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  • Open Access

    ARTICLE

    HybridEdge: A Lightweight and Secure Hybrid Communication Protocol for the Edge-Enabled Internet of Things

    Amjad Khan1, Rahim Khan1,*, Fahad Alturise2,*, Tamim Alkhalifah3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3161-3178, 2025, DOI:10.32604/cmc.2025.060372 - 17 February 2025

    Abstract The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the… More >

  • Open Access

    ARTICLE

    LSBSP: A Lightweight Sharding Method of Blockchain Based on State Pruning for Efficient Data Sharing in IoMT

    Guoqiong Liao1,3, Yinxiang Lei1,2,*, Yufang Xie1, Neal N. Xiong4

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3309-3335, 2025, DOI:10.32604/cmc.2024.060077 - 17 February 2025

    Abstract As the Internet of Medical Things (IoMT) continues to expand, smart health-monitoring devices generate vast amounts of valuable data while simultaneously raising critical security and privacy challenges. Blockchain technology presents a promising avenue to address these concerns due to its inherent decentralization and security features. However, scalability remains a persistent hurdle, particularly for IoMT applications that involve large-scale networks and resource-constrained devices. This paper introduces a novel lightweight sharding method tailored to the unique demands of IoMT data sharing. Our approach enhances state bootstrapping efficiency and reduces operational overhead by utilizing a dual-chain structure comprising… More >

  • Open Access

    ARTICLE

    YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

    Honglin Wang1, Yangyang Zhang2,*, Cheng Zhu3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3399-3417, 2025, DOI:10.32604/cmc.2024.058932 - 17 February 2025

    Abstract Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction… More >

  • Open Access

    ARTICLE

    Masked Face Restoration Model Based on Lightweight GAN

    Yitong Zhou, Tianliang Lu*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3591-3608, 2025, DOI:10.32604/cmc.2024.057554 - 17 February 2025

    Abstract Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US$3… More >

  • Open Access

    ARTICLE

    Secure Medical Image Retrieval Based on Multi-Attention Mechanism and Triplet Deep Hashing

    Shaozheng Zhang, Qiuyu Zhang*, Jiahui Tang, Ruihua Xu

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2137-2158, 2025, DOI:10.32604/cmc.2024.057269 - 17 February 2025

    Abstract Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a… More >

  • Open Access

    ARTICLE

    End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

    Shafi Ullah1,*, Haidawati Muhammad Nasir2, Kushsairy Kadir3,*, Akbar Khan1, Ahsanullah Memon4, Shanila Azhar1, Ilyas Khan5, Muhammad Ashraf1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3223-3249, 2025, DOI:10.32604/cmc.2024.054676 - 17 February 2025

    Abstract Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519; and (ii): elliptic curve More >

  • Open Access

    ARTICLE

    Innovative Lightweight Encryption Schemes Leveraging Chaotic Systems for Secure Data Transmission

    Haider H. Al-Mahmood1,*, Saad N. Alsaad2

    Intelligent Automation & Soft Computing, Vol.40, pp. 53-74, 2025, DOI:10.32604/iasc.2024.059691 - 10 January 2025

    Abstract In secure communications, lightweight encryption has become crucial, particularly for resource-constrained applications such as embedded devices, wireless sensor networks, and the Internet of Things (IoT). As these systems proliferate, cryptographic approaches that provide robust security while minimizing computing overhead, energy consumption, and memory usage are becoming increasingly essential. This study examines lightweight encryption techniques utilizing chaotic maps to ensure secure data transmission. Two algorithms are proposed, both employing the Logistic map; the first approach utilizes two logistic chaotic maps, while the second algorithm employs a single logistic chaotic map. Algorithm 1, including a two-stage mechanism… More >

  • Open Access

    CORRECTION

    Correction: A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion

    Khadija Manzoor1, Fiaz Majeed2, Ansar Siddique2, Talha Meraj3, Hafiz Tayyab Rauf4,*, Mohammed A. El-Meligy5, Mohamed Sharaf6, Abd Elatty E.Abd Elgawad6

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1459-1459, 2025, DOI:10.32604/cmc.2024.061588 - 03 January 2025

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

    Hongxing Wang1, Xilai Ju2, Hua Zhu1,*, Huafeng Li1,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

    Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

  • Open Access

    ARTICLE

    A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection

    Xiaoyun Chen1, Lanyao Zhang1, Xiaoling Chen1, Yigang Cen2, Linna Zhang1,*, Fugui Zhang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 521-542, 2025, DOI:10.32604/cmc.2024.058063 - 03 January 2025

    Abstract Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit random shapes and sizes, which makes it challenging to collect defective samples. Additionally, the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions. This paper proposes a novel Lightweight Multi-scale Feature Fusion network (LMFF) to address these challenges. The network comprises a feature extraction network, a multi-scale feature fusion module (MFF), and a segmentation network. Specifically, a feature extraction network is proposed to obtain… More >

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