Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 05 (2025)
Articles

Secure, Intelligent, and Distributed Communication Architectures for Autonomous Vehicular and Robotic Systems in V2X and 6G-Era Networks

pdf
  • Dilnoza Zubayd qizi Ismoilova
Dilnoza Zubayd qizi Ismoilova
Department of Electrical and Electronic Engineering, The University of Melbourne, Australia

Published 2025-05-31

Keywords

  • Autonomous vehicles,
  • V2X communication,
  • real-time encryption,
  • federated learning

How to Cite

Dilnoza Zubayd qizi Ismoilova. (2025). Secure, Intelligent, and Distributed Communication Architectures for Autonomous Vehicular and Robotic Systems in V2X and 6G-Era Networks. Stanford Database Library of American Journal of Applied Science and Technology, 5(05), 177–183. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/91

Copyright (c) 2025 Dilnoza Zubayd qizi Ismoilova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The rapid convergence of autonomous systems, vehicular networks, edge intelligence, and next-generation wireless communication has fundamentally transformed the operational landscape of cyber-physical mobility ecosystems. Autonomous vehicles, cooperative robotic systems, and intelligent transportation infrastructures now rely on continuous, real-time exchange of high-volume sensor data across heterogeneous communication domains. This paradigm shift has intensified long-standing concerns related to security, privacy, latency, trust, and system resilience, particularly in Vehicle-to-Everything (V2X) environments where safety-critical decisions are made under strict temporal constraints. Against this backdrop, the present research undertakes an extensive theoretical and analytical investigation of secure communication architectures for autonomous vehicular and robotic systems, with a particular emphasis on real-time encryption, distributed intelligence, and emerging paradigms such as federated and split learning.

Building upon foundational work in secure real-time sensor data transmission for autonomous systems, recent scholarship has emphasized the necessity of encryption mechanisms that balance cryptographic robustness with computational feasibility and ultra-low latency requirements (Patil & Deshpande, 2025). However, encryption alone is insufficient in isolation. The integration of edge computing, collaborative learning frameworks, and heterogeneous V2X access technologies introduces new attack surfaces and systemic vulnerabilities that demand a holistic, multi-layered security perspective. This article synthesizes and critically evaluates existing research across vehicular networking, cybersecurity, distributed machine learning, and autonomous system safety standards, while advancing a unifying conceptual framework that aligns security mechanisms with functional safety, ethical governance, and scalability requirements in 5G- and 6G-enabled environments.

Methodologically, this study adopts a structured qualitative research design grounded in systematic literature analysis, conceptual modeling, and comparative theoretical evaluation. Rather than proposing a singular algorithmic solution, the research focuses on interpretive synthesis, identifying patterns, tensions, and gaps across diverse bodies of literature. The results articulate a set of interdependent security principles—real-time cryptographic adaptability, decentralized trust management, privacy-preserving intelligence, and lifecycle-aware system governance—that collectively define the requirements of future autonomous mobility ecosystems. The discussion extends these findings by engaging with scholarly debates on centralized versus distributed control, the trade-offs between transparency and privacy, and the implications of emerging 6G sensing and localization capabilities for security architectures.

By offering a comprehensive, theoretically grounded, and critically nuanced examination of secure communication in autonomous systems, this article contributes to the ongoing discourse on how intelligent mobility infrastructures can be designed to be not only efficient and scalable, but also trustworthy, resilient, and ethically aligned. The findings are intended to inform researchers, system architects, policymakers, and standards bodies engaged in shaping the next generation of autonomous and connected systems.

 

pdf

References

  1. Arena, F., & Pau, G. (2019). An overview of vehicular communications. Future Internet, 11, 27.
  2. Patil, A. A., & Deshpande, S. (2025). Real-time encryption and secure communication for sensor data in autonomous systems. Journal of Information Systems Engineering and Management, 10(415), 41–55.
  3. Haidegger, T., Mai, V., Mörch, C., Boesl, D., Jacobs, A., Rao, B. R., Khamis, A., Lach, L., & Vanderborght, B. (2023). Robotics: Enabler and inhibitor of the Sustainable Development Goals. Sustainable Production and Consumption, 43, 422–434.
  4. Abboud, K., Omar, H. A., & Zhuang, W. (2016). Interworking of DSRC and cellular network technologies for V2X communications: A survey. IEEE Transactions on Vehicular Technology, 65, 9457–9470.
  5. McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016). Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629.
  6. Bauwens, J., Ruckebusch, P., Giannoulis, S., Moerman, I., & De Poorter, E. (2020). Over-the-air software updates in the Internet of Things: An overview of key principles. IEEE Communications Magazine, 58(2), 35–41.
  7. Ghosal, A., & Conti, M. (2020). Security issues and challenges in V2X: A survey. Computer Networks, 169, 107093.
  8. Ylianttila, M., Kantola, R., Gurtov, A., Mucchi, L., Oppermann, I., Yan, Z., Röning, J. (2020). 6G white paper: Research challenges for trust, security and privacy. arXiv preprint arXiv:2004.11665.
  9. Posner, J., Tseng, L., Aloqaily, M., & Jararweh, Y. (2021). Federated learning in vehicular networks: Opportunities and solutions. IEEE Network, 35(2), 152–159.
  10. Kim, K., Kim, J. S., Jeong, S., Park, J. H., & Kim, H. K. (2021). Cybersecurity for autonomous vehicles: Review of attacks and defense. Computers & Security, 102150.
  11. Liu, S., et al. (2019). Edge computing for autonomous driving: Opportunities and challenges. Proceedings of the IEEE, 107(8), 1697–1716.
  12. Bourdoux, A., Barreto, A. N., van Liempd, B., de Lima, C., Dardari, D., Belot, D., & Suutala, J. (2020). 6G white paper on localization and sensing. arXiv preprint arXiv:2006.01779.
  13. Houghtaling, M. A., Fiorini, S. R., Fabiano, N., Gonçalves, P. J., Ulgen, O., Haidegger, T., Carbonera, J. L., Olszewska, J. I., Page, B., & Murahwi, Z. (2024). Standardizing an ontology for ethically aligned robotic and autonomous systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54, 1791–1804.
  14. MacHardy, Z., Khan, A., Obana, K., & Iwashina, S. (2018). V2X access technologies: Regulation, research, and remaining challenges. IEEE Communications Surveys & Tutorials, 20, 1858–1877.
  15. ISO. (2011). ISO 26262: Road vehicles—Functional safety. Geneva: International Organization for Standardization.
  16. SAE International. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016).
  17. Bazzi, A., Cecchini, G., Menarini, M., Masini, B. M., & Zanella, A. (2019). Survey and perspectives of vehicular Wi-Fi versus sidelink cellular-V2X in the 5G era. Future Internet, 11, 122.
  18. Takacs, A., Drexler, D. A., Galambos, P., Rudas, I. J., Haidegger, T., & Csizmadia, T. (2020). Automotive safety in the development pipeline of highly automated vehicles. IEEE Systems, Man, and Cybernetics Magazine, 6, 35–40.
  19. Singh, A., et al. (2019). Detailed comparison of communication efficiency of split learning and federated learning. arXiv preprint arXiv:1909.09145.
  20. Wang, J., Shao, Y., Ge, Y., & Yu, R. (2019). A survey of vehicle to everything (V2X) testing. Sensors, 19, 334.