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43 articles for “Aerial vehicles”
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Recent Routing Protocols in UAV Networks: Classifications, Challenges, and Future Directions
Abstract: Routing protocols enable reliable communication in Unmanned Aerial Vehicle (UAV) networks, particularly Flying Ad-hoc Networks (FANETs), amid high-speed 3D mobility, dynamic topologies, and energy limits. Key challenges include intermittent links due to mobility, limited energy resources, variable network density, and the need to minimize end-to-end delay while maximizing throughput and fault tolerance. This paper classifies recent UAV routing protocols into topology-based routing protocols, position-based routing protocols, and hierarchical-based routing protocols. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 2, 2026 Read article
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Holographic Beam Switching and Intelligent Routing for Terahertz Space-Air-Ground Integrated Networks
Abstract: The rapid evolution of sixth-generation (6G) and beyond communication technologies necessitates highly adaptive, ultra-high-capacity, and low-latency networking frameworks capable of supporting global connectivity across terrestrial and non-terrestrial domains. This study proposes a novel holographic beam switching and intelligent routing framework for terahertz (THz) Space-Air-Ground Integrated Networks (SAGINs). The proposed architecture leverages holographic beamforming techniques to dynamically manipulate electromagnetic wavefronts, enabling precise beam steering, reduced interference, and enhanced spectral efficiency in …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Transfer Learning Based High-Precision Multi-Class Object Detection for Real-Time UAV Autonomous Landing via YOLOv8l in Unstructured Scenarios
Abstract: A significant challenge for autonomous drone landings in unstructured environments is that of reliably detecting and identifying objects in real-time to ensure safety and accuracy of the landing area. This paper presents a well-founded method for solving this problem using the YOLOv8l object detection framework to detect landing zones, obstacles and people in the relevant vicinity of the landing area. The dataset used for the training of the model contained …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article