2021/08/27 by Samaikya Valluripally, Vaibhav Akashe, Valluripally, Samaikya +9 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Network Security and Intrusion Detection #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2108.12315
openalex publication_date 2021/08/27 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Social virtual reality learning environments (VRLEs) provide immersive\nexperience to users with increased accessibility to remote learning. Lack of\nmaintaining high-performance and secured data delivery in critical VRLE\napplication domains (e.g., military training, manufacturing) can disrupt\napplication functionality and induce cybersickness. In this paper, we present a\nnovel rule-based 3QS-adaptation framework that performs risk and cost aware\ntrade-off analysis to control cybersickness due to performance/security anomaly\nevents during a VRLE session. Our framework implementation in a social VRLE\nviz., vSocial monitors performance/security anomaly events in network/session\ndata. In the event of an anomaly, the framework features rule-based adaptations\nthat are triggered by using various decision metrics. Based on our experimental\nresults, we demonstrate the effectiveness of our rule-based 3QS-adaptation\nframework in reducing cybersickness levels, while maintaining application\nfunctionality. Using our key findings, we enlist suitable practices for\naddressing performance and security issues towards a more high-performing and\nrobust social VRLE.\n