Vehicle to Everything (V2X) and Edge Computing: A Secure Federated learning-based Lifecycle for UAV-Assisted Vehicle Network: A Survey
DOI:
https://doi.org/10.21015/vtse.v14i1.2286Abstract
The Internet of Vehicles (IoV), integrated with edge computing, revolutionizes communication in smart cities. This paper investigates Unmanned Aerial Vehicle (UAV)-assisted vehicle networks, addressing the growing complexity of connected vehicle systems. The proposed system enhances efficiency, security, and scalability by incorporating Vehicle-to-Everything (V2X) communication, edge computing, and UAV deployment. Federated Learning (FL) creates a secure, privacy-preserving lifecycle for UAV-assisted V2X networks. UAVs serve as edge computing nodes, extending network coverage, performing real-time data acquisition, and enabling fast processing and decision-making. FL supports collaborative vehicle-UAV model training while ensuring data protection. The system lifecycle involves data acquisition, FL-based model development, and secure communication during system adjustments. Scalability, latency, and energy efficiency challenges are explored with proposed solutions. The research demonstrates how UAV-assisted V2X networks improve traffic management, autonomous driving, and emergency response, contributing to safer and more efficient transportation systems.
References
Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Federated learning for data privacy preservation in vehicular cyber-physical systems,” IEEE Netw., vol. 34, no. 3, pp. 50–56, May/Jun. 2020.
L. Zhou, H. Mao, X. Deng, J. Zhang, H. Zhao, and J. Wei, “Real-time radio map construction and distribution for UAV-assisted mobile edge computing networks,” IEEE Internet Things J., vol. 11, no. 12, pp. 21337–21346, Jun. 2024.
C. Wang, L. Liu, C. Jiang, S. Wang, P. Zhang, and S. Shen, “Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network,” IEEE J. Sel. Topics Signal Process., vol. 16, no. 3, pp. 434–446, Apr. 2022.
H. Zeng et al., “Collaborative computation offloading for UAVs and USV fleets in communication networks,” in Proc. Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), 2022, pp. 949–954.
W. Zhang, L. Li, N. Zhang, T. Han, and S. Wang, “Air-ground integrated mobile edge networks: A survey,” IEEE Access, vol. 8, pp. 125998–126018, 2020.
A. Hermosilla, A. M. Zarca, J. B. Bernabe, J. Ortiz, and A. Skarmeta, “Security orchestration and enforcement in NFV/SDN-aware UAV deployments,” IEEE Access, vol. 8, pp. 131779–131795, 2020.
Y. Wu, Y. Song, T. Wang, L. Qian, and T. Q. Quek, “Non-orthogonal multiple access assisted federated learning via wireless power transfer: A cost-efficient approach,” IEEE Trans. Commun., vol. 70, no. 4, pp. 2853–2869, Apr. 2022.
Q. Xue et al., “Beam management in ultra-dense mmWave network via federated reinforcement learning: An intelligent and secure approach,” IEEE Trans. Cogn. Commun. Netw., early access, Oct. 2022.
A. Chriki, H. Touati, H. Snoussi, and F. Kamoun, “UAV-GCS centralized data-oriented communication architecture for crowd surveillance applications,” in Proc. IEEE 15th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), 2019, pp. 2064–2069.
V. Hassija, V. Chamola, D. N. G. Krishna, and M. Guizani, “A distributed framework for energy trading between UAVs and charging stations for critical applications,” IEEE Trans. Veh. Technol., vol. 69, no. 5, pp. 5391–5402, May 2020.
V. Sharma, I. You, D. N. K. Jayakody, D. G. Reina, and K.-K. R. Choo, “Neural-blockchain-based ultra-reliable caching for edge-enabled UAV networks,” IEEE Trans. Ind. Informat., vol. 15, no. 10, pp. 5723–5736, Oct. 2019.
A. Islam and S. Y. Shin, “BUS: A blockchain-enabled data acquisition scheme with the assistance of UAV swarm in Internet of Things,” IEEE Access, vol. 7, pp. 103231–103249, 2019.
W. Xiao, M. Li, B. Alzahrani, R. Alotaibi, A. Barnawi, and Q. Ai, “A blockchain-based secure crowd monitoring system using UAV swarm,” IEEE Netw., vol. 35, no. 1, pp. 108–115, Jan./Feb. 2021.
