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Self-adapting Industrial Augmented Reality applications with proactive Dynamic Software Product Lines

Fulltext:


Authors:

Inmaculada Ayala, Mercedes Amor , Lidia Fuentes , Alessandro Papadopoulos

Research group:

Complex Real-Time Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

26th IEEE International Conference on Emerging Technologies and Factory Automation

DOI:

10.1109/ETFA45728.2021.9613392

Abstract

Industrial Augmented Reality (IAR) is a key enabling technology for Industry 4.0. However, its adoption poses several challenges because it requires the execution of computing-intensive tasks in devices with poor computational resources, which contributes to a faster draining of the device batteries. Proactive self-adaptation techniques could overcome these problems that affect the quality of experience by optimizing computational resources and minimizing user disturbance. In this work, we propose to apply ProDSPL, a proactive Dynamic Software Product Line, for the self-adaptation of IAR applications to satisfy the quality requirements. ProDSPL is compared against MODAGAME, a multi-objective DSPL approach that uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. The evaluation with randomly generated feature models running on mobile devices shows that ProDSPL gives results closer to the Pareto optimal than MODAGAME.

Bibtex

@inproceedings{Ayala6241,
author = {Inmaculada Ayala and Mercedes Amor and Lidia Fuentes and Alessandro Papadopoulos},
title = {Self-adapting Industrial Augmented Reality applications with proactive Dynamic Software Product Lines},
month = {September},
year = {2021},
booktitle = {26th IEEE International Conference on Emerging Technologies and Factory Automation},
url = {http://www.es.mdu.se/publications/6241-}
}