Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics
Authors: Cui, J., Yetgin, H., Liu, D., Zhang, J., Ng, S.X. and Hanzo, L.
Journal: IEEE Open Journal of Vehicular Technology
Volume: 2
Pages: 346-364
eISSN: 2644-1330
DOI: 10.1109/OJVT.2021.3095467
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.
https://eprints.bournemouth.ac.uk/35749/
Source: Scopus
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics
Authors: Cui, J., Yetgin, H., Liu, D., Zhang, J., Ng, S.X. and Hanzo, L.
Journal: IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY
Volume: 2
Pages: 346-364
eISSN: 2644-1330
DOI: 10.1109/OJVT.2021.3095467
https://eprints.bournemouth.ac.uk/35749/
Source: Web of Science (Lite)
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics
Authors: Cui, J., Yetgin, H., Liu, D., Zhang, J., Ng, S.X. and Hanzo, L.
Journal: IEEE Open Journal of Vehicular Technology
https://eprints.bournemouth.ac.uk/35749/
Source: Manual
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics
Authors: Cui, J., Yetgin, H., Liu, D., Zhang, J.J., Ng, S.X. and Hanzo, L.
Journal: IEEE Open Journal of Vehicular Technology
Volume: 2
Pages: 346-364
ISSN: 2644-1330
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.
https://eprints.bournemouth.ac.uk/35749/
Source: BURO EPrints