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Reliable Task Offloading for 6G-Based IoT Applications

computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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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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Truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive Applications in Edge Clouds 18th

Truthful Double Auction-Based Resource Allocation Mechanisms...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wu, Dongkuo Wang, Xueyi Wang, Xingwei Huang, Min Wang, Zhitong College of Computer Science and Engineering Northeastern University Liaoning Shenyang China College of Software Northeastern University Liaoning Shenyang China College of Information Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9783031714696

With the rapid advancement of wireless networks, edge computing has emerged as a promising paradigm for providing computing services to nearby latency-sensitive applications. Toward this trend, resource trading markets among mobile users (MUs) and edge clouds are emerging which need well-designed allocation and pricing mechanisms. In this paper, we propose a double auction-based multi-market resource trading framework (DAMRT), which integrates the dynamic programming and padding-based double auction methods, aiming to achieve approximate social welfare maximization and guarantee the properties of truthfulness and budget balance in the constrained edge computing system. To be specific, for admission control among different base station (BS) central markets, a dynamic programming-based method is proposed to obtain the optimal BS-MU association set. For each market, a truthful padding-based double auction resource allocation mechanism (TPDA) is proposed, which uses a linear programming-based padding method and a binary search algorithm to obtain the near-optimal allocation solution, and leverages a critical-value and a VCG-based pricing strategy for winning MUs and service providers. Our theoretical analysis proves that TPDA achieves truthfulness, individual rationality, and budget balance. Furthermore, simulation results verify the effectiveness of DAMRT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Exploring the Privacy-Accuracy Trade-Off Using Adaptive Gradient Clipping in Federated Learning

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IEEE Transactions on Network science and engineering 2025年第3期12卷 2254-2265页

作者： Zhang, Benteng Mao, Yingchi He, Xiaoming Ping, Ping Huang, Huawei Wu, Jie Hohai University College of Computer Science and Software Engineering Nanjing211100 China Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing210003 China Sun Yat-Sen University School of Software Engineering Zhuhai519082 China Temple University Center for Networked Computing PhiladelphiaPA19122 United States

In Differentially Private Federated Learning (DP-FL), gradient clipping can prevent excessive noise from being added to the gradient and ensure that the impact of noise is within a controllable range. However, state-of-the-art methods adopt fixed or imprecise clipping thresholds for gradient clipping, which is not adaptive to the changes in the gradients. This issue can lead to a significant degradation in accuracy while training the global model. To this end, we propose Differential Privacy Federated Adaptive gradient Clipping based on gradient Norm (DP-FedACN). DP-FedACN can calculate the decay rate of the clipping threshold by considering the overall changing trend of the gradient norm. Furthermore, DP-FedACN can accurately adjust the clipping threshold for each training round according to the actual changes in gradient norm, clipping loss, and decay rate. Experimental results demonstrate that DP-FedACN can maintain privacy protection performance similar to that of DP-FedAvg under member inference attacks and model inversion attacks. DP-FedACN significantly outperforms DP-FedAGNC and DP-FedDDC in privacy protection metrics. Additionally, the test accuracy of DP-FedACN is approximately 2.61%, 1.01%, and 1.03% higher than the other three baseline methods, respectively. DP-FedACN can improve the global model training accuracy while ensuring the privacy protection of the model. All experimental results demonstrate that the proposed DP-FedACN can help find a fine-grained privacy-accuracy trade-off in DP-FL. © 2013 IEEE.

关键词： Federated learning

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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SLwF: A Split Learning Without Forgetting Framework for Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 12008-12020页

