Special Issues
Table of Content

Advances in Edge Intelligence for Internet of Things

Submission Deadline: 31 July 2023 (closed) View: 9

Guest Editors

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


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


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

Published Papers

  • Open Access


    Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

    Huabin Wang, Ruichao Mo, Yuping Chen, Weiwei Lin, Minxian Xu, Bo Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2025-2047, 2023, DOI:10.32604/cmes.2023.026910
    (This article belongs to the Special Issue: Advances in Edge Intelligence for Internet of Things)
    Abstract Nowadays, the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network, such as pedestrian and vehicle detection, to provide efficient intelligent services to mobile users. However, as the accuracy requirements continue to increase, the components of deep learning models for pedestrian and vehicle detection, such as YOLOv4, become more sophisticated and the computing resources required for model training are increasing dramatically, which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance. For… More >

  • Open Access


    An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

    Siyu Ren, Shi Qiu, Keyang Cheng
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1643-1659, 2023, DOI:10.32604/cmes.2023.025248
    (This article belongs to the Special Issue: Advances in Edge Intelligence for Internet of Things)
    Abstract Star sensors are an important means of autonomous navigation and access to space information for satellites. They have been widely deployed in the aerospace field. To satisfy the requirements for high resolution, timeliness, and confidentiality of star images, we propose an edge computing algorithm based on the star sensor cloud. Multiple sensors cooperate with each other to form a sensor cloud, which in turn extends the performance of a single sensor. The research on the data obtained by the star sensor has very important research and application values. First, a star point extraction model is… More >

  • Open Access


    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 the 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 More >

    Graphic Abstract

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

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