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无人机网络中的优化与通信

Optimization and Communication in UAV Networks.

作者信息

Caillouet Christelle, Mitton Nathalie

机构信息

Inria, 40 Avenue Halley, 59650 Villeneuve d'Ascq, France.

Department INFO of IUT Nice Côte d'Azur, Université Côte d'Azur, CNRS, I3S, 2004 Route des Lucioles, 06902 Sophia Antipolis, France.

出版信息

Sensors (Basel). 2020 Sep 4;20(18):5036. doi: 10.3390/s20185036.

Abstract

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Current trends focus on deployment of a single UAV or a swarm of it to generally map an area, perform surveillance, monitoring or rescue operations, collect data from ground sensors or various communicating devices, provide additional computing services close to data producers, etc. Applications are very diverse and call for different features or requirements. But UAV remain low-power battery powered devices that in addition to their mission, must fly and communicate. Thanks to wireless communications, they participate to mobile dynamic networks composed of UAV and ground sensors and thus many challenges have to be addressed to make UAV very efficient. And behind any UAV application, hides an optimization problem. There is still a criterion or multiple ones to optimize such as flying time, energy consumption, number of UAV, quantity of data to send/receive, etc.

摘要

如今,无人机(UAV)在物联网(IoT)中越来越受欢迎,物联网通常在相对广阔的区域部署许多传感器。当前的趋势集中在部署单个无人机或一群无人机,以大致绘制一个区域、执行监视、监测或救援行动、从地面传感器或各种通信设备收集数据、在靠近数据生产者的地方提供额外的计算服务等。应用非常多样,需要不同的功能或要求。但无人机仍然是低功率电池供电设备,除了执行任务外,还必须飞行和通信。由于无线通信,它们参与由无人机和地面传感器组成的移动动态网络,因此要使无人机非常高效,必须解决许多挑战。而且在任何无人机应用背后,都隐藏着一个优化问题。仍然有一个或多个标准需要优化,例如飞行时间、能耗、无人机数量、发送/接收的数据量等。

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本文引用的文献

1
A Decentralized Low-Chattering Sliding Mode Formation Flight Controller for a Swarm of UAVs.
Sensors (Basel). 2020 May 30;20(11):3094. doi: 10.3390/s20113094.
2
Priority-Based Data Collection for UAV-Aided Mobile Sensor Network.
Sensors (Basel). 2020 May 27;20(11):3034. doi: 10.3390/s20113034.
3
Energy-Aware Management in Multi-UAV Deployments: Modelling and Strategies.
Sensors (Basel). 2020 May 14;20(10):2791. doi: 10.3390/s20102791.
5
Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective.
Sensors (Basel). 2020 Apr 13;20(8):2191. doi: 10.3390/s20082191.
8
Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs.
Sensors (Basel). 2019 Nov 8;19(22):4884. doi: 10.3390/s19224884.
10
Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning.
Sensors (Basel). 2019 Sep 18;19(18):4032. doi: 10.3390/s19184032.

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