L. Spampinato, A. Tarozzi, C. Buratti, and R. Marini, “DRL path planning for UAV-aided V2X networks: Comparing discrete to continuous action spaces,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), 2023, pp. 1–5.
R. Marini, S. Park, O. Simeone, and C. Buratti, “Continual meta-reinforcement learning for UAV-aided vehicular wireless networks,” in Proc. IEEE Int. Conf. Commun. (ICC), 2023, pp. 5664–5669.
M. Eskandari, H. Huang, A. V. Savkin, and W. Ni, “Model predictive control-based 3D navigation of a RIS-equipped UAV for LoS wireless communication with a ground intelligent vehicle,” IEEE Trans. Intell. Veh., vol. 8, no. 3, pp. 2371–2384, Mar. 2023.
M. Masuduzzaman, A. Islam, K. Sadia, and S. Y. Shin, “UAV-based MEC-assisted automated traffic management scheme using blockchain,” Future Gener. Comput. Syst., vol. 134, pp. 256–270, Sep. 2022.
M. I. Ashqer, “Evaluating a signalized intersection performance using unmanned aerial data,” Transp. Lett., vol. 16, no. 5, pp. 452–460, 2024.
Y. Su, M. Liwang, Z. Chen, and X. Du, “Toward optimal deployment of UAV relays in UAV-assisted IoV networks,” IEEE Trans. Veh. Technol., vol. 72, no. 10, pp. 13392–13405, Oct. 2023.
A. Bouguettaya, H. Zarzour, A. Kechida, and A. M. Taberkit, “Vehicle detection from UAV imagery with deep learning: A review,” IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 11, pp. 6047–6067, Nov. 2022.
N. Parvaresh, M. Kulhandjian, H. Kulhandjian, C. D’Amours, and B. Kantarci, “A tutorial on AI-powered 3D deployment of drone base stations: State of the art, applications and challenges,” Veh. Commun., vol. 36, Art. no. 100474, Aug. 2022.
S. A. H. Mohsan, M. A. Khan, F. Noor, I. Ullah, and M. H. Alsharif, “Towards the unmanned aerial vehicles (UAVs): A comprehensive review,” Drones, vol. 6, no. 6, Art. no. 147, 2022.
A. D. Boursianis, “Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review,” Internet Things, vol. 18, Art. no. 100187, May 2022.
M. Idrissi, M. Salami, and F. Annaz, “A review of quadrotor unmanned aerial vehicles: Applications, architectural design and control algorithms,” J. Intell. Robot. Syst., vol. 104, no. 2, pp. 1–33, 2022.
Z. Zuo, C. Liu, Q.-L. Han, and J. Song, “Unmanned aerial vehicles: Control methods and future challenges,” IEEE/CAA J. Autom. Sin., vol. 9, no. 4, pp. 601–614, Apr. 2022.
Z. Ning, “Mobile edge computing and machine learning in the Internet of unmanned aerial vehicles: A survey,” ACM Comput. Surveys, vol. 56, no. 1, Art. no. 13, 2023.
S. A. H. Mohsan, N. Q. H. Othman, Y. Li, M. H. Alsharif, and M. A. Khan, “Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends,” Intell. Serv. Robot., vol. 16, no. 1, pp. 109–137, 2023.
W. Sun, N. Xu, L. Wang, H. Zhang, and Y. Zhang, “Dynamic digital twin and federated learning with incentives for air-ground networks,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 1, pp. 321–333, Jan./Feb. 2022.
Z. Wei, Z. Meng, M. Lai, H. Wu, J. Han, and Z. Feng, “Anti-collision technologies for unmanned aerial vehicles: Recent advances and future trends,” IEEE Internet Things J., vol. 9, no. 10, pp. 7619–7638, May 2022.
D. Liu et al., “Opportunistic data collection in cognitive wireless sensor networks: Air–ground collaborative online planning,” IEEE Internet Things J., vol. 7, no. 9, pp. 8837–8851, Sep. 2020.
Y. Li, M. Scanavino, E. Capello, F. Dabbene, G. Guglieri, and A. Vilardi, “A novel distributed architecture for UAV indoor navigation,” Transp. Res. Procedia, vol. 35, pp. 13–22, 2018.
