Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational...
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Autor*in:	Andrea Augello [verfasserIn] Salvatore Gaglio [verfasserIn] Giuseppe Lo Re [verfasserIn] Daniele Peri [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	distributed computing UAV UAV–WSN integration UAV path planning UAV task scheduling UAV-supported IoT systems for smart cities

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 14, p 5298
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:14, p 5298

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22145298

Katalog-ID:	DOAJ085147591

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allfieldsSound	10.3390/s22145298 doi (DE-627)DOAJ085147591 (DE-599)DOAJafa48f88cfb34cd682406585742d33b8 DE-627 ger DE-627 rakwb eng TP1-1185 Andrea Augello verfasserin aut Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose. distributed computing UAV UAV–WSN integration UAV path planning UAV task scheduling UAV-supported IoT systems for smart cities Chemical technology Salvatore Gaglio verfasserin aut Giuseppe Lo Re verfasserin aut Daniele Peri verfasserin aut In Sensors MDPI AG, 2003 22(2022), 14, p 5298 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:14, p 5298 https://doi.org/10.3390/s22145298 kostenfrei https://doaj.org/article/afa48f88cfb34cd682406585742d33b8 kostenfrei https://www.mdpi.com/1424-8220/22/14/5298 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 14, p 5298
language	English
source	In Sensors 22(2022), 14, p 5298 volume:22 year:2022 number:14, p 5298
sourceStr	In Sensors 22(2022), 14, p 5298 volume:22 year:2022 number:14, p 5298
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	distributed computing UAV UAV–WSN integration UAV path planning UAV task scheduling UAV-supported IoT systems for smart cities Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Andrea Augello @@aut@@ Salvatore Gaglio @@aut@@ Giuseppe Lo Re @@aut@@ Daniele Peri @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Andrea Augello
spellingShingle	Andrea Augello misc TP1-1185 misc distributed computing misc UAV misc UAV–WSN integration misc UAV path planning misc UAV task scheduling misc UAV-supported IoT systems for smart cities misc Chemical technology Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications
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topic_title	TP1-1185 Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications distributed computing UAV UAV–WSN integration UAV path planning UAV task scheduling UAV-supported IoT systems for smart cities
topic	misc TP1-1185 misc distributed computing misc UAV misc UAV–WSN integration misc UAV path planning misc UAV task scheduling misc UAV-supported IoT systems for smart cities misc Chemical technology
topic_unstemmed	misc TP1-1185 misc distributed computing misc UAV misc UAV–WSN integration misc UAV path planning misc UAV task scheduling misc UAV-supported IoT systems for smart cities misc Chemical technology
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title	Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications
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title_auth	Time-Constrained Node Visit Planning for Collaborative UAV–WSN Distributed Applications
abstract	Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.
abstractGer	Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.
abstract_unstemmed	Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.
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