Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics
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 t...
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ftdoajarticles:oai:doaj.org/article:57510946548a4795b937d80c8a8059fb 2023-05-15T17:35:15+02:00 Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics Jingjing Cui Halil Yetgin Dong Liu Jiankang Zhang Soon Xin Ng Lajos Hanzo 2021-01-01T00:00:00Z https://doi.org/10.1109/OJVT.2021.3095467 https://doaj.org/article/57510946548a4795b937d80c8a8059fb EN eng IEEE https://ieeexplore.ieee.org/document/9477104/ https://doaj.org/toc/2644-1330 2644-1330 doi:10.1109/OJVT.2021.3095467 https://doaj.org/article/57510946548a4795b937d80c8a8059fb IEEE Open Journal of Vehicular Technology, Vol 2, Pp 346-364 (2021) Aeronautical ad hoc networks (AANETs) in-flight connectivity multi-objective combinatorial optimization problem (MOCOP) multi-objective evolutionary algorithm (MOEA) Transportation engineering TA1001-1280 Transportation and communications HE1-9990 article 2021 ftdoajarticles https://doi.org/10.1109/OJVT.2021.3095467 2022-12-31T13:12:22Z 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. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles IEEE Open Journal of Vehicular Technology 2 346 364 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Aeronautical ad hoc networks (AANETs) in-flight connectivity multi-objective combinatorial optimization problem (MOCOP) multi-objective evolutionary algorithm (MOEA) Transportation engineering TA1001-1280 Transportation and communications HE1-9990 |
spellingShingle |
Aeronautical ad hoc networks (AANETs) in-flight connectivity multi-objective combinatorial optimization problem (MOCOP) multi-objective evolutionary algorithm (MOEA) Transportation engineering TA1001-1280 Transportation and communications HE1-9990 Jingjing Cui Halil Yetgin Dong Liu Jiankang Zhang Soon Xin Ng Lajos Hanzo Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics |
topic_facet |
Aeronautical ad hoc networks (AANETs) in-flight connectivity multi-objective combinatorial optimization problem (MOCOP) multi-objective evolutionary algorithm (MOEA) Transportation engineering TA1001-1280 Transportation and communications HE1-9990 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Jingjing Cui Halil Yetgin Dong Liu Jiankang Zhang Soon Xin Ng Lajos Hanzo |
author_facet |
Jingjing Cui Halil Yetgin Dong Liu Jiankang Zhang Soon Xin Ng Lajos Hanzo |
author_sort |
Jingjing Cui |
title |
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics |
title_short |
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics |
title_full |
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics |
title_fullStr |
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics |
title_full_unstemmed |
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics |
title_sort |
twin-component near-pareto routing optimization for aanets in the north-atlantic region relying on real flight statistics |
publisher |
IEEE |
publishDate |
2021 |
url |
https://doi.org/10.1109/OJVT.2021.3095467 https://doaj.org/article/57510946548a4795b937d80c8a8059fb |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
IEEE Open Journal of Vehicular Technology, Vol 2, Pp 346-364 (2021) |
op_relation |
https://ieeexplore.ieee.org/document/9477104/ https://doaj.org/toc/2644-1330 2644-1330 doi:10.1109/OJVT.2021.3095467 https://doaj.org/article/57510946548a4795b937d80c8a8059fb |
op_doi |
https://doi.org/10.1109/OJVT.2021.3095467 |
container_title |
IEEE Open Journal of Vehicular Technology |
container_volume |
2 |
container_start_page |
346 |
op_container_end_page |
364 |
_version_ |
1766134353721556992 |