Self-adapting Industrial Augmented Reality Applications with Proactive Dynamic Software Product Lines

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.