M. Campion, P. Ranganathan, and S. Faruque, “UAV swarm communication and control architectures: A review,” J. Unmanned Veh. Syst., vol. 7, no. 2, pp. 93–106, 2018.
M. S. Alam, G. K. Kurt, H. Yanikomeroglu, P. Zhu, and N. D. Dao, “High altitude platform station based super macro base station constellations,” IEEE Commun. Mag., vol. 59, no. 1, pp. 103–109, Jan. 2021.
G. K. Kurt et al., “A vision and framework for the high altitude platform station (HAPS) networks of the future,” IEEE Commun. Surveys Tuts., vol. 23, no. 2, pp. 729–779, 2nd Quart., 2021.
J. Qiu, D. Grace, G. Ding, M. D. Zakaria, and Q. Wu, “Air-ground heterogeneous networks for 5G and beyond via integrating high and low altitude platforms,” IEEE Wireless Commun., vol. 26, no. 6, pp. 140–148, Dec. 2019.
Q. Wu, J. Xu, and R. Zhang, “Capacity characterization of UAV enabled two-user broadcast channel,” IEEE J. Sel. Areas Commun., vol. 36, no. 9, pp. 1955–1971, Sep. 2018.
J. Li et al., “Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UAV-aided data collection,” IEEE Internet Things J., vol. 7, no. 2, pp. 1464–1475, Feb. 2020.
L. Yang, H. Yao, J. Wang, C. Jiang, A. Benslimane, and Y. Liu, “Multi-UAV-enabled load-balance mobile-edge computing for IoT networks,” IEEE Internet Things J., vol. 7, no. 8, pp. 6898–6908, Aug. 2020.
S.-F. Chou, A.-C. Pang, and Y.-J. Yu, “Energy-aware 3D unmanned aerial vehicle deployment for network throughput optimization,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 563–578, Jan. 2020.
S. Zhang and J. Liu, “Analysis and optimization of multiple unmanned aerial vehicle-assisted communications in post-disaster areas,” IEEE Trans. Veh. Technol., vol. 67, no. 12, pp. 12049–12060, Dec. 2018.
Z. Kaleem, W. Khalid, A. Muqaibel, A. A. Nasir, C. Yuen, and G. K. Karagiannidis, “Learning-aided UAV 3D placement and power allocation for sum-capacity enhancement under varying altitudes,” IEEE Commun. Lett., vol. 26, no. 7, pp. 1633–1637, Jul. 2022.
X. Li, H. Yao, J. Wang, X. Xu, C. Jiang, and L. Hanzo, “A near-optimal UAV-aided radio coverage strategy for dense urban areas,” IEEE Trans. Veh. Technol., vol. 68, no. 9, pp. 9098–9109, Sep. 2019.
X. Zhang and L. Duan, “Fast deployment of UAV networks for optimal wireless coverage,” IEEE Trans. Mobile Comput., vol. 18, no. 3, pp. 588–601, Mar. 2019.
X. Liu et al., “Transceiver design and multi-hop D2D for UAV IoT coverage in disasters,” IEEE Internet Things J., vol. 6, no. 2, pp. 1803–1815, Apr. 2019.
M. Hu et al., “Joint routing and scheduling for vehicle-assisted multidrone surveillance,” IEEE Internet Things J., vol. 6, no. 2, pp. 1781–1790, Apr. 2019.
Z. Zhou et al., “When mobile crowd sensing meets UAV: Energy-efficient task assignment and route planning,” IEEE Trans. Commun., vol. 66, no. 11, pp. 5526–5538, Nov. 2018.
C. H. Liu, Z. Chen, and Y. Zhan, “Energy-efficient distributed mobile crowd sensing: A deep learning approach,” IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1262–1276, Jun. 2019.
Q. Zhang, M. Jiang, Z. Feng, W. Li, W. Zhang, and M. Pan, “IoT enabled UAV: Network architecture and routing algorithm,” IEEE Internet Things J., vol. 6, no. 2, pp. 3727–3742, Apr. 2019.
A. Mukherjee, S. Misra, V. S. P. Chandra, and M. S. Obaidat, “Resource-optimized multi-armed bandit-based offload path selection in edge UAV swarms,” IEEE Internet Things J., vol. 6, no. 3, pp. 4889–4896, Jun. 2019.
