Twin-component near-pareto routing optimization for AANETs in the North-Atlantic Region relying on real flight statistics

Article


Cui, J., Yetgin, H., Liu, D., Zhang, J., Ng, S.X. and Hanzo, L. 2021. Twin-component near-pareto routing optimization for AANETs in the North-Atlantic Region relying on real flight statistics. IEEE Open Journal of Vehicular Technology. 2, pp. 346-364. https://doi.org/10.1109/OJVT.2021.3095467
TypeArticle
TitleTwin-component near-pareto routing optimization for AANETs in the North-Atlantic Region relying on real flight statistics
AuthorsCui, J., Yetgin, H., Liu, D., Zhang, J., Ng, S.X. and Hanzo, L.
Abstract

Integrated ground-air-space (IGAS) networks intrinsically amalgamate terrestrial and non-terrestrial communication techniques in support of universal connectivity across the globe. Multi-hop routing over the IGAS networks has the potential to provide long-distance highly directional connections in the sky. For meeting the latency and reliability requirements of in-flight connectivity, we formulate a multi-objective multi-hop routing problem in aeronautical ad hoc networks (AANETs) for concurrently optimizing multiple end-to-end performance metrics in terms of the total delay and the throughput. In contrast to single-objective optimization problems that may have a unique optimal solution, the problem formulated is a multi-objective combinatorial optimization problem (MOCOP), which generally has a set of trade-off solutions, called the Pareto optimal set. Due to the discrete structure of the MOCOP formulated, finding the Pareto optimal set becomes excessively complex for large-scale networks. Therefore, we employ a multi-objective evolutionary algorithm (MOEA), namely the classic NSGA-II for generating an approximation of the Pareto optimal set. Explicitly, with the intrinsic parallelism of MOEAs, the MOEA employed starts with a set of candidate solutions for creating and reproducing new solutions via genetic operators. Finally, we evaluate the MOCOP formulated for different networks generated both from simulated data as well as from real historical flight data. Our simulation results demonstrate that the utilized MOEA has the potential of finding the Pareto optimal solutions for small-scale networks, while also finding a set of high-performance nondominated solutions for large-scale networks.

KeywordsAeronautical ad hoc networks (AANETs); in-flight connectivity; multi-objective combinatorial optimization problem (MOCOP); multi-objective evolutionary algorithm (MOEA)
Sustainable Development Goals11 Sustainable cities and communities
Middlesex University ThemeSustainability
PublisherIEEE
JournalIEEE Open Journal of Vehicular Technology
ISSN
Electronic2644-1330
Publication dates
Online07 Jul 2021
Print11 Aug 2021
Publication process dates
Submitted12 Jun 2021
Accepted04 Jul 2021
Deposited05 Apr 2024
Output statusPublished
Publisher's version
License
File Access Level
Open
Copyright Statement

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Digital Object Identifier (DOI)https://doi.org/10.1109/OJVT.2021.3095467
Web of Science identifierWOS:000685212400001
LanguageEnglish
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