AI Bibliography
AI Bibliography |
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Sun, W., Liu, J., & Yue, Y. (2019). Ai-enhanced offloading in edge computing: When machine learning meets industrial iot. IEEE Network, 33(5), 68–74. |
| Resource type: Journal Article BibTeX citation key: Sun2019 View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, Engineering, General, Military Science Subcategories: Big data, Cloud computing, Command and control, Cross-domain deterrence, Cyber, Edge AI, Internet of things, JADC2, Machine learning, Mosaic warfare, Networked forces, Neural nets, Q-learning Creators: Liu, Sun, Yue Publisher: Collection: IEEE Network |
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| Abstract |
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The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.
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