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Cybersickness is one of the greatest barriers to the adoption of virtual reality. A growing body of research has focused on identifying the characteristics of cybersickness and finding ways to mitigate it through the utilization of data from VR content, physiological signals, and body movement, along with artificial intelligence techniques. In this work, we extend prior research on cybersickness prediction by considering the role of different data modalities. We propose a novel deep learning model named multimodal, attention-based cybersickness (MAC), which learns temporal sequences and characteristics of video flows, eye movement, head movement, and electrodermal activity. Based on data collected from 27 participants, we demonstrate the effectiveness of MAC, showing an F1-score of 0.87. Our experimental results further show not only the influences of gender and prior VR experience but also the effectiveness of the attention mechanism on model performance, emphasizing the importance of considering the characteristics of data types and users in cybersickness modeling. [ABSTRACT FROM AUTHOR]

Securely sharing and managing personal content is a challenging task in multi-device environments. In this paper, we design and implement a new platform called personal content networking (PCN). Our work is inspired by content-centric networking (CCN) because we aim to enable access to personal content using its name instead of its location. The unique challenge of PCN is to support secure file operations such as replication, updates, and access control over distributed untrusted devices. The primary contribution of this work is the design and implementation of a secure content management platform that supports secure updates, replications, and fine-grained content-centric access control of files. Furthermore, we demonstrate its feasibility through a prototype implementation on the CCNx skeleton. [ABSTRACT FROM AUTHOR]

Geographic routing in wireless sensor networks requires sources nodes to be aware of the location information of sinks to send their data. To provide the sink location service, quorum-based schemes have been proposed, which exploit crossing points between a quorum of a sink location announcement (SLA) message from a sink and a quorum of a sink location query (SLQ) message from a source node. For guaranteeing at least one crossing point in irregular sensor networks with void areas or irregular boundaries, the previous schemes however collect and flood the network boundary information or forward a SLA and SLQ message along the whole network boundary. In this paper, we design a novel quorum-based sink location service scheme that exploits circle and line quorums, which does not require the network boundary information and send a SLA and SLQ message along the whole network boundary. In the proposed scheme, a source node sends a SLQ message to the network center and sends another SLQ message to an edge node in the network boundary, thus generating a SLQ line quorum. On the other hand, a sink node sends a SLA message along a circle path whose center is the network center, thus forming a SLQ circle quorum. By this way, it is guaranteed that the SLQ and SLA quorums have at least one crossing point in irregular sensor networks. Both numerical analysis and extensive simulation results verify that the proposed scheme outperforms the existing schemes in terms of the delivery distance, the delivery hop count, and the energy consumption for providing sink location service. [ABSTRACT FROM AUTHOR]