Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios
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http://hdl.handle.net/10045/141049
Títol: | Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios |
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Autors: | Gama García, Ángel Manuel | Alcaraz Calero, José M. | Mora, Higinio | Wang, Qi |
Grups d'investigació o GITE: | Arquitecturas Inteligentes Aplicadas (AIA) |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Paraules clau: | Software application | Virtualisation | AI-enabled sensor | Prediction algorithm | Random forest |
Data de publicació: | 15-de febrer-2024 |
Editor: | MDPI |
Citació bibliogràfica: | Sensors. 2024, 24(4): 1236. https://doi.org/10.3390/s24041236 |
Resum: | In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes. |
Patrocinadors: | This work was funded in part by the European Commission Horizon 2020 5G-PPP Program under Grant Agreement Number H2020-ICT-2020-2/101017226: “6G BRAINS: Bringing Reinforcement learning Into Radio Light Network for Massive Connections” and the EU Horizon INCODE project Programming Platform for Intelligent Collaborative Deployments over Heterogeneous Edge IoT Environments (HORIZON-CL4-2022-DATA-01-03/101093069). |
URI: | http://hdl.handle.net/10045/141049 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s24041236 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/s24041236 |
Apareix a la col·lecció: | INV - AIA - Artículos de Revistas Investigacions finançades per la UE |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Gama-Garcia_etal_2024_Sensors.pdf | 1,47 MB | Adobe PDF | Obrir Vista prèvia | |
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