Optimizing computational resources for edge intelligence through model cascade strategies

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dc.contributor.authorGómez Carmona, Oihane
dc.contributor.authorCasado Mansilla, Diego
dc.contributor.authorLópez de Ipiña González de Artaza, Diego
dc.contributor.authorGarcía-Zubía, Javier
dc.date.accessioned2024-11-15T08:24:49Z
dc.date.available2024-11-15T08:24:49Z
dc.date.issued2022-05-15
dc.date.updated2024-11-15T08:24:48Z
dc.description.abstractAs the number of interconnected devices increases and more artificial intelligence (AI) applications upon the Internet of Things (IoT) start to flourish, so does the environmental cost of the computational resources needed to send and process all the generated data. Therefore, promoting the optimization of AI applications is a key factor for the sustainable development of IoT solutions. Paradigms such as Edge Computing are progressively proposed as a solution in the IoT field, becoming an alternative to delegate all the computation to the Cloud. However, bringing the computation to the local stage is limited by the resources' availability of the devices hosted at the Edge of the network. For this reason, this work presents an approach that simplifies the complexity of supervised learning algorithms at the Edge. Specifically, it separates complex models into multiple simpler classifiers forming a cascade of discriminative models. The suitability of this proposal in a human activity recognition (HAR) context is assessed by comparing the performance of three different variations of this strategy. Furthermore, its computational cost is analyzed in several resource-constrained Edge devices in terms of processing time. The experimental results show the viability of this approach to outperform other ensemble methods, i.e., the Stacking technique. Moreover, it substantially reduces the computational cost of the classification tasks by more than 60% without a significant accuracy loss (around 3.5%). This highlights the potential of this strategy to reduce resource and energy requirements in IoT architectures and promote more efficient and sustainable classification solutions.en
dc.description.sponsorshipThe authors gratefully acknowledge the support of the Basque Government´s Department of Education for the predoctoral funding of one of the authors and the Deustek Research Group. They also acknowledge NVIDIA Corporation for thedonation of the Jetson Nano used for this researchen
dc.identifier.citationGomez-Carmona, O., Casado-Mansilla, D., Lopez-De-Ipina, D., & Garcia-Zubia, J. (2022). Optimizing Computational Resources for Edge Intelligence Through Model Cascade Strategies. IEEE Internet of Things Journal, 9(10), 7404-7417. https://doi.org/10.1109/JIOT.2021.3118845
dc.identifier.doi10.1109/JIOT.2021.3118845
dc.identifier.issn2327-4662
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1889
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherEdge computing
dc.subject.otherEdge intelligence
dc.subject.otherEmbedded systems
dc.subject.otherEnsemble learning
dc.subject.otherInternet of Things (IoT)
dc.subject.otherMachine Learning (ML)
dc.subject.otherOptimization
dc.titleOptimizing computational resources for edge intelligence through model cascade strategiesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage7417
oaire.citation.issue10
oaire.citation.startPage7404
oaire.citation.titleIEEE Internet of Things Journal
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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