LCUT-Sv9: UAV-Assisted Powerline Inspection Framework with Secure Time-Sensitive Communication for Industry 5.0

LCUT-Sv9: UAV-Assisted Powerline Inspection Framework with Secure Time-Sensitive Communication for Industry 5.0

Integrating Time-Sensitive Networking (TSN) in industrial wireless networks ensures reliability in data transmission. Automated powerline inspection using Unmanned Aerial Vehicles (UAVs) is an industrial application that requires time-constrained secure data exchanges with its ground station. Howeve...

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Bibliographic Details
Main Authors: Arun Kumar Sangaiah, Jayakrishnan Anandakrishnan, Nguyen Khanh Son, Hendri Darmawan, Gui-Bin Bian, Mohammed J. F. Alenazi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of the Communications Society
Subjects:
Industrial wireless network
time-sensitive networking
Industry 5.0
IoT
UAVs
YOLO
Online Access:https://ieeexplore.ieee.org/document/10859272/
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Summary:Integrating Time-Sensitive Networking (TSN) in industrial wireless networks ensures reliability in data transmission. Automated powerline inspection using Unmanned Aerial Vehicles (UAVs) is an industrial application that requires time-constrained secure data exchanges with its ground station. However it is currently facing several critical challenges include handling massive data generated from onboard sensor, the limited computational capabilities, and ensuring secure communication over open wireless channels. Aiming to emphasize human-centric automation for enhanced operational safety and in alignment with the principles of Industry 5.0, this paper introduces a time-sensitive communication and detection framework for UAVIndustrial applications, named Latent-Coded UAV-IoT Transmission Segmentation YOLOv9 (LCUT-Sv9). It employs a modified lightweight Tiny-YOLOv9 model for powerline detection and utilizes autoencoder-based Latent-Coded UAV-IoT Transmission (LCUT) with the Message Queuing Telemetry Transport (MQTT) protocol for time-sensitive communication. The LCUT-Sv9 framework was evaluated using the widely adopted Transmission Towers / Power Lines Aerial-image (TTPLA) dataset, and demonstrated a significant detection accuracy improvement of 24.15 % over state-of-the-art (SOTA) techniques. LCUT also ensures minimal data transmission, reducing encoded data by approximately 50 %, making it compatible with the strict requirements of TSN-enabled industrial wireless networks.
ISSN:2644-125X

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