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DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

2020Journal article Open access
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dc.contributor.authorKoubaa, Anis
dc.contributor.authorAmmar, Adel
dc.contributor.authorAlahda, Mahmoud
dc.contributor.authorKanhouc, Anas
dc.contributor.authorAzar, Ahmad Taher
dc.date.accessioned2020-10-30T09:24:46Z
dc.date.available2020-10-30T09:24:46Z
dc.date.issued2020
dc.descriptionThis article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systemspt_PT
dc.description.abstractUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/s20185240pt_PT
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.22/16372
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/20/18/5240pt_PT
dc.subjectUnmanned aerial vehicles (UAVs)pt_PT
dc.subjectDeep learningpt_PT
dc.subjectCloud computingpt_PT
dc.subjectInternet-of-Thingspt_PT
dc.subjectRemote sensingpt_PT
dc.subjectSmart citiespt_PT
dc.titleDeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applicationspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue18pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume20pt_PT
person.familyNameKoubaa
person.familyNameAmmar
person.familyNameAzar
person.givenNameAnis
person.givenNameAdel
person.givenNameAhmad Taher
person.identifier989131
person.identifier.ciencia-idCA19-2399-D94A
person.identifier.orcid0000-0003-3787-7423
person.identifier.orcid0000-0003-0795-132X
person.identifier.orcid0000-0002-7869-6373
person.identifier.ridO-5566-2014
person.identifier.scopus-author-id15923354900
person.identifier.scopus-author-id16229278700
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0337d7df-5f77-46a4-8269-83d14bd5ea6b
relation.isAuthorOfPublicationc6587bf7-dc30-4e11-8ade-36d17faafaf9
relation.isAuthorOfPublicationafff63d1-5f93-49f8-90f0-912129d87f3c
relation.isAuthorOfPublication.latestForDiscovery0337d7df-5f77-46a4-8269-83d14bd5ea6b

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