Uncertainty-Regularized Neural Surrogates for Sparse Governing- Drift- Blockchain-Backed Fifth-Generation Authentication with Trust-Aware Consensus Under Intermittent Edge Connectivity Constraints
Keywords:
Blockchain Technology, Fifth-Generation Networks, Continuous Authentication, Intermittent Connectivity, Trust-Aware ConsensusAbstract
The rapid deployment of fifth-generation telecommunication networks has necessitated decentralized architectures to support massive machine-type communications and ultra-reliable low-latency applications. Mobile edge computing brings processing capabilities closer to the end user, but introduces significant security vulnerabilities, particularly concerning continuous authentication under intermittent network connectivity. Traditional centralized authentication mechanisms suffer from high latency and single points of failure, making them unsuitable for dynamic edge environments where backhaul links to the core network frequently degrade or drop entirely. This paper proposes a novel blockchain-backed authentication framework tailored for fifth-generation edge environments, incorporating a unique trust-aware consensus mechanism designed specifically to operate under intermittent connectivity constraints. By utilizing a localized distributed ledger at the edge, the proposed system enables continuous, decentralized verification of user equipment identities without relying on continuous core network availability. The trust-aware consensus algorithm dynamically adjusts quorum requirements and validator weightings based on node reputation and historical uptime, ensuring that localized network partitions can maintain operational security and seamlessly merge ledgers upon connection restoration. Comprehensive simulated evaluations demonstrate that the proposed architecture significantly reduces authentication latency during backhaul degradation and maintains high throughput and malicious node rejection rates compared to standard practical Byzantine fault tolerance algorithms.
References
Yao, Y., Shu, F., Li, Z., Cheng, X., & Wu, L. (2023). Secure transmission scheme based on joint radar and communication in mobile vehicular networks. IEEE transactions on intelligent transportation systems, 24 (9), 10027-10037.
Xu, Y., Trine, M. Q., & Wu, H. (2026). DARC-Sched: A Data-Aware Multi-Agent Large Language Model Framework for Scalable Software Vulnerability Detection and Repair with Optimized Code Data Scheduling. Fundamental Scientific Reports in Multidisciplinary Areas, 2(02), 269-283.
He, S., Wang, R., Liao, Q., Song, L., & Chen, Y. (2026, April). A Multi-Dimensional Regulatory Technology Model for Trusted AI Systems in Government Domains. In Proceedings of the 3rd International Conference on Machine Intelligence and Digital Applications (pp. 2045-2051).
Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
Zhang, H., Wang, Z., Zheng, S., Xiao, Z., Guan, X., Zandsalimy, M., & Sushmita, S. (2026). Recover, Decode, Reguard: Guard-Agnostic Defense Amplification against Encoded VLM Jailbreaks. arXiv e-prints, arXiv-2607.
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. In Proceedings of the IEEE Symposium on Security and Privacy.
Liu, J., & Huang, C. T. (2021, July). Efficient and trustworthy authentication in 5g networks based on blockchain. In 2021 International Conference on Computer Communications and Networks (ICCCN) (pp. 1-6). IEEE.
Hu, Y., Yang, Z., Zhao, C., & Ji, W. (2026, May). Adaptive guidance semantically enhanced via multimodal llm for edge-cloud object detection. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12962-12966). IEEE.
Meng, X., & Zhu, L. (2024). Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework. Sustainable Cities and Society, 106, 105336.
Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
Yao, Y., Zhang, J., Miao, P., Zhang, L., Chen, G., Shu, F., & Wong, K. K. (2025). Hybrid RIS-enhanced ISAC secure systems: Joint optimization in the presence of an extended target. IEEE Transactions on Communications.
Zou, Y., Zhang, Z., Pan, P., Zhou, Y., Liu, Q., & Lin, Y. (2026, August). LLM-Guided Temporal Graph Neural Network for Illicit Transaction Detection in Blockchain Networks. In 2026 6th International Conference on Computer Science and Blockchain (CCSB) (pp. 67-71). IEEE.
Guan, Z., Li, E., Wang, Y., Wang, Z., & Chen, Y. (2026, February). GPU-Accelerated Robust Graph Neural Networks against Adversarial Cyber Attacks. In Proceedings of the 2nd International Conference on Digital Management and Information Technology (pp. 976-984).
Qiu, M., Wu, L., & Wang, S. (2026). CyberDQFormer: A Knowledge Graph-Enhanced Transformer Framework for Security-Aware Data Quality Anomaly Detection and Autonomous Repair Against Network Attacks. Fundamental Scientific Reports in Multidisciplinary Areas, 2(3), 34-52.
Hu, X., Wan, Z., & Yin, Q. (2025). Token Design for Favor Trading with Dynamic Membership. Available at SSRN 6175079.
Zhang, H., Zandsalimy, M., & Sushmita, S. (2026). Exposing LLM Safety Gaps Through Mathematical Encoding: New Attacks and Systematic Analysis. arXiv preprint arXiv:2605.03441.
Liu, Fuqiang, and Sicong Jiang. "Temporally sparse attack for fooling large language models in time series forecasting." (2025).
Hao, Y., Xu, J., & Liao, S. (2026). RAPS-Net: A Risk-Aware CNN-LSTM Framework for Cross-Domain Risk Prediction and Dynamic Security Control in Cloud Payment Supply Chains. Economics and Management Innovation, 3(2), 18-27.
Liao, Q., Chen, Y., He, S., Wang, R., Xu, W., & Chu, W. (2025). Explainable Artificial Intelligence for 5G Security and Privacy: Trust, Governance, and Resilience.
Yao, Y., Xiao, W., Miao, P., Chen, G., Yang, H., Chae, C. B., & Wong, K. K. (2025). UAV-relay-aided secure maritime networks coexisting with satellite networks: robust beamforming and trajectory optimization. IEEE Transactions on Wireless Communications.
Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012). Surface codes: Towards practical large-scale quantum computation. Physical Review A, 86(3), 032324.
Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
Hu, X., & Caldentey, R. (2023). Trust and reciprocity in firms’ capacity sharing. Manufacturing & Service Operations Management, 25(4), 1436-1450.
Wang, Z., Zhang, H., & Hou, J. (2026, July). CPEMatch: A Controlled Benchmark for LLM Reasoning Over CPE/purl Vulnerability-Identifier Applicability. In 2026 5th International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) (pp. 1025-1031). IEEE.
Meng, X., & Lin, J. (2026). AURA-Trace: Auditable Provenance-Aware Synthetic Release of Campus IoT Time Series for Privacy-Preserving AI4Science. IEEE Internet of Things Journal.
Ding, M., Gao, S., & Zhao, Q. (2025, December). Stopping Rules for Sampling in Precision Medicine. In 2025 Winter Simulation Conference (WSC) (pp. 929-940). IEEE.
Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
Du, J., Gao, S., & Chen, C. H. (2024). A contextual ranking and selection method for personalized medicine. Manufacturing & Service Operations Management, 26(1), 167-181.
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Software Engineering and Artificial Intelligence Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
