Enhancing the Performance of XR Environments Using Fog and Cloud Computing
Source abstract
Abstract
The extended reality (XR) environment demands high-performance computing and data processing capabilities, while requiring continuous technological development to enable a real-time integration between the physical and virtual worlds for user interactions. XR systems have traditionally been deployed in local environments primarily because of the need for the real-time collection of user behavioral patterns. On the other hand, these XR systems face limitations in local deployments, such as latency issues arising from factors, such as network bandwidth and GPU performance. Consequently, several studies have examined cloud-based XR solutions. While offering centralized management advantages, these solutions present bandwidth, data transmission, and real-time processing challenges. Addressing these challenges necessitates reconfiguring the XR environment and adopting new approaches and strategies focusing on network bandwidth and real-time processing optimization. This paper examines the computational complexities, latency issues, and real-time user interaction challenges of XR. A system architecture that leverages edge and fog computing is proposed to overcome these challenges and enhance the XR experience by efficiently processing input data, rendering output content, and minimizing latency for real-time user interactions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: IoT and Edge/Fog Computing · Augmented Reality Applications · Virtual Reality Applications and Impacts
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science & Engineering, Yuhan University, Bucheon 14780, Republic of Korea
Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea
Funding
National Research Foundation of Korea (NRF)
References (32)
Show source-provided references
1. A State-of-the-Art Survey on Augmented Reality-Assisted Digital Twin for Futuristic Human-Centric Industry Transformation10.1016/j.rcim.2022.102515 ↗
2. Effect of Augmented Reality Affordance on Motor Performance: In the Sport Climbing
3. Human Motion Recognition Based on SVM in VR Art Media Interaction Environment10.1186/s13673-019-0203-8 ↗
4. Interactive Experience Room Using Infrared Sensors and User’s Poses
5. Lee, Y., Yoo, B., and Lee, S.-H. (, January 8–12). Sharing Ambient Objects Using Real-Time Point Cloud Streaming in Web-Based XR Remote Collaboration. Proceedings of the 26th International Conference on 3D Web Technology, Pisa, Italy.
6. Theodoros Theodoropoulos Cloud-Based XR Services: A Survey on Relevant Challenges and Enabling Technologies
7. Adaptive Cloud-Based Extended Reality: Modeling and Optimization10.1109/ACCESS.2021.3062555 ↗
8. Perspectives on Point Cloud-Based 3D Scene Modeling and XR Presentation within the Cloud-Edge-Client Architecture10.1016/j.visinf.2023.06.007 ↗
9. Fair and Efficient Caching Algorithms and Strategies for Peer Data Sharing in Pervasive Edge Computing Environments10.1109/TMC.2019.2902090 ↗
10. VR Locomotion in the New Era of Virtual Reality: An Empirical Comparison of Prevalent Techniques10.1155/2019/7420781 ↗
11. (2023, October 01). AR/VR Light Engines: Perspectives and Challenges. Available online: https://opg.optica.org/aop/abstract.cfm?uri=aop-14-4-783.
12. Augmented Reality in Vocational Training: A Systematic Review of Research and Applications10.1016/j.chb.2021.107125 ↗
13. Çöltekin, A., Lochhead, I., Madden, M., Christophe, S., Devaux, A., Pettit, C., Lock, O., Shukla, S., Herman, L., and Stachoň, Z. (2020). Extended Reality in Spatial Sciences: A Review of Research Challenges and Future Directions. ISPRS Int. J. Geo-Inf., 9.10.3390/ijgi9070439 ↗
14. Zhou, Y., and Kunz, T. (2018). Ad Hoc Networks, Proceedings of the 9th International Conference, AdHocNets 2017, Niagara Falls, ON, Canada, 28–29 September 2017, Springer International Publishing.10.1007/978-3-319-51204-4 ↗
15. Future Edge Cloud and Edge Computing for Internet of Things Applications10.1109/JIOT.2017.2767608 ↗
16. Cloud-Edge Coordinated Processing: Low-Latency Multicasting Transmission10.1109/JSAC.2019.2906791 ↗
17. Toward Low-Latency and Ultra-Reliable Virtual Reality10.1109/MNET.2018.1700268 ↗
18. Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems10.1109/TCOMM.2020.3040283 ↗
19. OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
20. Kammerl, J., Blodow, N., Rusu, R.B., Gedikli, S., Beetz, M., and Steinbach, E. (2012, January 14–18). Real-Time Compression of Point Cloud Streams. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.10.1109/ICRA.2012.6224647 ↗
21. Tu, C., Takeuchi, E., Miyajima, C., and Takeda, K. (2016, January 1–4). Compressing Continuous Point Cloud Data Using Image Compression Methods. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Janeiro, Brazil.
22. Banfi, F., and Previtali, M. (2021). Human–Computer Interaction Based on Scan-to-BIM Models, Digital Photogrammetry, Visual Programming Language and eXtended Reality (XR). Appl. Sci., 11.10.3390/app11136109 ↗
23. Virtual Reality and Virtual Reality System Components10.4028/www.scientific.net/AMR.765-767.1169 ↗
24. Virtual Reality for Physically Disabled People10.1016/0010-4825(94)00039-S ↗
25. Yew, H.T., Ng, M.F., Ping, S.Z., Chung, S.K., Chekima, A., and Dargham, J.A. (2020, January 28–29). IoT Based Real-Time Remote Patient Monitoring System. Proceedings of the 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia.10.1109/CSPA48992.2020.9068699 ↗
26. Srikanth, S., and Meher, S. (2013, January 3–5). Compression Efficiency for Combining Different Embedded Image Compression Techniques with Huffman Encoding. Proceedings of the 2013 International Conference on Communication and Signal Processing, Melmaruvathur, India.10.1109/iccsp.2013.6577170 ↗
27. Mansri, I., Doghmane, N., Kouadria, N., Harize, S., and Bekhouch, A. (2020, January 19–22). Comparative Evaluation of VVC, HEVC, H.264, AV1, and VP9 Encoders for Low-Delay Video Applications. Proceedings of the 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA), Valencia, Spain.10.1109/MCNA50957.2020.9264275 ↗
28. Towards Minimizing Resource Usage with QoS Guarantee in Cloud Gaming10.1109/TPDS.2020.3024068 ↗
29. Calibration of VLP-16 Lidar Sensor and Vision Cameras Using the Center Coordinates of a Spherical Object
30. CNN-Based 3D Object Classification Using Hough Space of LiDAR Point Clouds10.1186/s13673-020-00228-8 ↗
31. Heuchert, S., Rimal, B.P., Reisslein, M., and Wang, Y. (Appl. Comput. Inform., 2021). Design of a Small-Scale and Failure-Resistant IaaS Cloud Using OpenStack, Appl. Comput. Inform., ahead-of-print.10.1108/ACI-04-2021-0094 ↗
32. (2023, October 14). Unreal Engine. Available online: https://www.unrealengine.com/ko/unreal-engine-5?gclid=Cj0KCQjw1aOpBhCOARIsACXYv-drJT2jlSeepwKq23R3R0Z_ytbHwFsbQ48UeEdRqxZCGn-ertYdOs0aAiOeEALw_wcB.