{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:07:09Z","timestamp":1780618029049,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RYC2018-025580-I"],"award-info":[{"award-number":["RYC2018-025580-I"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RTI2018-096384-B-I00"],"award-info":[{"award-number":["RTI2018-096384-B-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RTC2019-007159-5"],"award-info":[{"award-number":["RTC2019-007159-5"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007801","name":"Fundaci\u00f3n S\u00e9neca","doi-asserted-by":"publisher","award":["20813\/PI\/18"],"award-info":[{"award-number":["20813\/PI\/18"]}],"id":[{"id":"10.13039\/100007801","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as \u201cdark data\u201d, i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia\u2019s AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11\u00d7 for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.<\/jats:p>","DOI":"10.3390\/s20216335","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T09:03:04Z","timestamp":1604653384000},"page":"6335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5648-214X","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Cecilia","sequence":"first","affiliation":[{"name":"Computer Engineering Department (DISCA), Universitat Polit\u00e9cnica de Valencia (UPV), 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0038-0539","authenticated-orcid":false,"given":"Juan-Carlos","family":"Cano","sequence":"additional","affiliation":[{"name":"Computer Engineering Department (DISCA), Universitat Polit\u00e9cnica de Valencia (UPV), 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0008-4825","authenticated-orcid":false,"given":"Juan","family":"Morales-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Computer Science Department, Universidad Cat\u00f3lica de Murcia (UCAM), 30107 Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9802-4240","authenticated-orcid":false,"given":"Antonio","family":"Llanes","sequence":"additional","affiliation":[{"name":"Computer Science Department, Universidad Cat\u00f3lica de Murcia (UCAM), 30107 Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2758-8364","authenticated-orcid":false,"given":"Baldomero","family":"Imbern\u00f3n","sequence":"additional","affiliation":[{"name":"Computer Science Department, Universidad Cat\u00f3lica de Murcia (UCAM), 30107 Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.bushor.2020.01.005","article-title":"Growth paths for overcoming the digitalization paradox","volume":"63","author":"Gebauer","year":"2020","journal-title":"Bus. 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