Integrated vehicular and low-altitude Internet of Things networks require stable trust regulation under dynamic mobility, interference, and adversarial conditions. In this study, a unified edge intelligence approach is proposed to address the trustworthiness issue in modern edge intelligence. A validated constraint-consistent state information is preserved instead of raw data from multiple information sources to guide accurate information procurement and reserve-aware decision making. Vehicles, UAVs, roadside units, and edge nodes in the system are organized into shadow states corresponding to their real counterparts in a shared state space. Processed multi-source state information is represented as bounded and non-invertible representations that are structurally consistent and can be used for decision making without reconstructing the corresponding raw data. A cross-domain admissibility region is first determined by physical constraints, communication constraints, and mission constraints in the constraint domain. The geometric domain state evaluation stage then determines the scores of time and space geometric admissibility using multiple types of aerial, vehicle, and infrastructure heterogeneous witness information. Simulation results show that CODE achieves a Trustworthiness Index of 0.927, a maximum Coherence Consistency Score of 0.962, decision latency within 12.3–25.8 ms, and stability of 0.826 under high-density and multi-attack conditions.

Vehicular networks face increasing challenges to ensure security, privacy, and efficiency in dynamic communication environments.Current solutions often lack adaptability to evolving threats and efficient mechanisms for preserving privacy and reducing computational overhead.This study proposes PrivNet, a framework that integrates generative AI with advanced cryptographic and trust mechanisms to address these limitations.The framework comprises the Quantum-Augmented Holographic Cryptographic System (QAHCS) for dynamic and secure key generation, the Neural Overlap Privacy System (NOPS) for adaptive pseudonym morphing and entropy-driven identity obfuscation, and the Self-Supervised Generative Anomaly Detection (SS-GAI) module for real-time threat modeling and counter-anomaly injection.The system also incorporates Hyperledger Mesh for energy-efficient and secure transaction validation.Simulations were performed using NSL-KDD, CICIDS2017, and Car-Hacking / VeReMi datasets for 300 minutes.The results demonstrate a 12% improvement in detection accuracy, a 23% improvement in energy efficiency, and a 22% reduction in resource utilization.

ABSTRACT Functional requirements (FRs) prioritization is process of ranking of software FRs from development perspective such that which requirement to be implemented first and which should not. FRs prioritization is necessary as these requirements are interrelated such that one requirement is necessary for the implementation of another requirement. Also, when two parallel developers work on interrelated dependent requirements, requirements must be prioritized. Prioritizing small size requirements is not a big issue due to a fewer number of comparisons but when developers implement large size requirements such as enterprise resource planning (ERP), it requires a huge number of comparisons. Numerous techniques are suggested for FRs prioritization such as AHP, which yield more accurate results, but these techniques are not scalable for large size software requirements. In this research paper, a new prioritization approach based on graph and k‐means clustering is suggested that will capture all dependencies from a list of FRs using a directed graph and then prioritize it with a clustering technique with fewer comparisons. The proposed technique based on directed graph and clustering approach is validated on ODOO ERP, which shows that with n‐1 pairwise comparisons, requirements can be prioritized.