Table of Content

Advances in Edge Intelligence for Internet of Things

Submission Deadline: 30 December 2022 Submit to Special Issue

Guest Editors

Dr. Huaming Wu, Center for Applied Mathematics, Tianjin University, Tianjin, China
Dr. Chaogang Tang, China University of Mining and Technology, China

Summary

The explosive growth in the number of Internet of Things (IoT) devices leads to ubiquitous connections among human and environments, with predictable benefits and potential economic values. Limited physical size, computing capability and energy supply impede the diversified development of IoT applications. Additionally, there are increasing needs for resource-hungry IoT applications in various application domain such as medical and industrial fields. Many newly emergent technologies can be applied for addressing this issue, e.g., deep learning, fog computing and edge computing. Furthermore, a lot of symmetries and asymmetries exist in the management of IoT networks. The aim of this Special Issue (SI) is to focus on these advances for IoT and further stimulate progress in IoT.

Topics of interest include, but are not limited to the ones listed below:

Edge/Fog/Cloud Computing for IoTs

Intelligent task offloading, resource allocation, caching, cyber-security, and   privacy in IoTs

• AI-based data offloading, service outsourcing/placement in intelligent IoTs

Applications in various IoTs such as internet of medical things (IoMT), industrial IoT (IIoT), IoT in mines and so on

• Volunteer computing related technologies for edge/fog/cloud computing in IoTs

Optimization techniques in edge computing


Keywords

Edge computing, intelligent offloading, service outsourcing, caching, internet of things

Published Papers


  • Open Access

    ARTICLE

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

    Hong’an Li, Min Zhang, Dufeng Chen, Jing Zhang, Meng Yang, Zhanli Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369
    (This article belongs to this Special Issue: Advances in Edge Intelligence for Internet of Things)
    Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering… More >

    Graphic Abstract

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

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