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

Zeeshan Ali Haider; Inam Ullah; Akmalbek Abdusalomov; Mohsin Shah; Muhammad Zubair Khan; Basem Abu Zneid

doi:10.1016/j.jnlest.2026.100350

Journal of Electronic Science and Technology ›› 2026, Vol. 24 ›› Issue (1) :100350 DOI: 10.1016/j.jnlest.2026.100350

research-article

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

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^a Department of Computer Science, Qurtuba University of Science and Information Technology, Peshawar, 25000, Pakistan

^b Department of Computer Engineering, Gachon University, Seongnam, 13120, Republic of Korea

^c Department of Artificial Intelligence, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent, 100200, Uzbekistan

^d Department of Public Health, Health Services Academy, Islamabad, 44028, Pakistan

^e Faculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

^* E-mail addresses: Zeeshan.ali9049@gmail.com (Z.A. Haider),

inam@gachon.ac.kr (I. Ullah),

akmaljon@gachon.ac.kr (A. Abdusalomov),

syedmohsinshah@gmail.com (M. Shah),

dr.zubair.statistics@gmail.com (M.Z. Khan),

b.izneid@ammanu.edu.jo (B.A. Zneid).

Zeeshan Ali Haider received the M.S. degree in computer science (with distinction) from AbasynUniversity, Peshawar, Pakistan, and the B.S. degree in computer science from Islamia College, Peshawar, Pakistan. He is currently pursuing the Ph.D. degree in computer science at Qurtuba University of Science and Information Technology, Peshawar, Pakistan. His research focuses on emerging andinterdisciplinary technologies, particularly the Internet of vehicles (IoV), intelligent transportationsystems (ITS), unmanned aerial vehicles (UAV), Internet of things (IoT), artificial intelligence (AI),cybersecurity, cryptography, and blockchain technology.

Inam Ullah received the M.S. and Ph.D. degrees in information and communication engineering from Hohai University, Nanjing, China. He is currently an assistant professor at the Department of Computer Engineering, Gachon University, Seongnam, Republic of Korea. He has completed a postdoctoral fellowship from Chungbuk National University, Cheongju, Republic of Korea. His research field of interest is IoT, IoV, computing technologies, robotics, autonomous vehicles, ITS, computer networking, wireless sensor networks (WSNs), big data, AI, machine learning, and deep learning.

Akmalbek Abdusalomor received the B.S. degree in informatics and information technologies from Tashkent University of Information Technologies, Tashkent, Uzbekistan in 2015, and the M.S. and Ph.D. degrees in IT convergence engineering from Gachon University in 2017 and 2022, respectively. He is currently an assistant professor with the Computer Engineering Department, Gachon University. His research interests include object detection, AI (deep learning and machine learning), and medical image processing.

Mohsin Shah received the B.Sc. degree in telecommunication engineering from the University of Engineering and Technology Peshawar, Peshawar, Pakistan in 2007, the M.Sc. degree in telecommunication engineering from the University of Engineering and Technology, Taxila, Pakistan in 2012, and the Ph.D. degree in information and communication engineering from the University of Science and Technology of China, Chengdu, China in 2019. He is currently an assistant professor in the Department of Computer Engineering, Gachon University. His research interests include information hiding, multimedia security, secure signal processing, image segmentation, image forgery detection, and networks.

Muhammad Zubair Zubair is the director of Quality Assurance and professor biostatistics (BPS-21) at Health Services Academy (HSA), Islamabad. He is a former consultant at the national institute of population studies (NIPS), Ministry of Health. He served as the provincial director of the population Research Center, population council Islamabad, in Quetta for three years. He holds fully funded postgraduate degrees, including the Ph.D. degree in biostatistics, M.Phil. degree in financial statistics, and M.Sc. degree in statistics. His research interests include biostatistics, data analytics, public health, and consultancies to serve humanity.

Basem Abu Izneid received the Ph.D. degree in mechatronics from the University of Science Malaysia, Penang, Malaysia in 2010. Currently, he is an assistant professor in the Faculty of Engineering at Al-Ahliyya Amman University, Amman, Jordan. His research interests include intelligent sensors, smart robotics, AI, remote control and monitoring utilizing Internet of Things, and non-destructive measurement techniques.

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Abstract

The intelligent transportation systems require secure, low-latency, and reliable communication architectures to enable the real-time vehicular application. This 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 under heavy-traffic conditions. The proposed framework provides a flexible and 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.

Keywords

Blockchain / Edge intelligence / Internet of vehicles (IoV) / Reinforcement learning / Semantic communication / Unmanned aerial vehicle (UAV) / 6G

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Zeeshan Ali Haider, Inam Ullah, Akmalbek Abdusalomov, Mohsin Shah, Muhammad Zubair Khan, Basem Abu Zneid. Edge-intelligent semantic aggregation in blockchain-secured 6G UAV-assisted Internet of vehicles. Journal of Electronic Science and Technology, 2026, 24(1): 100350 DOI:10.1016/j.jnlest.2026.100350

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CRediT authorship contribution statement

Zeeshan Ali Haider: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Inam Ullah: Conceptualization, Supervision, Project administration, Funding acquisition, Methodology, Writing – review & editing, Validation. Akmalbek Abdusalomov: Methodology, Software, Formal analysis, Investigation, Writing – review & editing. Mohsin Shah: Investigation, Data curation, Formal analysis, Writing – review & editing. Muhammad Zubair Khan: Resources, Investigation, Validation, Writing – review & editing. Basem Abu Zneid: Resources, Validation, Supervision, Writing – review & editing.

Declaration of competing interest

Inam Ullah is a special section committee member for Journal of Electronic Science and Technology and was not involved in the editorial review or the decision to publish this article. Other authors declare that there are no competing interests.

Acknowledgment

This work was supported by the IITP (Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government (Ministry of Science and ICT)(IITP-2026-RS-2023-00259004). This work was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2025-25434261).

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