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A Trust-Based Computation Offloading Framework in Mobile Cloud-Edge computing Networks

IEEE Transactions on Mobile computing 2025年第6期24卷 5370-5385页

作者： Wang, Jianhui Li, Zhetao Liu, Haolin Qiu, Tie Luo, Hongbin Jinan University College of Information Science and Technology Guangzhou510632 China Xiangtan University School of Computer Science Xiangtan411105 China Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300350 China Beihang University School of Computer Science and Engineering Beijing100191 China

Cloud service centers (CSCs) can purchase edge computation resources to improve service quality in mobile cloud-edge computing networks. However, edge servers (ESs) are owned by different entities, and dishonest entities may launch computational forgery attacks, i.e., the ES falsely reports its idle computation resources to win more tasks for increased revenue. Most existing approaches ignore the threat of dishonest ESs. To address the challenges, we design a Trust-based Computation Offloading (TCO) framework. First, we construct the problem for minimizing the difference between the CSC's cost and the expected revenue (DCER), which is a mixed-integer nonlinear programming problem. Second, we develop a trust-based computation offloading method that quickly finds a good solution by decomposing the problem. Finally, a two-tier trust evaluation method was proposed to obtain accurate trust values. Experimental results indicate that TCO's comprehensive performance surpasses the benchmarks and significantly enhances computation offloading reliability with a lower performance loss. Notably, tasks are preferentially offloaded to honest ESs to ensure their revenue and promote ESs’ honesty under the TCO framework. Additionally, compared with no trust mechanisms, TCO reduces the service timeout count in an interval by 34.37% - 73.80% with a performance loss of only 1.42% - 4.10%. © 2002-2012 IEEE.

关键词： Cloud platforms

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Constructive Interference Precoding for IRS-NOMA Networks

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3964-3978页

作者： Cui, Ke Wang, Wei Dong, Chao Zhao, Nan Wu, Qihui Niyato, Dusit Dalian University of Technology School of Information and Communication Dalian116024 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210016 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

Owing to the ability of reconfiguring wireless channels, intelligent reflecting surface (IRS) can help non-orthogonal multiple access (NOMA) to release its tremendous potential. However, the inter-user interference becomes the bottleneck of IRS-NOMA networks. To tackle this challenge, we propose two constructive interference precoding (CIP) based countermeasures in this paper for interference exploitation in IRS-NOMA networks. Specifically, the first scheme makes the residual interference from higher-order users (HUs) be constructive to lower-order users (LUs), so that the interference-free decoding can be achieved. While the second scheme directly utilizes the interference from LUs for the signal reception of HUs to avoid successive interference cancellation (SIC). The transmit power is minimized by jointly optimizing the BS active beamforming and the IRS passive beamforming for the two schemes, subject to the signal-to-interference-plus-noise ratio (SINR) requirement of each user, SIC decoding constraints, constructive condition and IRS unit-modulus constraint. Due to the coupled variables and non-convex constraints, we first decompose each problem into two subproblems, and then apply successive convex approximation (SCA) to convert them into convex ones. Finally, an alternating optimization (AO) based algorithm is proposed to solve the two convex subproblems for each scheme iteratively. Simulation results are presented to show the superiority and applicability of the proposed schemes compared to benchmarks. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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New Approach of Hybrid Momentum Exchange Devices for Spacecraft Attitude Control System

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Guidance,Navigation and Control 2025年第1期05卷 115-137页

作者： Haytham M.Gamal Mohammed A.A.Desouky Mahmoud Ashry Ahmed Esmat Hussien Astronautics Engineering Department Military Technical College Egyptian Armed Forces College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Smart Village Higher Colleges of Technology

Control moment gyroscopes(CMGs) are a favorable choice for spacecraft attitude control systems thanks to their torque amplification capability. However, their performance is hindered by geometric singularities, which can severely limit control capabilities during attitude maneuvers. This paper proposes a hybrid attitude control system incorporating a single reaction wheel into the standard minimum redundant four-CMG pyramid configuration, providing an additional degree-of-freedom to avoid and escape singularities effectively. A comprehensive mathematical analysis of the system's Jacobian matrix,using row echelon form, is performed alongside a geometrical analysis of the hybrid configuration's momentum envelope. These analyses demonstrate the reflection of a reaction wheel inclusion into the system of four CMGs in the improvement of the system's capacity to avoid/escape singularity. Additionally, an asymptotic control law and an adapted steering law are developed to optimize control performance. The effectiveness of the proposed hybrid system is validated through simulation, which includes a comparative analysis with traditional CMG-only system. The results highlight the superiority of the hybrid system in handling singularities.

