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

Twin-component near-Pareto routing optimization for AANETs in the North-Atlantic region relying on real flight statistics
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 the sky. For meeting the latency and reliability requirements of in-flight connectivity, we formulate a multi-objective multi-hop routing problem in aeronautical \emph{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.
2644-1330
Cui, Jingjing
dbe3c3ed-762f-4abf-bd7b-8d2737f2f0fc
Yetgin, Halil
61dca21c-f273-4e17-81e7-16abcfb4cd32
Liu, Dong
889643f2-afeb-4479-bd41-3ccedd53d89d
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Cui, Jingjing
dbe3c3ed-762f-4abf-bd7b-8d2737f2f0fc
Yetgin, Halil
61dca21c-f273-4e17-81e7-16abcfb4cd32
Liu, Dong
889643f2-afeb-4479-bd41-3ccedd53d89d
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Cui, Jingjing, Yetgin, Halil, Liu, Dong, Zhang, Jiankang, Ng, Soon Xin and Hanzo, Lajos (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. (In Press)

Record type: Article

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 \emph{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.

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Accepted/In Press date: 4 July 2021

Identifiers

Local EPrints ID: 450278
URI: http://eprints.soton.ac.uk/id/eprint/450278
ISSN: 2644-1330
PURE UUID: 39c4800b-f716-4b15-b59c-ea898f428aab
ORCID for Dong Liu: ORCID iD orcid.org/0000-0002-0619-1480
ORCID for Jiankang Zhang: ORCID iD orcid.org/0000-0001-5316-1711
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 20 Jul 2021 16:31
Last modified: 21 Jul 2021 02:02

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Contributors

Author: Jingjing Cui
Author: Halil Yetgin
Author: Dong Liu ORCID iD
Author: Jiankang Zhang ORCID iD
Author: Soon Xin Ng ORCID iD
Author: Lajos Hanzo ORCID iD

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