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Resource prioritization and allocation in fog computing using hybrid optimization

Web Intelligence 2025年第Pre-press期Pre-press卷 1-22页

作者： Karpe, Sharmila Patil SH, Brahmananda Department of Computer Science and Engineering GITAM University GITAM School of Technology Bangalore Campus Karnataka India Department of Information Technology Walchand Institute of Technology Maharashtra Solapur India

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs. © 2024 – IOS Press. All rights reserved.

关键词： Resource allocation

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Prognostic Survival Analysis for AD Diagnosis and Progression Using MRI Data: An AI-Based Approach

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IEEE Access 2025年 13卷 89059-89078页

作者： Deenadayalan, T. Shantharajah, S.P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes. © 2013 IEEE.

关键词： Deep learning

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Infrared Small Target Detection with UAV Based on Convolutional Neural Networks

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engineering Letters 2025年第5期33卷 1505-1512页

作者： Zhang, Jingshi Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan 114051 China

Infrared target detection is now applied in many fields, such as medical imaging, military detection, autonomous driving, and environmental monitoring with drones. Due to the small size of these targets, complex environments, and limited contextual information, target detection is challenging. Therefore, this paper proposes an improved method for detecting small infrared targets based on the YOLOv9-c ***, we propose dynamic snake convolution (DSConv), a mechanism designed to precisely model tubular structures by adaptively concentrating on elongated and intricate curvilinear features within local regions. Second, we employ a new spatial and channel synergistic attention module (SCSA), integrating the SCSA module into the neck network of YOLOv9. The SCSA framework integrates two synergistic components, the Shared Multi-Semantic Spatial Attention (SMSA) module and the Progressive Channel Self-Attention (PCSA) module. This enhances the synergy between spatial and channel attention across multiple semantic levels. Finally, we introduce the C2F module into multi-scale dilated attention, altering its original dilation rates. C2F MSDA better aggregates semantic information, effectively reducing the redundancy of self-attention mechanisms. This improvement has shown promising results on the public HIT-UAV dataset, raising the average precision to 0.902 and providing potential technological support for the detection of small infrared targets. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanism Dynamic convolution Infrared small target detection YOLOv9

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Robust Detection of Malicious Encrypted Traffic via Contrastive Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4228-4242页

作者： Shen, Meng Wu, Jinhe Ye, Ke Xu, Ke Xiong, Gang Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Computer Science Beijing100081 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Institute of Information Engineering Chinese Academy of Sciences Beijing100190 China

Traffic encryption is widely used to protect communication privacy but is increasingly exploited by attackers to conceal malicious activities. Existing malicious encrypted traffic detection methods rely on large amounts of labeled samples for training, limiting their ability to quickly respond to new attacks. These methods also are vulnerable to traffic obfuscation strategies, such as injecting dummy packets. In this paper, we propose SmartDetector, a robust malicious encrypted traffic detection method via contrastive learning. We first propose a novel traffic representation named Semantic Attribute Matrix (SAM), which can effectively distinguish between malicious and benign traffic. We also design a data augmentation method to generate diverse traffic samples, which makes the detection model more robust against different traffic obfuscation strategies. We propose a malicious encrypted traffic classifier that first pre-trains a model via contrastive learning to learn deep representations from unlabeled data, then fine-tunes the model with a supervised classifier to achieve accurate detection even with only a few labeled samples. We conduct extensive experiments with five public datasets to evaluate the performance of SmartDetector. The results demonstrate that it outperforms the state-of-the-art (SOTA) methods in three typical scenarios. Specifically, in the evasion attack detection scenario, SmartDetector achieves an F1 score and AUC above 93%, with average improvements of 19.84% and 18.17% over the SOTA method, respectively. © 2005-2012 IEEE.

关键词： Data obfuscation

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Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

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IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

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Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4619-4634页

作者： Yu, Xiaoyan Zhou, Shen Li, Huafeng Zhu, Liehuang Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China

The use of a single image restoration framework to achieve multi-task image restoration has garnered significant attention from researchers. However, several practical challenges remain, including meeting the specific and simultaneous demands of different tasks, balancing relationships between tasks, and effectively utilizing task correlations in model design. To address these challenges, this paper explores a multi-expert adaptive selection mechanism. We begin by designing a feature representation method that accounts for both the pixel channel level and the global level, encompassing low-frequency and high-frequency components of the image. Based on this method, we construct a multi-expert selection and ensemble scheme. This scheme adaptively selects the most suitable expert from the expert library according to the content of the input image and the prompts of the current task. It not only meets the individualized needs of different tasks but also achieves balance and optimization across tasks. By sharing experts, our design promotes interconnections between different tasks, thereby enhancing overall performance and resource utilization. Additionally, the multi-expert mechanism effectively eliminates irrelevant experts, reducing interference from them and further improving the effectiveness and accuracy of image restoration. Experimental results demonstrate that our proposed method is both effective and superior to existing approaches, highlighting its potential for practical applications in multi-task image restoration. © 1991-2012 IEEE.

关键词： Restoration

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FDCPNet:feature discrimination and context propagation network for 3D shape representation

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 83-94页

作者： Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.

关键词： 3D shape representation Mesh model MeshNet Feature discrimination Context propagation

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A robust visual information hiding framework based on HVS pixel adaptive alpha blending (HPAAB) technique

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Multimedia Tools and Applications 2025年第14期84卷 13959-13982页

作者： Panda, Bishwabara Nayak, Manas Ranjan Mallick, Pradeep Kumar Basu, Abhishek School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of ECE RCC Institute of Information Technology Kolkata India

The popularity of the Internet and digital consumer gadgets has fundamentally changed our society and daily lives by making digital data collection, transmission, and storage exceedingly easy and convenient. However, protecting these data and preventing unauthorized changes is an important task. This challenge has arisen in several domains, including copyright protection, content authentication, information concealment, and protected communications. Several academics have developed digital watermarking algorithms to solve this issue, aiming to implant hidden data (a watermark) in digital material to affix a mark on or seal the digital data content. The proposed approach builds a spatial domain image watermarking approach using entropy, a spectral residual pixel-based saliency map, and alpha blending. This method states that the value of entropy improves the concealing capacity. To increase imperceptibility, a spectral residual-based saliency map is used to discover less visible portions of the input images and progressively incorporate more information based on entropy data. Any modifications made other than those detectable regions will be less noticeable to viewers since they result from saliency, which decides which elements of an image are the most visible from a visual perspective. Lastly, utilizing the quality criteria, the mechanism's capability is analyzed for numerous existing frameworks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Entropy

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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