A. A. Khan, A. A. Laghari, M. Shafiq, S. A. Awan, and Z. Gu, “Vehicle to everything (V2X) and edge computing: A secure lifecycle for UAV-assisted vehicle network and offloading with blockchain,” Drones, vol. 6, no. 12, Art. no. 377, 2022.
H. Mei, K. Yang, Q. Liu, and K. Wang, “Joint trajectory-resource optimization in UAV-enabled edge-cloud system with virtualized mobile clone,” IEEE Internet Things J., vol. 7, no. 7, pp. 5906–5921, Jul. 2020.
R. Bassoli, C. Sacchi, F. Granelli, and I. Ashkenazi, “A virtualized border control system based on UAVs: Design and energy efficiency considerations,” in Proc. IEEE Aerosp. Conf., 2019, pp. 1–11.
N. Pathak, S. Misra, A. Mukherjee, A. Roy, and A. Y. Zomaya, “UAV virtualization for enabling heterogeneous and persistent UAV-as-a-service,” IEEE Trans. Veh. Technol., vol. 69, no. 6, pp. 6731–6738, Jun. 2020.
V. Sanchez-Aguero, F. Valera, B. Nogales, L. F. Gonzalez, and I. Vidal, “VENUE: Virtualized environment for multi-UAV network emulation,” IEEE Access, vol. 7, pp. 154659–154671, 2019.
W.-T. Tsai, Z. Zhao, C. Zhang, L. Yu, and E. Deng, “A multi-chain model for CBDC,” in Proc. 5th Int. Conf. Dependable Syst. Their Appl. (DSA), 2018, pp. 25–34.
Z. Liu and Z. Li, “A blockchain-based framework of cross-border e-commerce supply chain,” Int. J. Inf. Manage., vol. 52, Art. no. 102059, 2020.
M. Attaran, “Blockchain technology in healthcare: Challenges and opportunities,” Int. J. Healthcare Manage., vol. 15, pp. 70–83, 2022.
T. Kumar et al., “Blockchain utilization in healthcare: Key requirements and challenges,” in Proc. IEEE 20th Int. Conf. e-Health Netw., Appl. Services (HealthCom), 2018, pp. 1–7.
J. Zhou et al., “A survey on federated learning and its applications for accelerating industrial Internet of Things,” arXiv preprint arXiv:2104.10501, 2021.
S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated learning for wireless communications: Motivation, opportunities, and challenges,” IEEE Commun. Mag., vol. 58, no. 6, pp. 46–51, Jun. 2020.
B. Brik, A. Ksentini, and M. Bouaziz, “Federated learning for UAVs-enabled wireless networks: Use cases, challenges, and open problems,” IEEE Access, vol. 8, pp. 53841–53849, 2020.
N. Rieke et al., “The future of digital health with federated learning,” npj Digital Medicine, vol. 3, Art. no. 119, 2020.
J. Ding et al., “Federated learning challenges and opportunities: An outlook,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 8752–8756.
K. Wei et al., “Vertical federated learning: Challenges, methodologies and experiments,” arXiv preprint, arXiv:2202.04309, 2022.
A. A. Barakabitze, A. Ahmad, R. Mijumbi, and A. Hines, “5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges,” Computer Networks, vol. 167, Art. no. 106984, Feb. 2020.
A. Alalewi, I. Dayoub, and S. Cherkaoui, “On 5G-V2X use cases and enabling technologies: A comprehensive survey,” IEEE Access, vol. 9, pp. 107710–107737, 2021.
A. Herrera, “Explainable AI for transparent decision-making in autonomous vehicle cybersecurity operations,” Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 31–52, 2022.
C. I. Nwakanma et al., “Explainable artificial intelligence (XAI) for intrusion detection and mitigation in intelligent connected vehicles: A review,” Applied Sciences, vol. 13, no. 3, p. 1252, 2023.
H. Lundberg et al., “Experimental analysis of trustworthy in-vehicle intrusion detection system using explainable artificial intelligence (XAI),” IEEE Access, vol. 10, pp. 102831–102841, 2022.
A. R. Cavalli and E. M. De Oca, “Cybersecurity, monitoring, explainability and resilience,” in Proc. 14th Int. Conf. Mobile Computing and Ubiquitous Networking (ICMU), 2023, pp. 1–7.