关键词： Control moment gyro singularity reaction wheel attitude control system

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Early Diabetic Retinopathy Cyber-Physical Detection System Using Attention-Guided Deep CNN Fusion

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 1898-1910页

作者： Hossain, M. Shamim Shorfuzzaman, Mohammad King Saud University Research Chair of Pervasive and Mobile Computing Department of Software Engineering College of Computer and Information Sciences Riyadh12372 Saudi Arabia Taif University Department of Computer Science College of Computers and Information Technology Taif21944 Saudi Arabia

Diabetic retinopathy is the most common and severe eye complication of diabetes, and it can cause vision loss or even blindness due to retina damage. Automatic and faster detection of various DR stages is crucial and can benefit both patients and ophthalmologists. With the ubiquity of measurement devices and growing processing power, cyber-physical characterization is becoming an enabler technology in many disciplines. This paper proposes a cyber-physical system (CPS) framework that will aid clinicians in establishing an early diagnosis of DR. Particularly;we present a component-based CPS architecture to use a deep learning-based predictive model deployed on the cloud for effective DR diagnosis and incorporate medical devices. To this end, a deep learning-based explainable CNN fusion model has been introduced in the proposed framework for automatic screening and interpretation of DR stages using digital fundus images. We extract salient features using various fine-tuned CNN models in conjunction with an attention network, and we use a locally connected layer to calculate the weighted contribution of these networks. We measure the performance of our approach using a public dataset comprising fundus images of five different categories. Experimental results demonstrate the proposed approach's effectiveness for faster and more accurate detection of various DR stages. © 2013 IEEE.

关键词： Biomedical equipment

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ABL-SMOTE: A Novel Resampling Method by Handling noisy and borderline challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Ghorashi, Sara Abdelwahab Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University P.O. Box 84428 Collage of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Classification of nuclei in whole slide imaging based on ensemble deep learning

Classification of nuclei in whole slide imaging based on ens...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Al-Thelaya, Khaled Alzubaidi, Mahmood Majeed, Fahad Ullah, Gilal Nauman Agus, Marco Schneider, Jens Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781032803722

Recently, several innovative digital imaging technologies led to the development of several medical imaging data representations. Whole slide imaging (WSI) refers to capturing and storing tissue glass slides and biopsy samples in a digital form. This newly emerging modality is increasingly gaining huge interest in pathology departments worldwide due to the unique advantages introduced by this medical imaging modality for diagnostic, educational, and research purposes. This work provides a comparative analysis of multiple classification approaches of nuclei in WSI where multiple nuclei context-aware manipulation strategies are evaluated by our nuclei classification experiments. We propose a contextaware deep ensemble approach based on ResNet deep architecture using different forms of input images pre-processed using multiple manual feature extraction techniques. Results show that our approach attains higher accuracy performance compared to traditional context-ware strategies in the literature. © 2025 The Author(s).

关键词： Medical imaging

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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization

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IEEE Access 2025年 13卷 72375-72388页

作者： Aldhahri, Eman Ali Almazroi, Abdulwahab Ali Alkinani, Monagi Hassan Ayub, Nasir Alghamdi, Elham Abdullah Janbi, Nourah Fahad University of Jeddah Collage of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21959 Saudi Arabia University of Jeddah College of Computing and Information Technology at Khulais Department of Information Technology Jeddah21959 Saudi Arabia Air University Islamabad Department of Creative Technologies Islamabad44000 Pakistan

Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA's Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA's statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy's significant environmental stress and crop health assessments. © 2013 IEEE.

关键词： Monitoring Crops Temperature measurement Adaptation models Temperature sensors Plants (biology) Accuracy Predictive models Urban agriculture Deep learning

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ALCS-PP: Adaptive Latency Control Scheme for Packet Pacing in Self-Clocked Congestion Control

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IEEE Access 2025年 13卷 77080-77123页

作者： Zubaydi, Haider Dhia Jagmagji, Ahmed Samir Molnár, Sándor Alzubaidi, Mahmood Department of Telecommunications and Artificial Intelligence Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Hungary Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

Effective congestion control is crucial for maintaining network stability, especially in dynamic environments. Self-Clocked Rate Adaptation for Multimedia (SCReAM) is a hybrid loss and delay-based algorithm for real-time multimedia transmission. It encounters challenges with static pacing and low latency under fluctuating round-trip time (RTT) and jitter resulting in ineffective bandwidth utilization. To address these issues, we propose ALCS-PP, a novel congestion control scheme featuring dynamic rate adjustment and adaptive packet pacing. The evaluation process is divided into three phases. The first phase involves initial performance evaluation using default parameters to establish a baseline comparison. The next phase explores parameter-specific experiments, where individual parameters such as target latency, jitter thresholds, and rate scaling factors are tuned to assess their impact on performance. The last phase investigates multi-parameter combinations to optimize the overall system configuration under various network conditions. Our proposed scheme effectively reduced network queue delay (NQD) by up to 21.91% and smoothed RTT (sRTT) by 13.44%, ensuring better responsiveness in real-time multimedia transmission. These improvements are achieved through adaptive tuning using dynamic soft clamping and gradual corrections, minimizing performance oscillations. Additionally, the scheme maintains metrics such as network throughput (NThput), congestion window (CWND), and bytes in flight (BiF) close to SCReAM’s baseline values, ensuring stable performance. The experimental evaluation highlights the algorithm’s capability to balance delay reduction and network stability, making it suitable for delay-sensitive applications such as 5G, Internet of Things (IoT), and real-time multimedia streaming. © 2013 IEEE.

关键词： Adaptive control systems

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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