Enhancing Augmented Reality Performance: An Exploration of Edge Computing and Code Offloading in Collaborative AR Systems
Daliton Silva, Dimas Cassimiro, Camila Dantas, Paulo Maciel · 2023
Augmented Reality (AR) Systems have been largely adopted as a useful technology. They are excellent candidates for AR application execution platforms due to their recent popularity. Nonetheless, the limited processing power and battery autonomy restrict the possibilities of more extensive use of smartphones as a broadly adopted AR platform. This work explores the use of Edge Computing and Code Offloading in a collaborative AR system. We present the proposed application architecture, which includes several interconnected components divided into three primary blocks. The benefits of AR are discussed, including its use in education, medical training, and industry. The central objective of this work is to provide an in-depth and structured analysis of the proposed architecture, paving the way for extensive studies that address various dependability issues. We implemented an AR system to validate the proposed architecture. We collected the data related to the execution times of the algorithms allocated to the Edge, where it was possible to characterize a probability distribution suitable for characterizing these times. The proposed architecture was validated, and the results obtained open possibilities for future developments and better adequation of the applications implemented according to the proposed architecture.