Collecting large volume data from wireless sensor network by drone.

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dc.contributor.authorSilva, Rone Ilídio da
dc.contributor.authorRezende, Josiane da Costa Vieira
dc.contributor.authorSouza, Marcone Jamilson Freitas
dc.date.accessioned2023-07-26T20:12:52Z
dc.date.available2023-07-26T20:12:52Z
dc.date.issued2023pt_BR
dc.description.abstractData collection is the most important task in wireless sensor networks (WSN). Each sensor node has to send the sensed data to a special node called sink, which is the user interface. The sensor nodes far from the sink send data to intermediate nodes that forward it by multi-hop data paths. This characteristic leads to higher energy consumption in the sensor nodes close to the sink since they have to relay data from all other sensor nodes. The literature presents several studies that use mobile sinks for data collection to reduce the number of hops in the data paths and distributes the energy consumption, considering that the nodes close to the mobile sink change. However, the majority of these studies consider only the network limitation, such as energy. Furthermore, they also consider sensor nodes sending only one data packet to the mobile sink. This work assumes a quad-copter drone as a mobile sink and sensor nodes having several data packets to send to the sink. We propose two GRASP-based heuristics to define drone tours for data collection. Since this vehicle has limited flight time, the primary metric analyzed here is the overall data collection time. Furthermore, they guarantee that the mobile sink will stay a minimal time inside the radio range of each sensor node to ensure that all of them will have enough time to send all data. The heuristics achieve this guarantee by looking for a subset of locations, among the infinite points inside the monitored area, where the drone will hover for data gathering. Hence, the proposed heuristics have to search for good locations to reduce the data gathering time and define the shortest path to reduce the trip time. Simulated experiments showed that the proposed GRASP-based heuristics outperformed the greed algorithm found as state of the art for this type of scenario, mainly when the volume of data stored in each sensor node is high.pt_BR
dc.identifier.citationSILVA, R. I. da; REZENDE, J. da C. V.; SOUZA, M. J. F. Collecting large volume data from wireless sensor network by drone. Ad Hoc Networks, v. 138, artigo 103017, 2023. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1570870522001895#!>. Acesso em: 06 jul. 2023.pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.adhoc.2022.103017pt_BR
dc.identifier.issn1570-8705
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/17075
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S1570870522001895#!pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectMobile sinkpt_BR
dc.subjectPath planningpt_BR
dc.titleCollecting large volume data from wireless sensor network by drone.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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