Hybrid software architecture with peripheral computing for adaptive VR driving training systems with biometric feedback

Artur Maliuha

Abstract


The subject of the article is the methodology for developing a hybrid three-tier software architecture for adaptive VR driving training systems with real-time biometric feedback, which ensures the distribution of functional components between the client, peripheral and microservice levels. The goal is to develop an architecture that provides a balance between minimal latency of biometric data processing for a comfortable VR experience, reduced costs, and the possibility of gradual scaling. Tasks to be solved: performing a comparative analysis of existing architectural approaches according to the criteria of latency, cost and scalability; developing a hybrid software architecture model; experimental verification of the proposed architecture; determining the scope of application of the hybrid architecture. The methods used include architectural design methods, synthesis and decomposition methods. The following results were obtained. A hybrid architecture was developed with a client layer for rendering and collecting biometric data, a peripheral layer for classifying user state and adapting training scenarios, and a microservice layer for asynchronous traffic simulation and data analytics with possible scalability. Experimental verification demonstrated a reduction in the latency of biometric data processing. The scope of application of the developed hybrid model was determined. Conclusions. The results of the study confirmed the relevance of a hybrid approach to architectural design with the placement of computationally intensive components on a peripheral server in the local network of the educational institution instead of a centralized cloud server to eliminate global network delays, and also provided an opportunity to justify the choice of software architecture taking into account the number of users, budget constraints, and latency requirements. The scientific novelty of the results obtained lies in the creation of a hybrid architecture model that involves the distribution of functional components of the system by levels based on constraints on processing speed, implementation cost, and the degree of independence of auxiliary calculations, which makes it possible to increase the efficiency of resource use in training using virtual reality, as well as to increase the efficiency of training by adapting to the current state of the learner.

Keywords


adaptive virtual reality; biometric feedback; driver training; edge computing; hybrid architecture; latency optimization; machine learning; microservices; scalability; stress classification

References


Boboc, R. G., Butilă, E. V., & Butnariu, S. Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications. Sensors (Basel), 2024, vol. 24, iss. 13, article no. 4417. DOI: 10.3390/s24134417.

Nasri, M. Towards Intelligent VR Training: A Physiological Adaptation Framework for Cognitive Load and Stress Detection. Jasani, K. [cs.HC]. 2025. Available at: https://arxiv.org/abs/2504.06461 (accessed 10.04.2025).

Kopaee, A. M., Hajseyedtaghia, S. A., Chitsaz, H. Latency Reduction in CloudVR: Cloud Prediction, Edge Correction. arXiv:2410.01898 [eess.SY]. 2024. DOI: 10.48550/arXiv.2410.01898.

Alencar, D. Dynamic Allocation of Microservices for Virtual Reality Content Delivery to Provide Quality of Experience Support in a Fog Computing Environment. Proc. Brazilian Computing Society (SBC). 2023. Available at: https://sol.sbc.org.br/index.php/ctd/article/view/24853 (accessed 12.10.2024).

Van der Perk, R., & et al. Distributed Safety Mechanism for Autonomous Vehicle Simulation. IEEE Trans. Intell. Transport. Syst., 2020, vol. 21, iss. 8, pp. 3345-3358. DOI: 10.1109/TITS.2019.2923456.

Vaughan, N., & et al. A Review of Virtual Reality Training for Surgical Education. Frontiers Robotics AI., 2016, vol. 3, article no. 38. DOI: 10.3389/frobt.2016.00038.

Chiossi, F., & et al. Adaptive VR with Brain-Computer Interfaces for Complex Visuomotor Task Training. CHI, 2025. DOI: 10.1145/3544548.3581389.

Mabioca, O., & et al. Event-Driven Architecture Patterns for Real-Time VR Systems. IEEE Software, 2025, vol. 42, iss. 1, pp. 78-86. DOI: 10.1109/MS.2024.3456789.

Casasnovas, M., & et al. Experimental Evaluation of Interactive Edge/Cloud Virtual Reality Applications Using Unity Render Streaming. Computer Communications, 2024, vol. 224, pp. 112-125. DOI: 10.1016/j.comcom.2024.08.001.

Kämäräinen, T., & et al. Imperceptible Latency for Mobile Cloud Gaming. ACM MobiSys, 2018, pp. 88-100. DOI: 10.1145/3210240.3210323.

IEEE. Dynamic Microservice Placement Framework for VR in 5G with NFV. IEEE Commun. Magazine, 2021, vol. 59, iss. 5, pp. 112-118. DOI: 10.1109/MCOM.2021.2056789.

Rodrigues, H., Silva, A.R., Avritzer, A. Assessment of Performance and its Scalability in Microservice Architectures: Systematic Literature Review. Journal of Systems and Software, Elsevier, 2025, vol. 208, article no. 111567. DOI: 10.1016/j.jss.2023.111567.

Lopez, P. A., &et al. Microscopic Traffic Simulation using SUMO. IEEE Intell. Transport. Syst. Conf., 2018, pp. 2575-2582. DOI: 10.1109/ITSC.2018.8569938.

Intel Corporation. Edge Computing in 5G Networks for Low-Latency Services. White Paper, 2019. Available at: https://intel.com/5g-edge-whitepaper-2019 (accessed 12.10.2024).

Imaginary Cloud. Top Scalability Patterns for Distributed Systems. Technical Blog, 2025. Available at: https://imaginarycloud.com/blog/scalability-patterns (accessed 15.05.2025).

Hogan, J., & et al. Analyzing Performance Issues of Virtual Reality Applications. arXiv:2211.02013 [cs.SE]. 2022. DOI: 10.48550/arXiv.2211.02013.

Geris, A., & et al. Balancing Performance and Comfort in Virtual Reality: A Configurable Framework for Optimizing VR Systems. Software: Practice and Experience, 2024, vol. 54, iss. 6, pp. 1128-1149. DOI: 10.1002/spe.3356.

Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Conference on Robot Learning (CoRL), 2017, arXiv:1711.03938 [cs.LG], pp. 1-16.

Azfar, T., Huang, K., Tracy, A., Misiewicz, S., Liu, C., & Ke, R. Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning. arXiv:2412.03925 [cs.RO]. 2024.

Cherukuri, B. R. Microservices and Containerization: Accelerating Web Application Development and Deployment. World Journal of Advanced Research and Reviews, 2020, vol. 8, iss. 2, pp. 234-245. DOI: 10.30574/wjarr.2020.8.2.0087.

Alhamad, A., & et al. Kubernetes-Based Orchestration for Scalable Microservices Deployment in Cloud-Native Environments. IEEE Access, 2023, vol. 11, pp. 45678-45691. DOI: 10.1109/ACCESS.2023.3284567.

Jasani, K. Performance Optimization in VR Applications: QA’s Role in Ensuring Quality and Efficiency. International Journal of Advances in Developmental Research, 2024, vol. 15, iss. 1, pp. 1287-1295. E-ISSN: 0976-4844.

AlShekh, R. H., & et al. The Role of Virtual Reality UDP Ethernet Communication in Network Performance Optimization. arXiv:2502.00785 [cs.NI]. 2025.




DOI: https://doi.org/10.32620/aktt.2025.5.09