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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

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arXiv 2022年

作者： Deng, Lang Yang, Jianfei Yuan, Shenghai Zou, Han Lu, Chris Xiaoxuan Xie, Lihua The School of Electrical and Electronics Engineering Nanyang Technological University Singapore The Department of Electrical Engineering and Computer Sciences University of California Berkeley United States The School of Informatics The University of Edinburgh United Kingdom

As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this paper, we propose a novel multimodal gait recognition method, namely GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, Channel State Information (CSI) that reflects the multi-path propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a Lightweight Residual Convolution Network (LRCN) as the backbone network, and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings

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arXiv 2023年

作者： Kazerouni, Amirhossein Azad, Reza Hosseini, Alireza Merhof, Dorit Bagci, Ulas School of Electrical Engineering Iran University of Science and Technology Iran Institute of Imaging and Computer Vision RWTH Aachen University Germany School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Northwestern University Chicago United States

Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a novel approach that enhances the control of the sinusoidal-based activation function in INRs using deep prior knowledge. INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function. Through a task-specific pre-trained model, INCODE adapts the task-specific parameters to optimize the representation process. Our approach not only excels in representation, but also extends its prowess to tackle complex tasks such as audio, image, and 3D shape reconstructions, as well as intricate challenges such as neural radiance fields (NeRFs), and inverse problems, including denoising, super-resolution, inpainting, and CT reconstruction. Through comprehensive experiments, INCODE demonstrates its superiority in terms of robustness, accuracy, quality, and convergence rate, broadening the scope of signal representation. Please visit the project’s website for details on the proposed method and access to the code. © 2023, CC BY.

关键词： Inverse problems

Tile Selection-based H.265/HEVC Coding

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Tile Selection-based H.265/HEVC Coding

International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Hakkı Burak Çardak Levent Çarkacioğlu Muhammed Oğuzhan Külekcı Department of Computer Engineering Istanbul Technical University Istanbul Turkey ASELSAN Inc. Ankara Turkey Istanbul Technical University Informatics Institute Istanbul Turkey

In the recent decade, video content production popularity has been increasing drastically with the support of advances in related technologies. In parallel with these developments, the increase in size and quality of video data has made data storage a problem, especially in projects with limited storage space. Various video compression algorithms have been developed to solve this problem. High-Efficiency Video Coding (HEVC) is one of them and it is an international standard developed for video *** this study, we propose a new method for tile-based video encoding by modifying the preprocess stage of the standard HEVC coding algorithm. The main idea of our study is to focus on the region of interest (ROI) in the compression algorithm and ignore the regions that do not intersect with the selected ROI. In this way, we aimed to reduce the computational complexity of the H.265/HEVC standard and achieve increased compression performance. We find this study valuable for real-time projects where disk space and computation time are critical.

关键词： Heuristic algorithms Memory Focusing Production Object detection Video compression Encoding

SecFedMDM-1: A Federated Learning-Based Malware Detection Model for Interconnected Cloud Infrastructures

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IEEE Access 2025年 13卷 101246-101261页

作者： Daniella Mughole Kalimumbalo Joke A. Badejo Kennedy O. Okokpujie Emmanuel Adetiba Molo Mbasa Joaquim Nzanzu Vingi Patrick Gabriel Oyeyemi Claude Takenga Department of Electrical and Information Engineering College of Engineering Covenant University Ota Nigeria Covenant Applied Informatics and Communication African Centre of Excellence Covenant University Ota Nigeria Département de Génie Electrique et Informatique Faculté des Sciences et Technologie Appliquées Université Libre des Pays des Grands Lacs Goma Democratic Republic of the Congo HRA Industrial Engineering Department Durban University of Technology Durban South Africa Department of Computer and Information Science College of Science and Technology Covenant University Ota Nigeria Entreprise NTIC Infokom GmbH Neubrandenburg Germany

Enforcing security and reliability in the cloud is a challenging but vital task because of the multitude of heterogeneous applications utilising the same infrastructure. Integrating a security analysis system to identify and mitigate potential threats, such as malicious software (Malware), is crucial for the effective functioning of cloud infrastructures. Over the past few decades, new malware analysis and detection approaches have emerged because of various malware strategies that evade host- and network-based security measures. This study established a federated learning-based malware detection model for interconnected cloud infrastructures. This method protects users’ privacy as numerous devices can collaborate to build machine learning models without sharing data. Three distinct deep-learning algorithms were chosen for the models’ training, validation, and testing phases. With the training of eight clients and twenty-five federation rounds, the FeedForward Neural Networks (FFNN) model performed best. It had accuracy, an F1-score, and a precision of 84%, while the Multi-Layer Perceptron (MLP) model performed with 83% accuracy, 83% F1-score, and 83% precision, and the Long Short-Term Memory (LSTM) model performed with 80% accuracy, 80% F1-score, and 80% precision.

