1,902 research outputs found
Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.
A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends
Index to NASA Tech Briefs, 1975
This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
Vehicle anomaly detection plays a vital role in highway safety applications
such as accident prevention, rapid response, traffic flow optimization, and
work zone safety. With the surge of the Internet of Things (IoT) in recent
years, there has arisen a pressing demand for Artificial Intelligence (AI)
based anomaly detection methods designed to meet the requirements of IoT
devices. Catering to this futuristic vision, we introduce a lightweight
approach to vehicle anomaly detection by utilizing the power of trajectory
prediction. Our proposed design identifies vehicles deviating from expected
paths, indicating highway risks from different camera-viewing angles from
real-world highway datasets. On top of that, we present VegaEdge - a
sophisticated AI confluence designed for real-time security and surveillance
applications in modern highway settings through edge-centric IoT-embedded
platforms equipped with our anomaly detection approach. Extensive testing
across multiple platforms and traffic scenarios showcases the versatility and
effectiveness of VegaEdge. This work also presents the Carolinas Anomaly
Dataset (CAD), to bridge the existing gap in datasets tailored for highway
anomalies. In real-world scenarios, our anomaly detection approach achieves an
AUC-ROC of 0.94, and our proposed VegaEdge design, on an embedded IoT platform,
processes 738 trajectories per second in a typical highway setting. The dataset
is available at
https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set
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