Suman Sukhavasi, Thinagaran Perumal^*, Norwati Mustapha, Razali Yaakob

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075613 - 12 March 2026

Abstract The Internet of Things (IoT) ecosystem is inherently heterogeneous, comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange. However, as the number of service requests grows, existing approaches suffer from increased discovery time and degraded Quality of Service (QoS). Moreover, the massive data generated by heterogeneous IoT devices often contain redundancy and noise, posing challenges to efficient data management. To address these issues, this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management. The architecture employs Modified-Ordered Points to Identify the Clustering Structure (M-OPTICS)… More >

Randika K. Makumbura¹, Hasanthi Wijesundara², Hirushan Sajindra¹, Upaka Rathnayake^1,*, Vikram Kumar³, Dineshbabu Duraibabu¹, Sumit Sen³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075539 - 12 March 2026

Abstract Accurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges… More >

Weimin Shi^1,*, Kuntao Lv¹, Chang Xuan¹, Ji Wu²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075203 - 12 March 2026

Abstract The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive… More >

Qingwen Yang¹, Zhi Li², Jiawei Tang¹, Yanyi Liu¹, Tiezheng Guo¹, Yingyou Wen^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074384 - 12 March 2026

Abstract Deploying Large Language Model (LLM)-based agents in the Industrial Internet of Things (IIoT) presents significant challenges, including high latency from cloud-based APIs, data privacy concerns, and the infeasibility of deploying monolithic models on resource-constrained edge devices. While smaller models (SLMs) are suitable for edge deployment, they often lack the reasoning power for complex, multi-step tasks. To address these issues, this paper introduces LEAF, a Lightweight Edge Agent Framework designed for efficiently executing complex tasks at the edge. LEAF employs a novel architecture where multiple expert SLMs—specialized for planning, execution, and interaction—work in concert, decomposing complex… More >

Wang Cheng¹, Zhuodong Liu², Xiangyu Li^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074250 - 12 March 2026

Abstract Smart pest control is crucial for building farm resilience and ensuring sustainable agriculture in the face of climate change and environmental challenges. To achieve effective intelligent monitoring systems, agricultural pest and disease detection must overcome three fundamental challenges: feature degradation in dense vegetation environments, limited detection capability for sub-32×32 pixel targets, and inadequate bounding box regression for irregular pest morphologies. This study proposes YOLOv12-KMA, a novel detection framework that addresses these limitations through four synergistic architectural innovations, specifically optimized for agricultural environments. First, we introduce efficient multi-head attention (C3K2-EMA), which reduces noise interference by 41%… More >

Yuji Sang¹, Lijun Liu^1,*, Long Lv^1,*, Husheng Wu², Hemin Yin¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073837 - 12 March 2026

Abstract Aiming at the challenges of low throughput, excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance (PBFT) algorithm in blockchain networks, its application in identity verification for distributed networking of a drone cluster is limited. Therefore, a lightweight blockchain-based identity authentication model for UAV swarms is designed, and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance (CG-PBFT) algorithm is proposed. CG-PBFT introduces a reputation score evaluation mechanism, classifies the reputation levels of nodes in the network, and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology. Experimental 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 >

Jiaxi Wang¹, Wenbo Sun², Ziyuan Zhou¹, Shihua Wu¹, Jiang Xu¹, Shan Ji^3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073237 - 10 February 2026

Abstract Lightweight nodes are crucial for blockchain scalability, but verifying the availability of complete block data puts significant strain on bandwidth and latency. Existing data availability sampling (DAS) schemes either require trusted setups or suffer from high communication overhead and low verification efficiency. This paper presents ISTIRDA, a DAS scheme that lets light clients certify availability by sampling small random codeword symbols. Built on ISTIR, an improved Reed–Solomon interactive oracle proof of proximity, ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication. This paper formalizes opening consistency and prove security… More >

Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Safwan Al-sayed¹, Xi Wang¹, Yijiang Peng^1,*, Esraa Hyarat², Ahmad Ali AlZubi³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074068 - 12 January 2026

Abstract In modern construction, Lightweight Aggregate Concrete (LWAC) has been recognized as a vital material of concern because of its unique properties, such as reduced density and improved thermal insulation. Despite the extensive knowledge regarding its macroscopic properties, there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC. This study aims to bridge this knowledge gap, spurred by the need to enhance the predictability and applicability of LWAC in various construction environments. With the help of advanced numerical methods, including the… More >