关键词： Malware Cloud computing computer security Companies Data models Servers Federated learning Training Data security Data privacy

A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret

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arXiv 2022年

作者： Dey, Sheelabhadra Pendurkar, Sumedh Sharon, Guni Hanna, Josiah P. Department of Computer Science and Engineering Texas A&M University College StationTX United States School of Informatics University of Edinburgh Edinburgh United Kingdom

In various control task domains, existing controllers provide a baseline level of performance that - though possibly suboptimal - should be maintained. Reinforcement learning (RL) algorithms that rely on extensive exploration of the state and action space can be used to optimize a control policy. However, fully exploratory RL algorithms may decrease performance below a baseline level during training. In this paper, we address the issue of online optimization of a control policy while minimizing regret w.r.t a baseline policy performance. We present a joint imitation-reinforcement learning framework, denoted JIRL. The learning process in JIRL assumes the availability of a baseline policy and is designed with two objectives in mind (a) leveraging the baseline's online demonstrations to minimize the regret w.r.t the baseline policy during training, and (b) eventually surpassing the baseline performance. JIRL addresses these objectives by initially learning to imitate the baseline policy and gradually shifting control from the baseline to an RL agent. Experimental results show that JIRL effectively accomplishes the aforementioned objectives in several, continuous action-space domains. The results demonstrate that JIRL is comparable to a state-of-the-art algorithm in its final performance while incurring significantly lower baseline regret during training in all of the presented domains. Moreover, the results show a reduction factor of up to 21 in baseline regret over a state-of-the-art baseline regret minimization approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Multimodal feature learning framework for disease biomarker discovery

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Multimodal feature learning framework for disease biomarker ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Sudhir Ghandikota Jaswanth K. Yella Anil G. Jegga Department of Electrical Engineering and Computer Science University of Cincinnati OH USA Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center OH USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Most in silico biomarker discovery methods are based on a single disease data modality. However, they often fail to identify and understand the relationships between different biomarkers emerging from various data types. Heterogeneous multimodal analysis methods facilitate the integration of multiple disease-related data resources, allowing us to gain a more accurate understanding of the mechanisms involved in a disease. Multimodal biomarkers resulting from such integrated analyses are complementary and robust and enable a holistic understanding of the disease. In this study, we proposed a novel neural network model that integrates and analyzes data from tissue transcriptomics, protein-protein interactions, and gene functional annotation resources. We employed a network-based approach by constructing a heterogeneous knowledge graph consisting of different biomedical entities and their inherent relationships. In addition, we utilized contextualized gene features mined from the biomedical literature. Our multimodal framework accurately predicts the differential activity of genes in idiopathic pulmonary fibrosis (IPF). Evaluation experiments were performed to determine the benefits of implementing the multimodal approaches. We also employed transcription factor enrichment analysis (TFEA) to validate the learned features. Finally, the gene clusters identified using multimodal gene features in IPF were found to be biologically relevant and meaningful.

关键词： Training Measurement Knowledge engineering Computational modeling Biological system modeling Neural networks Lung

HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling

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HCA-NET: Hierarchical Context Attention Network for Interver...

IEEE International Symposium on Biomedical Imaging

作者： Afshin Bozorgpour Bobby Azad Reza Azad Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Fraunhofer Institute for Digital Medicine MEVIS Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub.

关键词： Adaptation models Accuracy Spinal cord Semantics Pose estimation Predictive models Skeleton

Validation of Thermoformed Piezoelectret Transducer for Partial Discharge Detection in Energized Power Transformers

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Validation of Thermoformed Piezoelectret Transducer for Part...

Conference on Electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： D.A.P. Ferreira R.A.P. Altafim F.S.I. De Souza F. Nakagawa R.A. Flauzino J.P.P. do Carmo R.A.C. Altafim Engineering School of São Carlos University of São Paulo - São Carlos-SP Brazil Computer Systems Department Informatics Center Federal University of Paraíba - João Pessoa-PB Brazil

A piezoelectret-based acoustic transducer was constructed for the purpose of detecting partial discharges (PD) in an energized power transformer located at a power substation. The transducer was developed using Teflon®-FEP films and a thermoforming method to create the piezoelectret. The resulting transducer, referred to as the thermoformed-piezoelectret transducer (TPT), exhibits a frequency response ranging from 5 kHz to 50 kHz, with a pronounced resonance around 30 kHz. To validate the performance of the TPT, two power transformers with identical characteristics were utilized. One of the transformers operated under normal conditions and served as a reference for acoustic noise and electromagnetic interference, while the other had previously been diagnosed with internal PD. The occurrence of PD within the second transformer was confirmed through three methods: Dissolved Gases Analysis (DGA), Infrared Thermographic Inspection (ITI), and PD acoustic detection. The results obtained from the PD acoustic detection method were compared with the data acquired from the TPT's recorded signal, as well as with the findings from the DGA and ITI analyses. This comparison revealed a strong correlation between the conventional acoustic transducer and the TPT.

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