EagleEYE: Aerial Edge-enabled Disaster Relief Response System

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dc.contributor.author Febrian Ardiansyah, Muhammad
dc.contributor.author William, Timothy
dc.contributor.author Ibrahiem Abdullaziz, Osamah
dc.contributor.author Wang, Li-Chun
dc.contributor.author Tien, Po-Lung
dc.contributor.author Yuang, Maria C.
dc.date.accessioned 2020-10-02T13:07:17Z
dc.date.available 2020-10-02T13:07:17Z
dc.date.issued 2020-09-20
dc.identifier.bibliographicCitation Febrian Ardiansyah, M., William, T., Ibrahiem Abdullaziz, O., Wang, L.C., Tien, P.L. y Yuang, M.C. (2020).EagleEYE: Aerial Edge-enabled Disaster Relief Response System. In 2020 European Conference on Networks and Communications (EuCNC), pp. 321-325.
dc.identifier.isbn 978-1-7281-4355-2
dc.identifier.uri http://hdl.handle.net/10016/31032
dc.description This paper has been presented at 2020 European Conference on Networks and Communications (EuCNC).
dc.description.abstract The fifth generation (5G) mobile network has paved the way for innovations across vertical industries. The integration of distributed intelligent edge into the 5G orchestrated architecture brings the benefits of low-latency and automation. A successful example of this integration is exhibited by the 5G-DIVE project, which aims at proving the technical merits and business value proposition of vertical industries such as autonomous drone surveillance and navigation. In this paper, and as part of 5G-DIVE, we present an aerial disaster relief system, called EagleEYE, which utilizes edge computing and machine learning to detect emergency situations in real-time. EagleEYE reduces training time by devising an object fusion mechanism which enables reusing existing datasets. Furthermore, EagleEYE parallelizes the detection tasks to enable real-time response. Finally, EagleEYE is evaluated in a real-world testbed and the results show that EagleEYE can reduce the inference latency by 90% with a high detection accuracy of 87%.
dc.description.sponsorship This work has been partially funded by the H2020 EU/TW joint action 5G-DIVE (Grant #859881).
dc.format.extent 5
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2020 IEEE.
dc.subject.other Low-latency computing
dc.subject.other Object detection
dc.subject.other Container
dc.subject.other Edge computing
dc.title EagleEYE: Aerial Edge-enabled Disaster Relief Response System
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/EuCNC48522.2020.9200963
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/859881
dc.type.version acceptedVersion
dc.relation.eventdate 15-18 June 2020
dc.relation.eventplace Dubrovnik, Croatia
dc.relation.eventtitle 2020 European Conference on Networks and Communications (EuCNC)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 321
dc.identifier.publicationlastpage 325
dc.identifier.publicationtitle 2020 European Conference on Networks and Communications (EuCNC)
dc.contributor.funder European Commission
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