Y. K. Saheed and J. E. Chukwuere, “XAIEnsembleTL-IoV: A new explainable artificial intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles,” Results in Engineering, vol. 24, Art. no. 103171, Dec. 2024.
S. Atakishiyev, M. Salameh, H. Yao, and R. Goebel, “Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions,” IEEE Access, vol. 12, pp. 101603–101625, 2024.
S. Nazat, L. Li, and M. Abdallah, “XAI-ADS: An explainable artificial intelligence framework for enhancing anomaly detection in autonomous driving systems,” IEEE Access, vol. 12, pp. 48583–48607, 2024.
A. Páez, “Explainable reinforcement learning models for adaptive cyber defense in autonomous vehicles,” Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, pp. 238–261, 2023.
H. Park and A. Haghani, “Real-time prediction of secondary incident occurrences using vehicle probe data,” Transportation Research Part C: Emerging Technologies, vol. 70, pp. 69–85, Sep. 2016.
L. Gaur and B. M. Sahoo, Explainable Artificial Intelligence for Intelligent Transportation Systems: Ethics and Applications. Cham, Switzerland: Springer Nature, 2022.
Z. Li, “Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing,” Travel Behaviour and Society, vol. 31, pp. 284–294, Apr. 2023.
O. Frendo, J. Graf, N. Gaertner, and H. Stuckenschmidt, “Data-driven smart charging for heterogeneous electric vehicle fleets,” Energy and AI, vol. 1, Art. no. 100007, Aug. 2020.
H. Rifa-Pous, V. Garcia-Font, C. Nunez-Gomez, and J. Salas, “Security, trust and privacy challenges in AI-driven 6G networks,” arXiv preprint, arXiv:2409.10337, 2024.
B. Siniarski et al., “ROBUST-6G: Smart, automated, and reliable security service platform for 6G,” in Proc. 15th Int. Conf. Ubiquitous and Future Networks (ICUFN), 2024, pp. 384–389.
M. Liyanage et al., “A survey on zero touch network and service management (ZSM) for 5G and beyond networks,” Journal of Network and Computer Applications, vol. 203, Art. no. 103362, Jul. 2022.
D. Giannopoulos et al., “ACROSS: Automated zero-touch cross-layer provisioning framework for 5G and beyond vertical services,” in Proc. Joint European Conf. Networks and Communications & 6G Summit (EuCNC/6G Summit), 2023, pp. 735–740.
T. Senevirathna, V. H. La, S. Marchal, B. Siniarski, M. Liyanage, and S. Wang, “A survey on XAI for beyond 5G security: Technical aspects, use cases, challenges and research directions,” arXiv preprint, arXiv:2204.12822, 2022.
J. Kim et al., “EPU: An energy-efficient explainable AI accelerator with sparsity-free computation and heat map compression/pruning,” IEEE Journal of Solid-State Circuits, vol. 59, no. 3, pp. 830–841, Mar. 2024.
R. Souza et al., “Workflow provenance in the computing continuum for responsible, trustworthy, and energy-efficient AI,” in Proc. IEEE 20th Int. Conf. e-Science (e-Sci.), 2024, pp. 1–7.
E. Baccour, A. Erbad, A. Mohamed, M. Hamdi, and M. Guizani, “Reinforcement learning-based dynamic pruning for distributed inference via explainable AI in healthcare IoT systems,” Future Generation Computer Systems, vol. 155, pp. 1–17, Jun. 2024.
L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model compression and hardware acceleration for neural networks: A comprehensive survey,” Proceedings of the IEEE, vol. 108, no. 4, pp. 485–532, Apr. 2020.
P. Szántó, T. Kiss, and K. J. Sipos, “Energy-efficient AI at the edge,” in Proc. 11th Mediterranean Conf. Embedded Computing (MECO), 2022, pp. 1–6.
D. Shankar and T. Jose, “Optimizing electronics architecture for the deployment of convolution neural networks using system-level modeling,” in Proc. AIAA DATC/IEEE 43rd Digital Avionics Systems Conf. (DASC), 2024, pp. 1–8.
O-RAN Next Generation Research Group (nGRG), Principles and Methodologies for AI/ML Testing in Next Generation Networks, Rep. RR-2024-04, O-RAN ALLIANCE e.V., Alfter, Germany, 2024.
C. Agarwal et al., “OpenXAI: Towards a transparent evaluation of model explanations,” in Proc. Advances in Neural Information Processing Systems, vol. 35, 2022, pp. 15784–15799.
