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无人车在物联网和车联网中的部署优化。

Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles.

机构信息

Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania.

出版信息

Sensors (Basel). 2021 Oct 21;21(21):6984. doi: 10.3390/s21216984.

Abstract

Currently, the use of unmanned vehicles, such as drones, boats and ships, in monitoring tasks where human presence is difficult or even impossible raises several issues. Continuous efforts to improve the autonomy of such vehicles have not solved all aspects of this issue. In an Internet of Unmanned Vehicles (IoUV) environment, the idea of replacing the static wireless infrastructure and reusing the mobile monitoring nodes in different conditions would converge to a dynamic solution to assure data collection in areas where there is no infrastructure that ensures Internet access. The current paper fills a significant gap, proposing an algorithm that optimises the positions of unmanned vehicles such that an ad hoc network is deployed to serve specific wireless sensor networks that have no other Internet connectivity (hilly/mountainous areas, Danube Delta) and must be connected to an Internet of Things (IoT) ecosystem. The algorithm determines the optimum positions of UV nodes that decrease the path losses below the link budget threshold with minimum UV node displacement compared to their initial coordinates. The algorithm was tested in a rural scenario and 3rd Generation Partnership Project (3GPP), free space and two-ray propagation models. The paper proposes another type of network, a Flying and Surface Ad Hoc Network (FSANET), a concept which implies collaboration and coexistence between unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) and several use cases that motivate the need for such a network.

摘要

目前,在人类难以或甚至无法到达的监控任务中,使用无人飞行器(如无人机、船只等)会引发一些问题。尽管人们一直在努力提高这些飞行器的自主性,但仍未完全解决所有问题。在无人车物联网(IoUV)环境中,用可移动的监控节点代替静态的无线基础设施并重新利用这些节点的想法可以汇聚成一种动态解决方案,以确保在没有基础设施保证网络接入的区域进行数据收集。本文提出了一种算法,填补了这一重要空白,该算法优化了无人车的位置,部署了一个特定的自组网,以服务于那些没有其他互联网连接的特定无线传感器网络(山区、多瑙河三角洲),并且必须连接到物联网(IoT)生态系统。该算法确定了 UV 节点的最佳位置,使路径损耗低于链路预算阈值,同时与初始坐标相比,UV 节点的位移最小。该算法在农村场景和第三代合作伙伴计划(3GPP)、自由空间和两射线传播模型中进行了测试。本文还提出了另一种网络,即飞行和表面自组网(FSANET),这一概念意味着无人机(UAVs)和无人水面艇(USVs)之间的协作和共存,以及几个用例,这些用例证明了这种网络的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1398/8587671/a9e724b82d04/sensors-21-06984-g001.jpg

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