Abstract
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.
| Original language | English |
|---|---|
| Article number | 1236 |
| Number of pages | 21 |
| Journal | Sensors |
| Volume | 24 |
| Issue number | 4 |
| Early online date | 15 Feb 2024 |
| DOIs | |
| Publication status | Published - Feb 2024 |
Bibliographical note
Copyright © 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/).Keywords
- AI-enabled sensor
- prediction algorithm
- random forest
- software application
- virtualisation