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dc.contributor.authorZaiwar, Ali
dc.contributor.authorLei, Jiao
dc.contributor.authorBaker, Thar
dc.contributor.authorAbbas, Ghulam
dc.contributor.authorAbbas, Ziaul Haq
dc.contributor.authorKhaf, Sadia
dc.date.accessioned2020-03-16T10:01:54Z
dc.date.available2020-03-16T10:01:54Z
dc.date.created2020-01-09T14:18:37Z
dc.date.issued2019
dc.identifier.citationZaiwar, A., Lei, J., Baker, T., Abbas, G., Abbas, Z. H. & Khaf, S. (2019). A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing. IEEE Access, 7, 149623-149633. doi:en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/2646903
dc.description.abstractMobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computingen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2019 The Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420
dc.source.pagenumber149623-149633en_US
dc.source.volume7en_US
dc.source.journalIEEE Accessen_US
dc.identifier.doi10.1109/ACCESS.2019.2947053
dc.identifier.cristin1769532
cristin.qualitycode1


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