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Artificial Intelligence for Optimizing Edge

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Edge AI

Abstract

DNNs (general DL models) can extract latent data features, while DRL can learn to deal with decision-making problems by interacting with the environment. Computation and storage capabilities of edge nodes, along with the collaboration of the cloud, make it possible to use DL to optimize edge computing networks and systems. With regard to various edge management issues such as edge caching, offloading, communication, security protection, etc., (1) DNNs can process user information and data metrics in the network, as well as perceiving the wireless environment and the status of edge nodes, and based on these information, (2) DRL can be applied to learn the long-term optimal resource management and task scheduling strategies, so as to achieve the intelligent management of the edge, viz. intelligent edge as shown in Tables 8.1, 8.2, and 8.3.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tian** University Tian**, Tian**, China

    **aofei Wang & Yiwen Han

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Victor C. M. Leung

  3. School of Computer and Engineering, Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. 2012 Lab, Huawei Technologies (China), Shenzhen, China

    Xueqiang Yan

  5. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China

    Xu Chen

Authors
  1. **aofei Wang
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  2. Yiwen Han
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  3. Victor C. M. Leung
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  4. Dusit Niyato
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  5. Xueqiang Yan
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  6. Xu Chen
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Wang, X., Han, Y., Leung, V.C.M., Niyato, D., Yan, X., Chen, X. (2020). Artificial Intelligence for Optimizing Edge. In: Edge AI. Springer, Singapore. https://doi.org/10.1007/978-981-15-6186-3_8

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  • DOI: https://doi.org/10.1007/978-981-15-6186-3_8

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  • Online ISBN: 978-981-15-6186-3

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