A comprehensive 3-dimensional random mobility modeling framework for airborne networks

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dc.contributor.authorXie, Junfei
dc.contributor.authorWan, Yan
dc.contributor.authorWang, Baoqian
dc.contributor.authorFu, Shengli
dc.contributor.authorLu, Kejie
dc.contributor.authorKim, Jae H.
dc.creator.orcidhttps://orcid.org/0000-0001-7406-3221en_US
dc.creator.orcidhttp://orcid.org/0000-0002-1977-5100en_US
dc.creator.orcidhttps://orcid.org/0000-0001-7406-3221
dc.creator.orcidhttps://orcid.org/0000-0002-1977-5100
dc.creator.orcidhttps://orcid.org/0000-0001-7406-3221
dc.creator.orcidhttps://orcid.org/0000-0002-1977-5100https://orcid.org/0000-0001-7406-3221
dc.creator.orcidhttp://orcid.org/0000-0002-1977-5100
dc.creator.orcidhttps://orcid.org/0000-0001-7406-3221
dc.creator.orcidhttps://orcid.org/0000-0002-1977-5100
dc.creator.orcidhttps://orcid.org/0000-0001-7406-3221
dc.creator.orcidhttp://orcid.org/0000-0002-1977-5100
dc.date.accessioned2022-03-22T21:28:20Z
dc.date.available2022-03-22T21:28:20Z
dc.date.issued2018-03-26
dc.description.abstractTo design and evaluate airborne networks (ANs), it is crucial to utilize random mobility models (RMMs) that capture the physical movement patterns of different aerial vehicles in real scenarios. Compared to expensive flight field tests, RMM-based modeling, simulation, and emulation is cost-effective with a large set of RMM-generated flight trajectories. Despite the importance of RMMs, we notice that most existing models focus on the 2-D movement, and do not consider the temporal and 3-D spatial correlation of aerial mobility patterns. In this paper, we propose a comprehensive 3-D smooth turn (ST) modeling framework for fixed-wing aircraft, which can serve as a design and evaluation foundation for future ANs. In the proposed framework, we develop two realistic 3-D ST RMMs that capture the diverse mobility patterns of fixed-wing aircraft, through coupling stochastic forcing with physical laws that govern the 3-D aerial maneuvers. We also develop two boundary models to determine the movement of aerial vehicles when they approach simulation boundaries. Moreover, we propose an approach to estimate the optimal 3-D ST RMMs, with which we can produce rich trajectory ensembles with statistical mobility patterns that match with the real trajectory data.en_US
dc.description.abstractTo design and evaluate airborne networks (ANs), it is crucial to utilize random mobility models (RMMs) that capture the physical movement patterns of different aerial vehicles in real scenarios. Compared to expensive flight field tests, RMM-based modeling, simulation, and emulation is cost-effective with a large set of RMM-generated flight trajectories. Despite the importance of RMMs, we notice that most existing models focus on the 2-D movement, and do not consider the temporal and 3-D spatial correlation of aerial mobility patterns. In this paper, we propose a comprehensive 3-D smooth turn (ST) modeling framework for fixed-wing aircraft, which can serve as a design and evaluation foundation for future ANs. In the proposed framework, we develop two realistic 3-D ST RMMs that capture the diverse mobility patterns of fixed-wing aircraft, through coupling stochastic forcing with physical laws that govern the 3-D aerial maneuvers. We also develop two boundary models to determine the movement of aerial vehicles when they approach simulation boundaries. Moreover, we propose an approach to estimate the optimal 3-D ST RMMs, with which we can produce rich trajectory ensembles with statistical mobility patterns that match with the real trajectory data.
dc.description.sponsorshipThis work was supported in part by the Texas Comprehensive Research Fund Program Grant and in part by the National Science Foundation under Grant CAREER-1714519, Grant CI-1730675 (CI-1730589, CI-1730570, and CI-1730325), and Grant CNS-1522458en_US
dc.description.sponsorshipThis work was supported in part by the Texas Comprehensive Research Fund Program Grant and in part by the National Science Foundation under Grant CAREER-1714519, Grant CI-1730675 (CI-1730589, CI-1730570, and CI-1730325), and Grant CNS-1522458
dc.identifier.citationXie, J., Wan, Y., Wang, B., Fu, S., Lu, K. and Kim, J.H., 2018. A comprehensive 3-dimensional random mobility modeling framework for airborne networks. IEEE Access, 6, pp.22849-22862.en_US
dc.identifier.citationXie, J., Wan, Y., Wang, B., Fu, S., Lu, K. and Kim, J.H., 2018. A comprehensive 3-dimensional random mobility modeling framework for airborne networks. IEEE Access, 6, pp.22849-22862.
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2018.2819600
dc.identifier.urihttps://hdl.handle.net/1969.6/90308
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherIEEEen_US
dc.publisherIEEE
dc.subjectatmospheric modelingen_US
dc.subjectaircraften_US
dc.subjectsolid modelingen_US
dc.subjecttrajectoryen_US
dc.subjectcorrelationen_US
dc.subjectaerodynamicsen_US
dc.subjectmilitary aircraften_US
dc.subjectatmospheric modeling
dc.subjectaircraft
dc.subjectsolid modeling
dc.subjecttrajectory
dc.subjectcorrelation
dc.subjectaerodynamics
dc.subjectmilitary aircraft
dc.titleA comprehensive 3-dimensional random mobility modeling framework for airborne networksen_US
dc.titleA comprehensive 3-dimensional random mobility modeling framework for airborne networks
dc.typeArticleen_US
dc.typeArticle

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