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Towards Sustainable Machine Learning: Analyzing Energy-Efficient Algorithmic Strategies for Environmental Sensor Data

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dc.contributor.authorCetkin, Berkay
dc.contributor.authorBegic Fazlic, Lejla
dc.contributor.authorGuldner, Achim
dc.contributor.authorNaumann, Stefan
dc.contributor.authorDartmann, Guido
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:11Z
dc.date.available2024-10-21T18:24:11Z
dc.date.issued2024
dc.description.abstractThis study evaluates the energy efficiency of machine learning (ML) classification models across 49 test setups, each representing different conditions derived from a set of scenarios. Utilizing internet of things (IoT) technology with an ESP8266 microcontroller, we collected and analyzed environmental data including temperature, humidity, and CO2 levels from a simulated room environment. We measured energy consumption for data preprocessing, model training, and testing, alongside energy efficiency metrics that consider output, processing time, and F1 score. The study also performed correlation analyses to explore the relationship between energy consumption and performance metrics. Furthermore, it assessed the trade-offs between accuracy and energy efficiency by comparing an ensemble model to its constituent algorithms. The measurements, conducted according to the Green Software Measurement Model (GSMM), provide essential insights into selecting energy-efficient algorithms for a broad spectrum of IoT applications.en
dc.identifier.doi10.18420/inf2024_102
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-746-3
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45073
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectAlgorithmic Optimization
dc.subjectEnergy Efficiency
dc.subjectInternet of Things
dc.subjectMachine Learning
dc.titleTowards Sustainable Machine Learning: Analyzing Energy-Efficient Algorithmic Strategies for Environmental Sensor Dataen
dc.typeText/Conference Paper
gi.citation.endPage1164
gi.citation.publisherPlaceBonn
gi.citation.startPage1155
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitle5. Workshop "KI in der Umweltinformatik" (KIU-2024)

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