Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

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...

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Autores principales:	Ana-Maria Dragulinescu, Simona Halunga, Ciprian Zamfirescu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	algorithm Internet of Unmanned Vehicles link budget LoRa/LoRaWAN optimisation path loss Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/cfde94f9aa7940368228dc31ff8ec133
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spelling	oai:doaj.org-article:cfde94f9aa7940368228dc31ff8ec1332021-11-11T19:01:47ZUnmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles10.3390/s212169841424-8220https://doaj.org/article/cfde94f9aa7940368228dc31ff8ec1332021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6984https://doaj.org/toc/1424-8220Currently, 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.Ana-Maria DragulinescuSimona HalungaCiprian ZamfirescuMDPI AGarticlealgorithmInternet of Unmanned Vehicleslink budgetLoRa/LoRaWANoptimisationpath lossChemical technologyTP1-1185ENSensors, Vol 21, Iss 6984, p 6984 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	algorithm Internet of Unmanned Vehicles link budget LoRa/LoRaWAN optimisation path loss Chemical technology TP1-1185
spellingShingle	algorithm Internet of Unmanned Vehicles link budget LoRa/LoRaWAN optimisation path loss Chemical technology TP1-1185 Ana-Maria Dragulinescu Simona Halunga Ciprian Zamfirescu Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
description	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.
format	article
author	Ana-Maria Dragulinescu Simona Halunga Ciprian Zamfirescu
author_facet	Ana-Maria Dragulinescu Simona Halunga Ciprian Zamfirescu
author_sort	Ana-Maria Dragulinescu
title	Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
title_short	Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
title_full	Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
title_fullStr	Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
title_full_unstemmed	Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
title_sort	unmanned vehicles’ placement optimisation for internet of things and internet of unmanned vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cfde94f9aa7940368228dc31ff8ec133
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Unmanned Vehicles’ Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles

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