Work

An intent aware framework for semantic edge intelligence in autonomous 6g UAV and vehicular networks

Year: 2026

Type: article

Abstract: Autonomous UAV–vehicular communication in 6G environments requires low-latency, context-aware, and scalable edge intelligence beyond conventional data-centric transmission models. This paper proposes SEM-EDGE-X, an intent-aware semantic edge intelligence framework that integrates semantic feature extraction, adaptive edge routing, predictive resource allocation, and multi-agent coordination for dynamic UAV–vehicular networks. The framework incorporates cold-start semantic fusion, uncertainty-aware human-in-the-loop reinforcement, semantic conflict resolution, and trust-driven security mechanisms to improve communication reliability and decision efficiency. SEM-EDGE-X is evaluated using a MATLAB/Python-based simulation environment with SUMO mobility modelling, KITTI and UAVDT datasets, and CNN-based semantic encoding under varying UAV and vehicular densities. Performance is analyzed using latency, semantic accuracy, semantic similarity, energy consumption, throughput, scalability, and SNR robustness metrics. Experimental results demonstrate that SEM-EDGE-X consistently outperforms conventional cloud, MEC, DeepSC, and AI-assisted edge frameworks in highly dynamic 6G communication environments

Source: Scientific Reports

DOI: 10.1038/s41598-026-76018-6

Authors: S. Salma Begum, Mudassir Khan, G. Suresh Babu, S. Raghavi, Sapiah Sakri

Institutions: Presidency University, King Khalid University, St. Joseph’s College of Engineering, Princess Nourah bint Abdulrahman University

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,690