Jinwei Mao^1,2, Wang Luo^1,2,*, Jiangtao Xu³, Daohua Zhu³, Wei Liang³, Zhechen Huang³, Bao Feng^1,2, Shuang Yang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072505 - 12 March 2026

Abstract With the rapid development of power Internet of Things (IoT) scenarios such as smart factories and smart homes, numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency. Multi-access edge computing technology deploys cloud computing capabilities at the network edge; constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability. Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,… More >

Chengqi Liu¹, Yongtao Li², Weiping Zou^3,*, Deyu Lin^4,5,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074975 - 10 February 2026

Abstract With the large-scale deployment of the Internet of Things (IoT) devices, their weak security mechanisms make them prime targets for malware attacks. Attackers often use Domain Generation Algorithm (DGA) to generate random domain names, hiding the real IP of Command and Control (C&C) servers to build botnets. Due to the randomness and dynamics of DGA, traditional methods struggle to detect them accurately, increasing the difficulty of network defense. This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments. Specifically, a teacher model combining CharacterBERT, a bidirectional long short-term memory More >

Zuyi Huang¹, Yue Wang¹, Jia Liu², Haodong Yi¹, Lejun Ai¹, Min Chen^1,3,*, Salman A. AlQahtani⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074034 - 10 February 2026

Abstract The personalized fine-tuning of large language models (LLMs) on edge devices is severely constrained by limited computation resources. Although split federated learning alleviates on-device burdens, its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning, which leads to excessive communication overhead. To address this, we propose Language-Empowered Split Fine-Tuning (LESFT), a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm. LESFT simultaneously learns from multiple logically equivalent but linguistically diverse reasoning chains, providing richer supervisory signals and improving data efficiency. This process-oriented training allows more effective reasoning More >

Suman Kunwar^*

Journal on Artificial Intelligence, Vol.8, pp. 39-49, 2026, DOI:10.32604/jai.2026.076674 - 22 January 2026

Abstract The rise in convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked various image classification models (EfficientNetV2S/M, ResNet50/101, MobileNet) and object detection (YOLOv8n, YOLOv11n) including our purposed YOLOv8n-CBAM model using our annotated dataset designed for recycling. We found a clear trade-off between accuracy and resource consumption: the best classifier, EfficientNetV2S, achieved high accuracy (≈96%) but suffered from high latency More >

Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

Muhammad Asim^1,*, Abdelhamied A. Ateya¹, Mudasir Ahmad Wani^1,2, Gauhar Ali¹, Mohammed ElAffendi¹, Ahmed A. Abd El-Latif¹, Reshma Siyal³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070684 - 12 January 2026

Abstract The growing developments in 5G and 6G wireless communications have revolutionized communications technologies, providing faster speeds with reduced latency and improved connectivity to users. However, it raises significant security challenges, including impersonation threats, data manipulation, distributed denial of service (DDoS) attacks, and privacy breaches. Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks. This survey provides a comprehensive review of how Federated Learning (FL), Blockchain, and Digital Twin (DT) technologies can collectively enhance the security of 5G and 6G systems. Blockchain offers decentralized, immutable, and transparent mechanisms for securing More >

Bohui Li¹, Bin Wang¹, Linjie Wu¹, Xingjuan Cai^1,*, Maoqing Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-28, 2026, DOI:10.32604/cmc.2025.070592 - 09 December 2025

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 >

Ateeqa Jalal^1,*, Umar Farooq^1,4,5, Ihsan Rabbi^1,4, Afzal Badshah², Aurangzeb Khan^1,4, Muhammad Mansoor Alam^3,4, Mazliham Mohd Su’ud^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068289 - 09 December 2025

Abstract The exponential growth of Internet of Things (IoT) devices, autonomous systems, and digital services is generating massive volumes of big data, projected to exceed 291 zettabytes by 2027. Conventional cloud computing, despite its high processing and storage capacity, suffers from increased network latency, network congestion, and high operational costs, making it unsuitable for latency-sensitive applications. Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership (TCO). Hybrid solutions, such as fog computing, cloudlets, and Mobile Edge Computing (MEC), attempt to balance cost and performance;… More >

Abdullah Alourani¹, Mehtab Alam^2,*, Ashraf Ali³, Ihtiram Raza Khan⁴, Chandra Kanta Samal²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161 - 10 November 2025

Abstract The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The… More >

Qingtao Meng, Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069970 - 10 November 2025

Abstract This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the… More >