE Transactions on Cognitive Communications and Networking, 11(6), 3797– 3810.
  9. Dinler, Ö. B. (2025). UAV cybersecurity with Mamba-KAN-Liquid hybrid model: Deep learning-based real-time anomaly detection. Drones, 9(11), Article 806.
  10. Farkhari, H., Viana, J., Kahvazadeh, S., Sebastiao, P., Jimenez, V. P. G., & Dinis, R. (2024). A hybrid approach to reliable jamming identification in UAV communications using combined DNNs and ML algorithms. IEEE Access, 12, 178898–178908.
  11. Fu, W. X., Li, B., Wang, H. P., Gong, H. C., & Lin, X. (2025). A GAN-based method for cognitive covert communication UAV jamming-assistance under fully labeled sample conditions. Technologies, 13(7), Article 283.
  12. Ghelani, J., Gharia, P., & El-Ocla, H. (2024). Gradient monitored reinforcement learning for jamming attack detection in FANETs. IEEE Access, 12, 23081–23095.
  13. Han, S. Y., Wan, P. F., Lian, Z. X., Wang, M. Y., Li, D. D., & Fan, C. L. (2025). Research on the construction and resource optimization of a UAV command information system based on large language models. Drones, 9(9), Article 639.
  14. Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018). Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2704–2713).
  15. Jeon, S. E., Lee, S. J., Lee, Y. R., Yu, H. J., & Lee, I. G. (2024). Machine learning-based cooperative clustering for detecting and mitigating jamming attacks in beyond 5G networks. Information Systems Frontiers, 26(5), 2657–2674.
  16. Khan, M. R., & Premkumar, G. R. V. (2025). Robust UAV-oriented wireless communications via multi-agent deep reinforcement learning to optimize user coverage. Drones, 9(5), Article 321.
  17. Koloskova, A., Loizou, N., Boreiri, S., Jaggi, M., & Stich, S. (2020). A unified theory of decentralized SGD with changing topology and local updates. In Proceedings of the 37th International Conference on Machine Learning (ICML) (pp. 5381–5393). https://proceedings.mlr.press/v119/koloskova20a.html
  18. Li, Y. C., Pawlak, J., Price, J., Niyaz, Q., Paheding, S., & Devabhaktuni, V. (2022). Jamming detection and classification in OFDM-based UAVs via feature- and spectrogram-tailored machine learning. IEEE Access, 10, 16859–16870.
  19. Lin, Y. X., & Lai, Y. C. (2025). Deep learning-based navigation system for automatic landing approach of fixed- wing UAVs in GNSS-denied environments. Aerospace, 12(4), Article 324.
  20. Lohan, P., Kantarci, B., Ferrag, M. A., Tihanyi, N., & Shi, Y. (2024). From 5G to 6G networks: A survey on AI-based jamming and interference detection and mitigation. IEEE Open Journal of the Communications Society, 5, 3920–3974.
  21. Lu, F. W., Liu, G. L., Lu, W. D., Gao, Y., Cao, J., Zhao, N., & Nallanathan, A. (2024). Resource and trajectory optimization for UAV-relay-assisted secure maritime MEC. IEEE Transactions on Communications, 72(3), 1641– 1652.
  22. Meheretu, S. E., Nigussie, E., Gebremeskel, G. B., & Hailesilassie, S. Y. (2025). A systematic literature review on spoofing and jamming approaches in unmanned aerial vehicles navigation. Journal of Aerospace Technology and Management, 17, Article e3425.
  23. Mertens, J. S., Panebianco, A., Surudhi, A., Prabagarane, N., & Galluccio, L. (2023). Network intelligence vs. jamming in underwater networks: How learning can cope with misbehavior. Frontiers in Communications and Networks, 4, Article 1179626.
  24. Miao, J., Chen, H., Li, H., & Bai, S. (2023). Secrecy energy efficiency enhancement in UAV-assisted MEC system. Sensors, 23(2), Article 723.
  25. Mood, S. E., Souri, A., Inanç, N., & Chen, M. Y. (2025). A novel gallop operator for horse herd optimization algorithm: Application to feature selection of safety defect prediction in UAV. Cluster Computing, 28(13), Article 820.
  26. Morshedi, R., & Matinkhah, S. M. (2026). Cybersecurity challenges and solutions in unmanned aerial vehicles (UAVs). Journal of Field Robotics, 43(1), 314–329.
  27. Mu, G. C. (2021). Security efficiency maximization for multi-UAV-aided network with mobile edge computing. Frontiers in Computer Science, 3, Article 691854.
