Abuzar Khan¹, Abid Iqbal^2,*, Ghassan Husnain^1,*, Fahad Masood¹, Mohammed Al-Naeem³, Sajid Iqbal⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077222 - 09 April 2026

Abstract Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations. This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical, privacy-preserving cloud solutions. We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIS trajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation. Using a public AIS corpus with realistic… More >

Yihao Kuang^1,2, Hong Zhang^1,2, Jiaqi Wang^1,2, Lingyu Jin^1,2, Bo Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076652 - 09 April 2026

Abstract 3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding in robotics. Recent technological advancements in this field have significantly bolstered the environmental interaction capabilities of intelligent systems. This field faces persistent challenges, including feature degradation induced by point cloud sparsity, representation drift caused by non-rigid deformation, and occlusion in complex scenarios. Traditional appearance matching methods, particularly those relying on Siamese networks, are severely constrained by point cloud characteristics, often failing under rapid motions or structural ambiguities among similar objects. In response,… More >

Fangxiang Hu¹, Qi Fu^1,2,*, Shiwen Zhang¹, Jing Huang¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074154 - 09 April 2026

Abstract In the Internet of Vehicles (IoV) environment, the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles. Traditional approaches, which rely on one or more computational resources within the cloud-edge-device computing model, struggle to ensure overall service quality when handling high-density traffic flows and large-scale tasks. To address this issue, we propose a computational offloading scheme based on a cloud-edge-device collaborative 6G IoV edge computing model, namely, Multi-Agent Deep Reinforcement Learning-based and Server-weighted scoring Selection (MADRLSS), which aims to optimize dynamic offloading decisions and resource allocation. The… More >

Moina-yndi Ibrahim¹, Warda Soulaimana², Zhenjie Zhao^3,*

Journal on Internet of Things, Vol.8, pp. 67-86, 2026, DOI:10.32604/jiot.2026.078735 - 07 April 2026

Abstract The increasing demand for efficient agricultural water management, exacerbated by population growth and climate change, has spurred the development of Internet of Things (IoT)-based smart irrigation systems as an alternative to inefficient traditional methods that waste water and reduce crop productivity. This paper presents a low-cost IoT irrigation platform that integrates real-time camera surveillance for enhanced farm monitoring and precise water management. The system employs an Espressif System (ESP8266) microcontroller to automate irrigation control based on soil moisture readings and an Espressif System ESP32 Camera Module (ESP32-CAM) to provide live imaging of the agricultural plot,… More >

Polina A. Tretyakova^*, Alexey P. Belkin, Alexander A. Rumyantsev, Anna A. Menshikova

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.075393 - 27 March 2026

Abstract Limited adoption of solar energy in the Northwestern region of Russia is associated with insufficient data on annual solar radiation indicators and on the potential of solar collectors for water heating. The study aims to evaluate the potential of solar water heating for domestic use in Northwestern Russia, using Tyumen city as the case. In this region, the number of cloudy days ranges from 5% to 50%, with cloud cover increasing in winter. New data on the total solar radiation, availability duration, and cloud cover have been collected. Solar irradiance could reach 900 MJ/m² during summer… More >

Goodness Adediran¹, Kenny Awuson-David², Yussuf Ahmed^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077606 - 12 March 2026

Abstract Amazon Web Services (AWS) CloudTrail auditing service provides detailed records of operational and security events, enabling cloud administrators to monitor user activity and manage compliance. Although signature-based threat detection methods have been enhanced with machine learning and Large Language Models (LLMs), these approaches remain limited in addressing emerging threats. This study evaluates a two-step Retrieval Augmented Generation (RAG) approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance. The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework, AWS Threat Technique Catalogue, and threat reports to overcome limitations of… More >

Thi-Loan Nguyen^1,2,*, Huy-Nam Chu³, The-Thanh Hua³, Trung-Nghia Phung², Van-Hung Le^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076732 - 12 March 2026

Abstract To support the process of grasping objects on a tabletop for the blind or robotic arm, it is necessary to address fundamental computer vision tasks, such as detecting, recognizing, and locating objects in space, and determining the position of the grasping information. These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm. In this paper, we collected, annotated, and published the benchmark TQU-GraspingObject dataset for testing, validation, and evaluation of deep learning (DL) models for detecting, recognizing, and localizing grasping objects in 2D and 3D… More >

Yanping Chen¹, Zhengxin Zhan¹, Xiaohui Yan¹, Le Zou^1,*, Yucheng Zhong¹, Hailei Wang²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075459 - 12 March 2026

Abstract With the increasing complexity of substation inspection tasks, achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional (3D) environments remains a critical challenge. To address this problem, this paper proposes an improved path planning algorithm—Random Geometric Graph (RGG)-guided Rapidly-exploring Random Tree (R-RRT)—based on the classical Rapidly-exploring Random Tree (RRT) framework. First, a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering, noise removal, coordinate transformation, and obstacle inflation using spherical structuring elements. During the planning stage, a dynamic… More >

Xuehan Li¹, Tao Jing², Yang Wang², Bo Gao³, Jing Ai⁴, Minghao Zhu^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075426 - 12 March 2026

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 >

Yujie Tian¹, Ming Zhu¹, Jing Li^1,*, Cong Liu², Ziyang Zhang¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075063 - 12 March 2026

Abstract Workflow scheduling is critical for efficient cloud resource management. This paper proposes Tunicate Swarm-Highest Response Ratio Next, a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy. The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme, while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority. Experimental results demonstrate that the Tunicate Swarm-Highest Response Ratio Next reduces costs by up to 94.8% compared to meta-heuristic baselines. It also achieves competitive cost efficiency vs. a learning-based method while offering superior operational simplicity More >