Energy Data Classification at the Edge: A Comparative Study for Energy Efficiency Applications

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dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorAlsalemi, Abdullah
dc.contributor.authorAmira, Abbes
dc.contributor.authorMalekmohamadi, Hossein
dc.contributor.authorDiao, Kegong
dc.date.acceptance2023-08
dc.date.accessioned2023-09-01T09:18:28Z
dc.date.available2023-09-01T09:18:28Z
dc.date.issued2023
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linken
dc.description.abstractAs the global economy is increasingly influenced by energy policy and efficiency, the opportunities of energy data classification are broadening. Performance metrics, especially for Deep Learning (DL), have motivated the accelerated development of computing hardware. In particular, High-Performance Edge Computing (HPEC) has an important role in complementing cloud computing in the collection, pre-processing, and post-processing of big data in a more privacy-preserving manner. Yet, such opportunities bring challenges concerning the selection of hardware platforms and classification algorithms. Therefore, in this article, we aim to carry out a comparative study that examines the merits, performance and efficiency metrics, and limitations of notable HPEC platforms centered around a DL classification framework. In this implementation, a novel 2D Gramian Angular Field (GAF) energy data classifier is presented and run on a publicly available dataset. Following, a DL classifier is trained and exported as a lightweight counterpart for validation on several commonly used HPEC platforms, namely the Jetson Nano, ODROID-XU4, Coral Dev Board, and the BeagleBone AI. Current results show competitive computational performance among the reviewed platforms, as fast as ~8 msec per classified image.en
dc.funderNo external funderen
dc.identifier.citationAlsalemi, A., Amira, A., Malekmohamadi, H. and Diao, K. (2023) Energy Data Classification at the Edge: A Comparative Study for Energy Efficiency Applications. Cluster Computingen
dc.identifier.doihttps://doi.org/10.1007/s10586-023-04142-3
dc.identifier.issn1386-7857
dc.identifier.urihttps://hdl.handle.net/2086/23181
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherSpringeren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectEnergy efficiencyen
dc.subjecthigh performance edge computingen
dc.subjectartificial intelligenceen
dc.subjectdata lakeen
dc.subjectdeep learningen
dc.subjectinternet of energyen
dc.subjectGramian angular fieldsen
dc.titleEnergy Data Classification at the Edge: A Comparative Study for Energy Efficiency Applicationsen
dc.typeArticleen

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