A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks

To 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...

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Main Authors: Junfei Xie, Yan Wan, Baoqian Wang, Shengli Fu, Kejie Lu, Jae H. Kim
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8325272/
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author Junfei Xie
Yan Wan
Baoqian Wang
Shengli Fu
Kejie Lu
Jae H. Kim
author_facet Junfei Xie
Yan Wan
Baoqian Wang
Shengli Fu
Kejie Lu
Jae H. Kim
author_sort Junfei Xie
collection DOAJ
description To 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.
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spelling doaj.art-4c34a1fd92024cc884cb282c0222f4f92022-12-21T23:26:41ZengIEEEIEEE Access2169-35362018-01-016228492286210.1109/ACCESS.2018.28196008325272A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne NetworksJunfei Xie0https://orcid.org/0000-0001-7406-3221Yan Wan1Baoqian Wang2Shengli Fu3Kejie Lu4Jae H. Kim5Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX, USADepartment of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, USADepartment of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX, USADepartment of Electrical Engineering, University of North Texas, Denton, TX, USADepartment of Computer Science and Engineering, University of Puerto Rico at Mayagüez, Mayagüez, Puerto RicoBoeing Research & Technology, Seattle, WA, USATo 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.https://ieeexplore.ieee.org/document/8325272/Modelingparameter estimationrandom mobility modelstochastic systemsunmanned aerial vehicles
spellingShingle Junfei Xie
Yan Wan
Baoqian Wang
Shengli Fu
Kejie Lu
Jae H. Kim
A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks
IEEE Access
Modeling
parameter estimation
random mobility model
stochastic systems
unmanned aerial vehicles
title A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks
title_full A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks
title_fullStr A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks
title_full_unstemmed A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks
title_short A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks
title_sort comprehensive 3 dimensional random mobility modeling framework for airborne networks
topic Modeling
parameter estimation
random mobility model
stochastic systems
unmanned aerial vehicles
url https://ieeexplore.ieee.org/document/8325272/
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