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Edge-intelligent semantic aggregation in blockchain-secured 6G UAV-assisted Internet of vehicles

Zeeshan Ali HaiderDepartment of Computer Science, Qurtuba University of Science and Information Technology, Peshawar, 25000, PakistanInam UllahDepartment of Computer Engineering, Gachon University, Seongnam, 13120, Republic of KoreaAkmalbek AbdusalomovDepartment of Artificial Intelligence, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent, 100200, UzbekistanMohsin ShahDepartment of Computer Engineering, Gachon University, Seongnam, 13120, Republic of KoreaMuhammad Zubair KhanFaculty of Engineering, Health Services Academy, Govt of Pakistan, Chak Shahzad, Islamabad, 44028, PakistanBasem Abu ZneidFaculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
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Abstract

The intelligent transportation systems require secure, low-latency, and reliable communication architectures to enable the real-time vehicular application. The paper proposes an edge-intelligent semantic aggregation (EISA) framework for 6G unmanned aerial vehicle (UAV)-assisted Internet of vehicles (IoV) networks that integrates task-driven semantic communication, deep reinforcement learning (DRL)-based edge intelligence, and blockchain-based semantic validation across 6G terahertz (THz) links. UAVs in the proposed architecture serve as adaptive edge nodes that receive semantically vital information about the vehicle at any given stage, optimize aggregation and transmission parameters dynamically, and guarantee data integrity through a structured, lightweight consortium blockchain that signs semantically detailed representations rather than raw packets. Simulation results from a hybrid NS-3, MATLAB, and Python environment indicate that the proposed framework can achieve up to 45% reduction in end-to-end latency, an approximately 70% increase in throughput, and semantic efficiency with blockchain verification delays of less than 20 ms (more than 98%). These findings support the effectiveness of the proposed co-design for achieving context-aware, energy-efficient, and reliable communication in heavy-traffic conditions. The proposed framework provides a flexible, scalable foundation for next-generation 6G-enabled automotive networks, with subsequent growth toward federated learning-based collaborative intelligence, digital-twin-assisted traffic modeling, and quantum-safe blockchain mechanisms to enhance scalability, intelligence, and long-term security.

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