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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South University School of Artificial Intelligence Guilin University of Electronic Technology The Department of Computer Science Universidad de Chile

In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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Resource Allocation for RSMA-Aided Multibeam Satellite Communications

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China Communications 2025年第2期22卷 241-255页

作者： Cui Huanxi Xiao Zhenyu Zhu Lipeng School of Electronic and Information Engineering Beihang UniversityBeijing 100091China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper investigates the resource allocation for rate-splitting multiple access(RSMA)enabled multibeam satellite communication ***,we minimize the total unmet user rate,which denotes the difference between the users’rate requirement and the practical achievable rate,as well as the total transmit power of the satellite by optimizing the precoding,power allocation,and rate allocation,under the per-feed power and rate *** solve the non-convex optimization problem,a twostage scheme is *** particular,in the first stage,we present a precoding scheme by maximizing the signal-to-leakage-plus-noise ratio of each beam to eliminate the inter-beam *** the second stage,we introduce auxiliary variables to obtain an upper bound on the objective function under the given precoding matrix and transform the rate constraints of the original problem into second-order cones(SOC)and linear matrix inequations(LMI).Then,the successive convex approximation(SCA)technique is used to obtain suboptimal power and rate allocation ***,the initial feasible solution for power allocation is provided by using the standard interior point ***,numerical results verify the superiority of our proposed solution compared to the benchmark methods in terms of objective function values.

关键词： multibeam satellite power allocation precoding rate allocation rate-splitting multiple access(RSMA)

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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering Sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering Sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI

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computers, Materials & Continua 2025年第4期83卷 1367-1398页

作者： Armughan Ali Hooria Shahbaz Robertas Damaševicius Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Applied Informatics Vytautas Magnus UniversityKaunas44309Lithuania

Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)*** identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor *** study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception *** were evaluated using two benchmark datasets,HAM10000 and Skin Cancer *** the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC *** findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin *** addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.

关键词： Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection

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ABA-IDS: Attention-Based Autoencoder for Intrusion Detection in Assistive Mobility Robotic Network

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IEEE Access 2025年 13卷 68785-68800页

作者： Ayo Oladele, Daniel Didam Markus, Elisha Central University of Technology Free State Electronic and Computer Engineering Department of Electrical Bloemfontein9301 South Africa

Innovative mobility aids like smart wheelchairs and walkers are transforming the lives of individuals with mobility challenges. These devices use sensors and in-vehicle network technologies, such as Controller Area Network (CAN), to enable real-time data exchange. While CAN is cost-effective, robust, and low-latency, it is vulnerable to cyber-attacks due to issues like message transmission on a shared bus, ID-based priority arbitration, and lack of encryption or authentication mechanisms. To mitigate these vulnerabilities, researchers have developed machine and deep learning-based intrusion detection systems (IDS). However, these systems often struggle with low feature learning and high model complexity, making them unsuitable for resource-constrained CAN networks. This paper introduces ABA-IDS, a low-footprint IDS that uses a ConvLSTM autoencoder with an attention mechanism to detect anomalies in assistive mobility networks. The model's design allows it to efficiently capture key spatial and temporal features of the network. By preprocessing CAN data based on CAN ID and transmission frequency, ABA-IDS combines frequency-based and content-based approaches to enhance anomaly detection. Anomaly scores are generated from reconstruction errors, and testing on a benchmark dataset showed that ABA-IDS outperformed existing models with fewer parameters. This makes ABA-IDS effective for deployment in resource-constrained in-vehicle networks, significantly improving the security of assistive mobility devices against cyber threats. The link to the project repository can be found here. © 2025 IEEE.

关键词： Network intrusion

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Distributed Attention-Enabled Multi-Agent Reinforcement Learning Based Frequency Regulation of Power Systems

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IEEE Transactions on Power Systems 2025年第3期40卷 2427-2437页

作者： Zhao, Yunzheng Liu, Tao Hill, David J. University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia

This paper develops a new distributed attention-enabled multi-agent reinforcement learning method for frequency regulation of power systems. Specifically, the controller of each generator is modelled as an agent, and the reward and observation are designed based on the characteristics of power systems. All the agents learn their own control policies in the offline training phase and generate frequency control signals in the online execution phase. The target of the proposed algorithm is to conduct both offline training and online frequency control in a distributed way. To achieve this goal, two distributed information-sharing mechanisms are proposed based on the different global information to be discovered. First, a consensus-based reward-sharing mechanism is designed to estimate the globally averaged reward. Second, a distributed observation-sharing scheme is developed to discover the global observation information. Furthermore, the attention strategy is embedded in the observation-sharing scheme to help agents adaptively adjust the importance of observations from different neighbors. With these two mechanisms, a new distributed attention-enabled proximal policy optimization (DAPPO) based method is proposed to achieve model-free frequency control. Simulation results on the IEEE 39-bus system and the NPCC 140-bus system demonstrate that the proposed DAPPO achieves stable offline training and effective online frequency control. © 1969-2012 IEEE.

关键词： Reinforcement learning

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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ToSiM-IoT: Towards a Sustainable Optimisation of Machine Learning Tasks in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 16829-16840页

作者： Kaushal, Ashish Almurshed, Osama Muftah, Asmail Auluck, Nitin Rana, Omer Indian Institute of Technology Department of Computer Science and Engineering Ropar India Cardiff University School of Computer Science and Informatics United Kingdom Azzaytuna University Libya

With the rise of digital infrastructure and Internet of Things (IoT), a substantial amount of data is continuously generated that needs to be processed efficiently. While modern artificial intelligence (AI) approaches have shown good capabilities in handling large volumes of data, their excessive demands for memory and processing power result in very high utilisation of resources. In this work, we propose ToSiM-IoT, an optimisation framework that introduces a layer selection approach to identify an ideal mix of active, and inactive layers, using a genetic algorithm for model training. Next, we design a pruning mechanism that identifies performance-critical features using heatmap visualisation, during model inference, and eliminates the remaining features. Two machine learning (ML) models - InceptionV3 and VGG16, have been evaluated on an agricultural weed detection scenario, using the DeepWeeds image classification dataset. Experimental results demonstrate that our framework can achieve a significant reduction in model size and training time, while maintaining high accuracy, for both models. Therefore, this approach provides the potential to be efficiently deployed on intelligent IoT systems where computational capabilities are limited. © 2025 IEEE.

关键词： Adversarial machine learning

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Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data 24

Vision Transformer for Audio-Based Depression Detection on M...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Pratiwi, Monica Sanjaya, Samuel Ady Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798400709586

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed depression can result in deteriorating mental well-being, which includes a higher risk of experiencing suicidal ideation and engaging in self-destructive actions. Voice analysis can be employed to distinguish between individuals with depression and those without depression. However, the research that has been conducted on voice recognition to distinguish between depressed and non-depressed individuals keeps focusing on the use of a single dataset source as input for the classification model that is being developed. This work utilizes the Vision Transformer model and various pre-trained transformers, such as DeiT (Data-Efficient Image Transformer) and Swin Transformer, to detect depression based on voice. The objective was to create a more generic model not restricted to a specific language. Integrating Mel-spectrogram characteristics with a vision transformer-based model can enhance the efficacy of voice recognition models when dealing with multi-language data. The result shows 21% higher accuracy than the previous study that also implemented a cross-dataset test. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spectrographs

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