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Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics.

Cui, J., Yetgin, H., Liu, D., Zhang, J. 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, 346-364.

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Twin-Component_Near-Pareto_Routing_Optimization_for_AANETs_in_the_North-Atlantic_Region_Relying_on_Real_Flight_Statistics.pdf - Published Version
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OJVT-Twin-Component_Near-Pareto_Routing_Optimization_fo.pdf - Accepted Version
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Available under License Creative Commons Attribution.


DOI: 10.1109/OJVT.2021.3095467


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.

Item Type:Article
Additional Information:L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EPP0342841 and EPP0039901 COALESCE as well as of the European Research Councils Advanced Fellow Grant QuantCom Grant No. 789028
Uncontrolled Keywords:Aeronautical ad hoc networks (AANETs); in-flight connectivity; multi-objective combinatorial optimization problem (MOCOP); multi-objective evolutionary algorithm (MOEA)
Group:Faculty of Science & Technology
ID Code:35749
Deposited By: Symplectic RT2
Deposited On:09 Jul 2021 11:58
Last Modified:14 Mar 2022 14:28


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