A. Adhikari, E. Wenink, J. Van Der Waa, C. Bouter, I. Tolios, and S. Raaijmakers, “Towards FAIR explainable AI: A standardized ontology for mapping XAI solutions to use cases, explanations, and AI systems,” in Proc. 15th Int. Conf. Pervasive Technologies Related to Assistive Environments, 2022, pp. 562–568.
D. Wu et al., “Energy saving in cellular wireless networks via transfer deep reinforcement learning,” in Proc. IEEE Global Communications Conf. (GLOBECOM), 2023, pp. 7019–7024.
Z. Ye, K. Wang, Y. Chen, X. Jiang, and G. Song, “Multi-UAV navigation for partially observable communication coverage by graph reinforcement learning,” IEEE Transactions on Mobile Computing, vol. 22, no. 7, pp. 4056–4069, Jul. 2023.
G. Shen, L. Lei, X. Zhang, Z. Li, S. Cai, and L. Zhang, “Multi-UAV cooperative search based on reinforcement learning with a digital twin driven training framework,” IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8354–8368, Jul. 2023.
A. Fotouhi, M. Ding, L. Galati Giordano, M. Hassan, J. Li, and Z. Lin, “Joint optimization of access and backhaul links for UAVs based on reinforcement learning,” in Proc. IEEE Globecom Workshops (GC Wkshps), 2019, pp. 1–6.
H. Zhang, K. Zeng, and S. Lin, “FedUR: Federated learning optimization through adaptive centralized learning optimizers,” IEEE Transactions on Signal Processing, vol. 71, pp. 2622–2637, 2023.
I. Guarino, G. Aceto, D. Ciuonzo, A. Montieri, V. Persico, and A. Pescapè, “Explainable deep-learning approaches for packet-level traffic prediction of collaboration and communication mobile apps,” IEEE Open Journal of the Communications Society, vol. 5, pp. 1299–1324, 2024.
I. Soto, M. Calderon, O. Amador, and M. Urueña, “A survey on road safety and traffic efficiency vehicular applications based on C-V2X technologies,” Vehicular Communications, vol. 33, Art. no. 100428, 2022.
A. Hajisami, J. Lansford, A. Dingankar, and J. Misener, “A tutorial on the LTE-V2X direct communication,” IEEE Open Journal of Vehicular Technology, vol. 3, pp. 388–398, 2022.
M. Noor-A-Rahim et al., “6G for vehicle-to-everything (V2X) communications: Enabling technologies, challenges, and opportunities,” Proceedings of the IEEE, vol. 110, no. 6, pp. 712–734, Jun. 2022.
Annu and P. Rajalakshmi, “Towards 6G V2X sidelink: Survey of resource allocation—mathematical formulations, challenges, and proposed solutions,” IEEE Open Journal of Vehicular Technology, vol. 5, pp. 344–383, 2024.
A. M. Ince, A. E. Canbilen, and H. Yanikomeroglu, “HAPS-enabled V2X architecture for hyper reliable and low-latency communication (HRLLC) in 6G networks,” in Proc. 6th Int. Conf. Communications, Signal Processing, and Their Applications, 2024, pp. 1–6.
J. Sasiain, D. Franco, A. Atutxa, J. Astorga, and E. Jacob, “Toward the integration and convergence between 5G and TSN technologies and architectures for industrial communications: A survey,” IEEE Communications Surveys & Tutorials, vol. 27, no. 1, pp. 259–321, Feb. 2025.
W. Rafique, J. R. Barai, A. O. Fapojuwo, and D. Krishnamurthy, “A survey on beyond 5G network slicing for smart cities applications,” IEEE Communications Surveys & Tutorials, vol. 27, no. 1, pp. 595–628, Feb. 2025.
A. Nasrallah et al., “Ultra-low latency (ULL) networks: The IEEE TSN and IETF DetNet standards and related 5G ULL research,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 88–145, 1st Quart., 2019.
R. Sedar, C. Kalalas, F. Vázquez-Gallego, L. Alonso, and J. Alonso-Zarate, “A comprehensive survey of V2X cybersecurity mechanisms and future research paths,” IEEE Open Journal of the Communications Society, vol. 4, pp. 325–391, 2023.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
