Special Issues
Table of Content

Advanced Edge Computing and Artificial Intelligence in Smart Environment

Submission Deadline: 28 February 2026 (closed) View: 1683 Submit to Special Issue

Guest Editor(s)

Dr. Chao Fang

Email: fangchao@bjut.edu.cn

Affiliation: School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China

Homepage:

Research Interests: intelligent network control, cloud-edge-terminal cooperation computing

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Dr. Feixiang Li

Email: lifeixiang126@126.com

Affiliation: The 15th Research Institute of China Electronics Technology Corporation, Beijing, 100083, China

Homepage:

Research Interests: mobile edge computing, software defined networks, and evolution algorithm

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Dr. Maoqing Zhang

Email: maoqing_zhang@haut.edu.cn

Affiliation: School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou,450001, China

Homepage:

Research Interests: machine learning, deep learning, intelligent computation and their applications

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Summary

The rapid proliferation of smart environments, encompassing domains such as smart cities, healthcare, transportation, and industrial automation, has driven the demand for efficient and real-time data processing. Edge intelligence—the convergence of edge computing and artificial intelligence (AI)—has emerged as a transformative paradigm, enabling localized data analytics and decision making at the network edge. This Special Issue focuses on "Advanced Edge Computing and Artificial Intelligence in Smart Environments", highlighting the latest advancements, challenges, and applications in this dynamic field.


The integration of AI at the edge is reshaping the architecture of smart environments, offering solutions to issues such as latency, bandwidth constraints, energy efficiency, and privacy. Topics of interest include, but are not limited to, novel algorithms and frameworks for edge intelligence, federated and distributed learning approaches, resource allocation and optimization strategies, security- and privacy-enhancing techniques, and application-driven innovations in smart environments.


By presenting cutting-edge research and real-world implementations, this Special Issue aims to provide a comprehensive overview of the state of the art in advanced edge intelligence, fostering interdisciplinary collaboration and guiding future developments in creating smarter, more resilient, and efficient environments. Researchers, practitioners, and policymakers are invited to contribute their insights and innovations to this critical and rapidly evolving field.


Keywords

edge intelligence; internet of things; resource allocation and optimization; real-time and energy efficiency; security and resiliency; machine learning; applications in smart cities, healthcare, transportation, and industrial automation

Published Papers


  • Open Access

    ARTICLE

    H-LoRA: Rethinking Rank Selection for Controllable Knowledge Retention in Edge AI

    Darren Chai Xin Lun, Lim Tong Ming
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080068
    (This article belongs to the Special Issue: Advanced Edge Computing and Artificial Intelligence in Smart Environment)
    Abstract The deployment of specialized language models in resource-constrained edge environments (1B parameters, 2 GB memory, 100 ms latency) faces a critical challenge: Supervised Fine-Tuning (SFT) achieves domain expertise but suffers from irreversible catastrophic forgetting, while traditional Low-Rank Adaptation (LoRA) with conservative ranks (r  64) often underperforms due to insufficient adaptation capacity. This work introduces H-LoRA (High-Rank LoRA) for edge-deployable models and establishes a fundamental distinction between destructive forgetting and controllable knowledge retention. Through comprehensive experiments on compact models (0.12B Minimind and Qwen-0.5B) across three domains (Human Resources, Medical, Mathematics) using 29,647 samples, we… More >

  • Open Access

    ARTICLE

    A Joint Optimization Model for Device Selection and Power Allocation under Dynamic Uncertain Environments

    Bohui Li, Bin Wang, Linjie Wu, Xingjuan Cai, Maoqing Zhang
    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-28, 2026, DOI:10.32604/cmc.2025.070592
    (This article belongs to the Special Issue: Advanced Edge Computing and Artificial Intelligence in Smart Environment)
    Abstract Federated Learning (FL) provides an effective framework for efficient processing in vehicular edge computing. However, the dynamic and uncertain communication environment, along with the performance variations of vehicular devices, affect the distribution and uploading processes of model parameters. In FL-assisted Internet of Vehicles (IoV) scenarios, challenges such as data heterogeneity, limited device resources, and unstable communication environments become increasingly prominent. These issues necessitate intelligent vehicle selection schemes to enhance training efficiency. Given this context, we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions, and develop a dynamic interval multi-objective More >

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