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dc.contributor.author
Loor, Fernando
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dc.contributor.author
Manuel Manriquez
dc.contributor.author
Gil Costa, Graciela Verónica
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dc.contributor.author
Marín, Mauricio
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dc.date.available
2022-02-10T11:56:30Z
dc.date.issued
2022-02
dc.identifier.citation
Loor, Fernando; Manuel Manriquez; Gil Costa, Graciela Verónica; Marín, Mauricio; Feasibility of P2P-STB based crowdsourcing to speed-up photo classification for natural disasters; Springer; Cluster Computing-the Journal Of Networks Software Tools And Applications; 25; 1; 2-2022; 279-302
dc.identifier.issn
1386-7857
dc.identifier.uri
http://hdl.handle.net/11336/151733
dc.description.abstract
We present a distributed platform aimed to process photos taken after a natural disaster strikes by people witnesses of the situation. These photos have to be processed as quickly as possible to collect statistical data used by the decision makers to coordinate rescue teams. A photo can be classified using a predefined taxonomy such as infrastructure and service, affected people, emotional support, among others. Some photos can be classified automatically while other photos require human intervention. The proposed platform is organized in three layers: an architecture, a communication pattern algorithm and optimization modules. The architecture is based on a community of digital volunteers forming a peer-to-peer network. The digital volunteers receive photos from a centralized server that collects and integrates the results into the management process to improve the general understanding of the situation or rescue actions. We present three communication pattern algorithms that define the flow of tasks between the volunteers and the server. The first algorithm is based on point-to-point communication and the other two algorithms use cache techniques inside the peer-to-peer network. Our proposal is devised for short term campaigns and we aim to speed-up the image processing process, to reduce the workload of the server and to reduce communication latency between the server and the volunteers. We evaluate our proposed platform under highly demanding task traffic rates. We analyze the impact of the input parameters of each communication pattern algorithm. We evaluate the performance of our proposed platform with different approaches presented in the technical literature which are deployed as optimization modules. Results show that the performance of the platform when using the cache-based communication pattern algorithms can outperform the one-to-one communication algorithm under high task traffic rates.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
NATURAL DISASTER
dc.subject
P2P NETWORK
dc.subject
P2P-STB-BASED PLATFORM
dc.subject
PERFORMANCE EVALUATION
dc.subject.classification
Ciencias de la Computación
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Feasibility of P2P-STB based crowdsourcing to speed-up photo classification for natural disasters
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-02-09T14:17:18Z
dc.journal.volume
25
dc.journal.number
1
dc.journal.pagination
279-302
dc.journal.pais
Alemania
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dc.journal.ciudad
Berlín
dc.description.fil
Fil: Loor, Fernando. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
dc.description.fil
Fil: Manuel Manriquez. Universidad de Santiago de Chile; Chile
dc.description.fil
Fil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
dc.description.fil
Fil: Marín, Mauricio. Universidad de Santiago de Chile; Chile
dc.journal.title
Cluster Computing-the Journal Of Networks Software Tools And Applications
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/s10586-021-03381-6
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