Publications
2024
Schultheis, Alexander; Alt, Benjamin; Bast, Sebastian; Guldner, Achim; Jilg, David; Katic, Darko; Mundorf, Johannes; Schlagenhauf, Tobias; Weber, Sebastian; Bergmann, Ralph; Bergweiler, Simon; Creutz, Lars; Dartmann, Guido; Malburg, Lukas; Naumann, Stefan; Rezapour, Mahdi; Ruskowski, Martin
EASY: Energy-Efficient Analysis and Control Processes in the Dynamic Edge-Cloud Continuum for Industrial Manufacturing Journal Article
In: Künstliche Intelligenz, 2024.
Abstract | Links | BibTeX | Tags: {Edge-Cloud Continuum, Analysis and Control Processes}, Energy- and Resource-Efficiency
@article{SchultheisEASY2024,
title = {EASY: Energy-Efficient Analysis and Control Processes in the Dynamic Edge-Cloud Continuum for Industrial Manufacturing},
author = {Alexander Schultheis and Benjamin Alt and Sebastian Bast and Achim Guldner and David Jilg and Darko Katic and Johannes Mundorf and Tobias Schlagenhauf and Sebastian Weber and Ralph Bergmann and Simon Bergweiler and Lars Creutz and Guido Dartmann and Lukas Malburg and Stefan Naumann and Mahdi Rezapour and Martin Ruskowski},
url = {https://www.wi2.uni-trier.de/shared/publications/2024_KI_EASY_SchultheisEtAl.pdf},
doi = {10.1007/s13218-024-00868-3},
year = {2024},
date = {2024-01-01},
journal = {Künstliche Intelligenz},
abstract = {According to the guiding principles of Industry 4.0, edge computing enables the data-sovereign and near-real-time processing of data directly at the point of origin. Using these edge devices in manufacturing organization will drive the use of industrial analysis, control, and Artificial Intelligence (AI) applications close to production. The goal of the EASY project is to make the added value of edge computing available by providing an easily usable edge-cloud continuum with a runtime environment and services for the execution of AI-based analysis and control processes. Within this continuum, a dynamic, distributed, and optimized execution of services is automated across the entire spectrum from centralized cloud to decentralized edge instances to increase productivity and resource efficiency.},
keywords = {{Edge-Cloud Continuum, Analysis and Control Processes}, Energy- and Resource-Efficiency},
pubstate = {published},
tppubtype = {article}
}
According to the guiding principles of Industry 4.0, edge computing enables the data-sovereign and near-real-time processing of data directly at the point of origin. Using these edge devices in manufacturing organization will drive the use of industrial analysis, control, and Artificial Intelligence (AI) applications close to production. The goal of the EASY project is to make the added value of edge computing available by providing an easily usable edge-cloud continuum with a runtime environment and services for the execution of AI-based analysis and control processes. Within this continuum, a dynamic, distributed, and optimized execution of services is automated across the entire spectrum from centralized cloud to decentralized edge instances to increase productivity and resource efficiency.