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A metaheuristic approach for mission assignment and task offloading in Open RAN-enabled intelligent transport systems

Nguyen, Ngoc Hung LU orcid ; Van Thieu, Nguyen ; Luu, Quang-Trung ; Son, Vo Phi and Nguyen, Van-Dinh (2025) p.811-816
Abstract
We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses... (More)
We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
GLOBECOM 2025 - 2025 IEEE Global Communications Conference : 8-12 Dec. 2025 - 8-12 Dec. 2025
pages
811 - 816
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
DOI
10.1109/GLOBECOM59602.2025.11431892
language
English
LU publication?
yes
id
1d32516c-fcf4-4f75-aba7-f26199d678eb
date added to LUP
2026-03-24 20:29:23
date last changed
2026-03-26 10:34:43
@inproceedings{1d32516c-fcf4-4f75-aba7-f26199d678eb,
  abstract     = {{We explore mission assignment and task offloading in Open Radio Access Network (Open RAN)-enabled intelligent transportation systems (ITS), where autonomous vehicles utilize mobile edge computing for efficient processing. Existing studies often overlook mission dependencies and offloading costs, leading to suboptimal decisions. To address this, we formulate a novel optimization problem that integrates these factors and enhances performance through vehicle cooperation. We then develop the chaotic Gaussian-based global artificial rabbit optimization (CG-GARO) algorithm, a new metaheuristic approach, which significantly improves mission assignment efficiency and resource utilization. Simulation results show that our approach surpasses baseline metaheuristics in both system benefits and mission completion rates, demonstrating strong potential for real-world deployment in dynamic ITS environments.}},
  author       = {{Nguyen, Ngoc Hung and Van Thieu, Nguyen and Luu, Quang-Trung and Son, Vo Phi and Nguyen, Van-Dinh}},
  booktitle    = {{GLOBECOM 2025 - 2025 IEEE Global Communications Conference : 8-12 Dec. 2025}},
  language     = {{eng}},
  month        = {{12}},
  pages        = {{811--816}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{A metaheuristic approach for mission assignment and task offloading in Open RAN-enabled intelligent transport systems}},
  url          = {{http://dx.doi.org/10.1109/GLOBECOM59602.2025.11431892}},
  doi          = {{10.1109/GLOBECOM59602.2025.11431892}},
  year         = {{2025}},
}