Special Issues
Table of Content

Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application

Submission Deadline: 28 February 2026 View: 25380 Submit to Special Issue

Guest Editors

Dr. Hwa-Young Jeong

Email: hyjeong@khu.ac.kr

Affiliation: Humanitas College, Kyung Hee University, 02447, Seoul, Republic of Korea

Homepage:

Research Interests: software engineering, IoT, intelligent IoT, machine learning, deep learning, machine learning, multimedia system, e-learning system


Dr. Jason C. Hung

Email: jhung@gm.nutc.edu.tw

Affiliation: Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, 404, Taichung City, Taiwan

Homepage:

Research Interests: e-learning, intelligent system, social computing, affective computing, multimedia system, artificial intelligence


Dr. Yen Neil Yuwen

Email: neilyyen@u-aizu.ac.jp

Affiliation: School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, 965-8580, Japan

Homepage:

Research Interests: human-centered computing computational intelligence, human modeling, data mining, machine learning, social web, cognitive science, awareness engineering


Summary

Recently, IoT applications such as smart homes, autonomous vehicles, smart agriculture, and smart healthcare have improved the quality of people's lives. As this technical trends, the emerging distributed computing paradigm of "fog computing" and "edge computing," which is an extension of cloud computing, has gained attention from the industrial and research communities. Also as the use of IoT and machine learning (ML) increases, the workload on the cloud and fog/edge becomes increasingly diverse and dynamic.


There are many active research projects being conducted on applications in cloud-fog, fog-edge, and cloud-edge computing environments. This special issue welcomes application or demonstration studies in fields such as IoT, digital twin, sensor networks, smart healthcare, environmental engineering, and security within these cloud-fog, fog-edge, and cloud-edge computing environments. Potential topics include but are not limited to:
· Cloud-Fog-Edge-based Digital Twin Network
· Healthcare Applications with Fog-Edge-Cloud Computing
· Deep Learning in Fog-Cloud Computing Environments
· Multi-dimensional Industrial IoT (IIoT) Data in Cloud-Fog or Fog-Edge Network
· Security in Cloud-Fog-Edge Computing
· Detecting Attacks in Fog and Cloud Computing Environments
· Intelligent Sensor Networks for Cloud-Fog-Edge Computing
· Blockchain for Secure Cloud-Fog-Edge Computing
· Anomaly Detection in Cloud-Fog-Edge Environments
· Integration Frameworks for Cloud-Fog-Edge Computing


Keywords

Digital Twin, healthcare with cloud-fog computing, fog-edge computing, cloud-edge computing, integration for cloud, fog, and edge computing, cloud-fog or fog-edge network

Published Papers


  • Open Access

    ARTICLE

    Optimizing CNN Class Granularity for Power-Efficient Edge AI in Sudden Unintended Acceleration Verification

    HeeSeok Choi, Joon-Min Gil
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074511
    (This article belongs to the Special Issue: Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application)
    Abstract Given the growing number of vehicle accidents caused by unintended acceleration and braking failure, verifying Sudden Unintended Acceleration (SUA) incidents has become a persistent challenge. A central issue of debate is whether such events stem from mechanical malfunctions or driver pedal misapplications. However, existing verification procedures implemented by vehicle manufacturers often involve closed tests after vehicle recalls; thus raising ongoing concerns about reliability and transparency. Consequently, there is a growing need for a user-driven framework that enables independent data acquisition and verification. Although previous studies have addressed SUA detection using deep learning, few have explored… More >

  • Open Access

    ARTICLE

    EdgeST-Fusion: A Cross-Modal Federated Learning and Graph Transformer Framework for Multimodal Spatiotemporal Data Analytics in Smart City Consumer Electronics

    Mohammed M. Alenazi
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075966
    (This article belongs to the Special Issue: Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application)
    Abstract Multimodal spatiotemporal data from smart city consumer electronics present critical challenges including cross-modal temporal misalignment, unreliable data quality, limited joint modeling of spatial and temporal dependencies, and weak resilience to adversarial updates. To address these limitations, EdgeST-Fusion is introduced as a cross-modal federated graph transformer framework for context-aware smart city analytics. The architecture integrates cross-modal embedding networks for modality alignment, graph transformer encoders for spatial dependency modeling, temporal self-attention for dynamic pattern learning, and adaptive anomaly detection to ensure data quality and security during aggregation. A privacy-preserving federated learning protocol with differential privacy guarantees enables… More >

  • Open Access

    REVIEW

    Cloud-Edge-End Collaborative SC3 System in Smart Manufacturing: A Survey

    Xuehan Li, Tao Jing, Yang Wang, Bo Gao, Jing Ai, Minghao Zhu
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075426
    (This article belongs to the Special Issue: Integrating Computing Technology of Cloud-Fog-Edge Environments and its Application)
    Abstract With the deep integration of cloud computing, edge computing and the Internet of Things (IoT) technologies, smart manufacturing systems are undergoing profound changes. Over the past ten years, an extensive body of research on cloud-edge-end systems has been generated. However, challenges such as heterogeneous data fusion, real-time processing and system optimization still exist, and there is a lack of systematic review studies. In this paper, we review a cloud-edge-end collaborative sensing-communication-computing-control (SC3) system. This system integrates four layers of sensing, communication, computing and control to address the complex challenges of real-time decision making, resource… More >

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