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

Xie, Junfei; Wan, Yan; Wang, Baoqian; Fu, Shengli; Lu, Kejie; Kim, Jaehwan

doi:10.1109/access.2018.2819600

Cited by 29 publications

(17 citation statements)

References 28 publications

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“…Since field measurements would be extremely expensive for passenger planes engaged in different maneuvers in different network scenarios, stochastic and/or semi-stochastic mobility modeling may be more realistic solutions. However, random mobility modeling [207] typically used in FANETs may fail to accurately characterize AANETs, since the passenger airplanes' routes are typically pre-planned, hence pre-planned semistochastic mobility models may be developed for AANETs.…”

Section: A Channel and Phy Layermentioning

confidence: 99%

Aeronautical $Ad~Hoc$ Networking for the Internet-Above-the-Clouds

Chen²,

et al. 2019

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The engineering vision of relying on the "smart sky" for supporting air traffic and the "Internet above the clouds" for in-flight entertainment has become imperative for the future aircraft industry. Aeronautical ad hoc Networking (AANET) constitutes a compelling concept for providing broadband communications above clouds by extending the coverage of Air-to-Ground (A2G) networks to oceanic and remote airspace via autonomous and self-configured wireless networking amongst commercial passenger airplanes. The AANET concept may be viewed as a new member of the family of Mobile ad hoc Networks (MANETs) in action above the clouds. However, AANETs have more dynamic topologies, larger and more variable geographical network size, stricter security requirements and more hostile transmission conditions. These specific characteristics lead to more grave challenges in aircraft mobility modeling, aeronautical channel modeling and interference mitigation as well as in network scheduling and routing. This paper provides an overview of AANET solutions by characterizing the associated scenarios, requirements and challenges. Explicitly, the research addressing the key techniques of AANETs, such as their mobility models, network scheduling and routing, security and interference are reviewed. Furthermore, we also identify the remaining challenges associated with developing AANETs and present their prospective solutions as well as open issues. The design framework of AANETs and the key technical issues are investigated along with some recent research results. Furthermore, a range of performance metrics optimized in designing AANETs and a number of representative multi-objective optimization algorithms are outlined.

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Section: A Channel and Phy Layermentioning

confidence: 99%

Aeronautical $Ad~Hoc$ Networking for the Internet-Above-the-Clouds

Chen²,

et al. 2019

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“…Other works include the improved 3D Gauss-Markov model for dynamic and uncertain environments [25], namely 3D-DUMM. On the other hand, the author in [26] improved the random mobility model specifically for three-dimensional scenarios, where the model depends on the z axis direction (vertical movement).…”

Section: Related Workmentioning

confidence: 99%

UAV Mobility model for dynamic UAV-to-car communications in 3D environments

et al. 2020

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Junfie et al [23] introduced two 3-D smooth turn (ST) random mobility models (RMMs) to facilitate the design and evaluation of airborne networks (ANs). Both models are extended from the basic 2D Smooth Turn random mobility model, but differ in motion patterns along the z direction.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Random Direction-3D Mobility Model to Achieve Better QoS Support in MANET

Khan¹,

Das²

2020

IJACSA

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Mobile Ad hoc Networks (MANETs) provides changing network topology due to the mobility of nodes. The complexity of the network increases because of dynamic topology of nodes. In a MANET, nodes communicate with each other without the help of any infrastructure. Therefore, achieving QoS in MANET becomes a little difficult. The movement of mobile nodes is represented through mobility models. These models have great impact on QoS in MANET. We have proposed a mobility model which is a 3D implementation of existing Random Direction (RD) mobility model. We have done a simulation on AODV with QoS metrics throughput, delay and PDR, using NS-3 and performed analysis of the proposed mobility models with other 3D mobility models, namely Random Way Point (RWP) and Gauss Markov (GM). It is concluded that our proposed model gives better throughput, delay and PDR for AODV routing protocol in comparing to RWP and GM mobility models. This paper is for students and researchers who are involved in wireless technology and MANET. It will help them to understand how a mobility model impacts the entire network and how its enhancement improves the QoS in MANET.

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

Cited by 29 publications

References 28 publications

Aeronautical $Ad~Hoc$ Networking for the Internet-Above-the-Clouds

Aeronautical $Ad~Hoc$ Networking for the Internet-Above-the-Clouds

UAV Mobility model for dynamic UAV-to-car communications in 3D environments

Implementation of Random Direction-3D Mobility Model to Achieve Better QoS Support in MANET

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