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Fog Computing for Augmented Reality: Trends, Challenges and Opportunities
Augmented reality applications are computationally intensive and have latency requirements in the range of 15-20 milliseconds. Fog computing addresses these requirements by providing on-demand computing capacity and lower latency by bringing the computational resources closer to the augmented reality devices. In this paper, we reviewed papers providing custom solutions for augmented reality using the fog architecture and identified that the ongoing research trends towards balancing quality-of-experience, energy, and latency for both single and collaborative multi-device augmented reality applications. Furthermore, some works also focus on providing architectures for fog-based augmented reality systems and also on the training of machine learning algorithms in the fog layers to improve user experience. Based on these findings, we provide some challenges and research directions that can facilitate the adoption of fog-based augmented reality systems. ; © 2020 IEEE. This is the accepted version of the conference paper published at IEEE. The final publication is available at IEEE via https://doi.org/10.1109/ICFC49376.2020.00017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Fog Computing for Augmented Reality: Trends, Challenges and Opportunities
Augmented reality applications are computationally intensive and have latency requirements in the range of 15-20 milliseconds. Fog computing addresses these requirements by providing on-demand computing capacity and lower latency by bringing the computational resources closer to the augmented reality devices. In this paper, we reviewed papers providing custom solutions for augmented reality using the fog architecture and identified that the ongoing research trends towards balancing quality-of-experience, energy, and latency for both single and collaborative multi-device augmented reality applications. Furthermore, some works also focus on providing architectures for fog-based augmented reality systems and also on the training of machine learning algorithms in the fog layers to improve user experience. Based on these findings, we provide some challenges and research directions that can facilitate the adoption of fog-based augmented reality systems. ; © 2020 IEEE. This is the accepted version of the conference paper published at IEEE. The final publication is available at IEEE via https://doi.org/10.1109/ICFC49376.2020.00017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Fog Computing for Augmented Reality: Trends, Challenges and Opportunities
Salman, Shaik Mohammed (Autor:in) / Sitompul, Taufik Akbar (Autor:in) / Papadopoulos, Alessandro Vittorio (Autor:in) / Nolte, Thomas (Autor:in)
29.05.2020
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC:
690
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