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Virtual Reality (VR)-based Learning Environments (VRLEs) are gaining popularity due to the wide availability of cloud and its edge (a.k.a. fog) technologies and high-speed networks. Thus, there is a need to investigate Internet-of-Things (IoT)-based application design concepts within social VRLEs to offer scalable, cost-efficient services that adapt to dynamic cloud/fog system conditions. In this paper, we investigate the costperformance trade-offs for an IoT-based application that integrates large-scale sensor data from Social VRLEs and coordinates the real-time data processing and visualization across cloud/fog platforms. To facilitate dynamic performance adaptation of the IoT-based application with increased user scale, we present a set of cost-aware adaptive control rules. The implementation of the rules is based on an analytical queuing model that determines the performance states of the IoT-based application, given the current workload and the allocated cloud/fog resources. Using the IoTbased application in an exemplar VRLE use case, we evaluate the cost-performance trade-offs with three system architectures i.e., cloud-only, edge-only and edge-cloud architectures. Experiment results illustrate the best/worst practices in the cost-performance trade-offs for a range of simulated IoT scenarios involving monitoring user emotional data collected by using brain sensors. Our results also detail the impact of the system architecture selection, and the benefits in enabling feedback about student emotions to instructors during Social VR learning sessions. Lastly, we show the benefits of integrating our model-based feedback control in maximizing IoT-based application performance while keeping the associated costs at a minimum level. 

Virtual Reality (VR)-based Learning Environments (VRLEs) are gaining popularity due to the wide availability of cloud and its edge (a.k.a. fog) technologies and high-speed networks. Thus, there is a need to investigate Internet-of-Things (IoT)-based application design concepts within social VRLEs to offer scalable, cost-efficient services that adapt to dynamic cloud/fog system conditions. In this paper, we investigate the costperformance trade-offs for an IoT-based application that integrates large-scale sensor data from Social VRLEs and coordinates the real-time data processing and visualization across cloud/fog platforms. To facilitate dynamic performance adaptation of the IoT-based application with increased user scale, we present a set of cost-aware adaptive control rules. The implementation of the rules is based on an analytical queuing model that determines the performance states of the IoT-based application, given the current workload and the allocated cloud/fog resources. Using the IoTbased application in an exemplar VRLE use case, we evaluate the cost-performance trade-offs with three system architectures i.e., cloud-only, edge-only and edge-cloud architectures. Experiment results illustrate the best/worst practices in the cost-performance trade-offs for a range of simulated IoT scenarios involving monitoring user emotional data collected by using brain sensors. Our results also detail the impact of the system architecture selection, and the benefits in enabling feedback about student emotions to instructors during Social VR learning sessions. Lastly, we show the benefits of integrating our model-based feedback control in maximizing IoT-based application performance while keeping the associated costs at a minimum level. 

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a threedimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system. 

—Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a threedimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system. 

Virtual Learning Environments (VLEs) are spaces designed to educate students remotely via online platforms. Although traditional VLEs such as iSocial have shown promise in educating students, they offer limited immersion that diminishes learning effectiveness. This paper outlines a virtual reality learning environment (VRLE) over a high-speed network, which promotes educational effectiveness and efficiency via our creation of flexible content and infrastructure which meet established VLE standards with improved immersion. This paper further describes our implementation of multiple learning modules developed in High Fidelity, a "social VR" platform. Our experiment results show that the VR mode of content delivery better stimulates the generalization of lessons to the real world than non-VR lessons and provides improved immersion when compared to an equivalent desktop version