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无人机物联网：自组织空中网络中的QoS保障

Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks.

作者信息

Kumar Kirshna, Kumar Sushil, Kaiwartya Omprakash, Sikandar Ajay, Kharel Rupak, Mauri Jaime Lloret

机构信息

Jawaharlal Nehru University (JNU), New Delhi 110067, India.

School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK.

出版信息

Sensors (Basel). 2020 Jun 2;20(11):3160. doi: 10.3390/s20113160.

DOI:10.3390/s20113160
PMID:32498459
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7308874/
Abstract

Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV's aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.

摘要

空中自组织网络有潜力在维持地面系统与无人机（UAV）之间通信的同时，实现智能服务。先前的研究聚焦于在敌对和非敌对环境中整合无人机等空中物体的优势，从而实现以空中数据为中心的智能服务。无人机辅助通信中的服务质量（QoS）保障是空中自组织网络环境中一个具有挑战性的研究课题。关于空中自组织网络的文献缺乏针对分布式网络环境的面向协作服务的建模，而是依赖于成本高昂的面向静态基站的集中式网络环境。为此，本文提出了一种针对无人机辅助的空中自组织网络环境的服务质量保障框架（QSPU），重点关注可靠的空中通信。考虑到高度动态的空中自组织环境，对无人机的空中移动性和服务参数进行了建模。以无人机为中心的移动性模型被用于开发一个完整的空中路由框架。对比性能评估展示了所提出的空中通信框架的优势。显然，在无人机辅助的空中自组织网络环境中，就一些面向服务的性能指标而言，QSPU优于现有技术。

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2
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3
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4
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Sensors (Basel). 2023 Jan 8;23(2):723. doi: 10.3390/s23020723.
5
Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network.无人机支持的海上网络的联合资源管理和轨迹优化。
Sensors (Basel). 2022 Dec 13;22(24):9763. doi: 10.3390/s22249763.
6
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Sensors (Basel). 2022 Jul 22;22(15):5477. doi: 10.3390/s22155477.
7
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Sensors (Basel). 2022 Mar 29;22(7):2620. doi: 10.3390/s22072620.
8
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10
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4
Optimizing 802.15.4 Outdoor IoT Sensor Networks for Aerial Data Collection.优化用于空中数据采集的802.15.4室外物联网传感器网络。
Sensors (Basel). 2019 Aug 9;19(16):3479. doi: 10.3390/s19163479.