作者： Feng, Xingyu Jia, Renqi Luo, Chengwen Leung, Victor C. M. Xu, Weitao School of Information Engineering Beijing100190 China City University of Hong Kong Shenzhen Research Institute Hong Kong Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Split learning (SL) is widely regarded as a promising distributed machine learning framework with superior privacy-preserving properties, lower communication and computation costs. However, in real Internet of Things (IoT) scenarios, existing SL may not perform well because the local data of IoT devices often do not follow the same distribution. This leads to the model continuously adapting to the current data distribution in each training epoch, resulting in a catastrophic forgetting phenomenon. Existing methods typically attempt to add raw or generated data from previous devices in the current training epoch to review knowledge, but direct access to the local data of other devices carries serious privacy risks. Data augmentation techniques based on generative networks often have poor robustness and increase the computation cost on the device side. To address these challenges, we propose a new SL framework called SL without Forgetting (SLwF). To mitigate catastrophic forgetting without accessing any previous data, we propose a contrastive learning-based training method that leverages current training data to review previous knowledge, and learn new knowledge better. Furthermore, we adopt an exponential moving average (EMA)-based model update strategy to preserve lost knowledge, further alleviating the forgetting problem. We implement the SLwF framework in real IoT scenarios and extensively evaluated its performance using four publicly available datasets. Compared to other related research (e.g., IoTSL), SLwF performs better in terms of final accuracy and robustness while avoiding excessive device energy consumption. © 2014 IEEE.

关键词： Contrastive Learning

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CLS-3D: Content-Wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection in Autonomous Vehicles

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IEEE Access 2025年 13卷 69840-69856页

作者： Mushtaq, Husnain Latif, Sohaib Saad Bin Ilyas, Muhammad Mohsin, Syed Muhammad Ali, Mohammed Central South University School of Computer Science and Engineering Changsha410083 China Grand Asian University Department of Computer Science and Software Engineering Sialkot51040 Pakistan The University of Chenab Department of Computer Science Gujranwala50700 Pakistan COMSATS University Islamabad Department of Computer Science Islamabad45550 Pakistan Virtual University of Pakistan Standard College of Modern Sciences Lahore55150 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia Abha61421 Saudi Arabia

Accurate 3D object detection is vital in autonomous driving. Single-modal detectors, using either camera or LiDAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LiDAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3D (Content-wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection). This novel framework fuses LiDAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3D object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3D achieves state-of-the-art performance, with 89.52% 3D mAP and 94.08% BEV mAP, outperforming existing methods. © 2013 IEEE.

关键词： Depth perception

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A Combine CNN-RNN Based Approach for Augmenting the Performance of Speech Emotions Recognition

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Traitement du Signal 2025年第2期42卷 647-662页

作者： Sayed, Nasir Husnain, Ghassan Shoaib, Muhammad Ghadi, Yazeed Yasin Alajmi, Masoud Qahmash, Ayman Department of Computer Science CECOS University of IT and Emerging Science Peshawar25000 Pakistan Department of Computer Science Islamia College Peshawar Peshawar25100 Pakistan Department of Computer Science and Software Engineering Al Ain University Al Ain64141 United Arab Emirates Department of Computer Engineering College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Informatics and Computer Systems King Khalid University Abha61421 Saudi Arabia

Due to the advancement of neural networks and the increasing demand for accurate and real-time Speech Emotion Recognition (SER) in human-computer interactions, it is necessary to compare existing methods and databases in SER in order to arrive at feasible solutions and a complete understanding of this open-ended problem in SER. To detect and recognize the emotions expressed in speech, various techniques have been used in the literature, including well-established speech analysis and classification techniques. These techniques, including speech analysis and classification, have been used to extract emotions from signals. In this study, we propose a novel method for analyzing signals called Wavelet-Scaled Spectrogram which combines the frequency and scale spectrum of a signal using wavelet transform. This method is effective in analyzing signals at different scales and frequency content. In order to train models for speech emotion identification, a large number of handcrafted features and intermediary depictions i.e., frequency-time plot that have traditionally been utilized in data compilation, collection, and analysis. The development of end-to-end models which extract characteristics and learn directly from raw speech signals to improve speech recognition has recently been studied by researchers following the emergence of deep learning. After training and evaluation on the famous speech databases EmoDB, RAVDESS and IEMOCAP, the proposed model is evaluated on various speakers in both speaker-independent and speaker-dependent modes and on a variety of different voices. When advanced preprocessing techniques or data augmentation are omitted from the proposed architecture, the results demonstrate that it can produce products comparable to those produced by the current state of the art. Three concurrent CNN pipelines and a series of modified local features learning blocks (LFLBs) achieved the highest classification accuracy outperforming some advance state-of-the-approa

关键词： Recurrent neural networks

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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