  28. Nguyen, A. N., Nguyen, G. H., Nguyen, K., Ngo, T. S., Bui, N. A., Le, P. C., Hoang, M. D., Trinh, T. D., Hoang, T. A., & So- In, C. (2026). On the performance and optimization of energy-efficient secrecy computation offloading for multiantenna UAV-enabled NOMA-MEC with aerial friendly jamming. IEEE Open Journal of the Communications Society, 7, 925–953.
  29. Ni, D., Ma, S., Du, J., Wu, Y., Zhou, C., & Xiao, H. (2025). A federated hierarchical DQN-based distributed intelligent anti-jamming method for UAVs. Sensors, 26(1), Article 181.
  30. Niu, Q. R., Zhang, L. Y., Ren, S. Y., Gao, W., & Wang, C. J. (2025). Attention-enhanced contrastive BiLSTM for UAV intention recognition under information uncertainty. Drones, 9(4), Article 319.
  31. Qasim, N. H., & Jawad, A. M. (2024). 5G-enabled UAVs for energy-efficient opportunistic networking. Heliyon, 10(12), e32660.
  32. Ryu, J., & Kim, S. (2025). Mitigating jamming attacks in underwater sensor networks using M-Qubed-based opportunistic routing protocol. ETRI Journal, 47(3), 559–571.
  33. Şahin, C. B. (2026). Quantum-resilient federated learning for multi-layer cyber anomaly detection in UAV systems. Sensors, 26(2), Article 509.
  34. Shafique, A., Mehmood, A., & Elhadef, M. (2021). Detecting signal spoofing attack in UAVs using machine learning models. IEEE Access, 9, 93803–93815.
  35. Shin, M., Chang, S. Y., Kim, J., Park, K., & Kim, J. (2024). Trajectory-driven deep learning for UAV location integrity checks. IEEE Access, 12, 178789–178804.
  36. Simon, O., & Götthans, T. (2022). A survey on the use of deep learning techniques for UAV jamming and deception. Electronics, 11(19), Article 3025.
  37. Taleb, T., Sehad, N., Nadir, Z., & Song, J. S. (2023). VR-based immersive service management in B5G mobile systems: A UAV command and control use case. IEEE Internet of Things Journal, 10(6), 5349–5363.
  38. Viana, J., Farkhari, H., Sebastiao, P., Campos, L. M., Koutlia, K., Bojovic, B., & Dinis, R. (2024). Deep attention recognition for attack identification in 5G UAV scenarios: Novel architecture and end-to-end evaluation. IEEE Transactions on Vehicular Technology, 73(1), 131–146.
  39. Viana, J., Farkhari, H., & Sebastiao, P. (2025). PCA-featured transformer for jamming detection in 5G UAV networks. IEEE Open Journal of the Communications Society, 6, 9287–9303.
  40. Vo, V., Long, N. Q., Dang, V. H., Ho, T. D., Tran, H., Chatzinotas, S., Tran, D. H., Sanguanpong, S., & So-In, C. (2025). Deep learning-driven throughput maximization in covert communication for UAV-RIS cognitive systems. IEEE Open Journal of the Communications Society, 6, 4140–4155.
  41. Wei, Z. K., & Guo, W. S. (2024). Control layer security: Exploiting unobservable cooperative states of autonomous systems for secret key generation. IEEE Transactions on Mobile Computing, 23(10), 9989–10000.
  42. Xue, H. N., Zhuo, Z. H., Yan, W. H., & Zhang, Y. X. (2025). Research on UAV jamming signal generation based on intelligent jamming. IEEE Access, 13, 14686–14701.
  43. Yadav, K., Upadhyay, P. K., Moualeu, J. M., Osman, A. A. F., & Nardelli, P. H. J. (2025). Deep learning-based secrecy performance of UAV-IRS NOMA systems with friendly jamming. IEEE Open Journal of the Communications Society, 6, 4533–4548.
  44. Yang, H. L., Zheng, M. T., Shao, Z. L., Jiang, Y. F., & Xiong, Z. H. (2025). Intelligent computation offloading and trajectory planning for 3-D target search in low-altitude economy scenarios. IEEE Wireless Communications Letters, 14(4), 949–953.
  45. Zhou, Y., Ma, Z., Fan, P. Z., Al-Nahari, A., Yeoh, P. L., Vucetic, B., & Li, Y. H. (2026). Energy minimization for UAV- enabled covert offloading MEC systems. IEEE Transactions on Vehicular Technology, 75(2), 3306–3311.