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De-Duplicated Hierarchical Offloading in Vehicular Edge Computing With Task Dependencies

IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10293-10303页

作者： Liao, Zhuofan Shao, Zhenyi Zheng, Bin Tang, Xiaoyong Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China

In vehicular edge computing (VEC), most tasks require high real-time and energy requirements, but the mobility of vehicles and the difficulty of intelligent computing make it hard to meet these requirements. Due to the fact that most VEC tasks can be decomposed into smaller granularity, based on the dependencies between small subtasks, the repetition of tasks can be reduced, thereby improving task completion rates. In this work, we explore the dependencies of subtasks in different applications and design a two-stage multihop clustering de-duplication offloading (MCDO) mechanism. First, MCDO gives a multihop two-layer clustering (MTLC) algorithm to divide clusters based on similarities between different tasks. Based on this, MCDO further designs a de-duplication logical hierarchical offloading (DLHO). DLHO forms a directed acyclic graph (DAG) of de-duplicated subtasks in each cluster and offloads these subtasks in a logical hierarchical manner. Simulation results show that, compared to existing approaches PC5-GO, FedEdge, and MD-TSDQN, MCDO can achieve a minimum improvement of 15.1% in terms of latency and 20.8% in terms of energy consumption.

关键词： computational modeling Roads Energy consumption Resource management Internet of Things Delays Cloud computing Clustering algorithms Servers Edge computing Partial offloading resource allocation task dependency vehicle clusters vehicular edge computing (VEC)

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Tracking Control for Multi-Agent Systems With Model Switching and Topological Switching: A Novel Dual-Switch-Based Dynamic Event-Triggered Approach

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9353-9362页

作者： Zhang, Shuo Liu, Jinhai Zhang, Huaguang Wang, Wei Zhang, Zhigang Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

The event-triggered tracking consensus of multi-agent systems (MASs) with model switching, topological switching and non-zero leader inputs is concerned in this paper. In previous event-triggered studies on MASs, only a single switching behavior is considered and triggering parameters remain unchanged during switching, which shows more conservatism. In sharp contrast to existing research, this paper deals with two switching behaviors simultaneously, i.e. model switching and topological switching. Accordingly, dual-switch-based dynamic event-triggered rules are proposed to ensure better triggering performance. Further, dual-switch-based event-triggered control protocols are devised for agents. By constructing dual-switch-based multiple Lyapunov functions and utilizing the average dwell time (ADT) method, sufficient conditions are given to ensure the tracking exponential consensus. Finally, two examples of practical mass-spring systems with variable masses are carried out to certify the effectiveness and superiority of our approaches. Note to Practitioners-In real-world MASs, model switching and topological switching often coexist. Considering the limited computational and communication resources, it is of practical significance to conduct event-triggered research on MASs with model switching and topological switching. However, in existing event-triggered research on MASs, triggering control protocols are designed only for one type of switching behavior. To fill the research gap, this paper delves into the event-triggered control of MASs under both model switching and topological switching, offering greater practicality for engineering applications. Meanwhile, the devised triggering protocol is less conservative, ensuring better triggering performance and saving more computing and communication resources. In the simulation, the proposed approach is applied to practical mass-spring control problems under variable masses and switching topologies, and its practical appli

关键词： Switches Event detection Protocols Control systems Topology Power system dynamics computational modeling Vehicle dynamics Switched systems Multi-agent systems Switched multi-agent systems switching topologies dual-switch-based dynamic triggering rules non-zero leader inputs tracking consensus

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Attention-Enhanced AGRU Framework for Induction Motor Incipient Fault Diagnosis in Electric Vehicles

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Saleh, Mohammad AlShaikh Ghrayeb, Ali Refaat, Shady S. Abu-Rub, Haitham Khatri, Sunil P. Kammermann, Jorg Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield AL10 9EU Herts England Tech Univ Munich Sch Engn & Design D-80333 Garching Germany

Electric vehicles (EVs) are playing a pivotal role in transportation systems to conform to the rising exigencies for enhanced performance with safety and hindered environmental impact. Thus, to improve the efficiency and downsize the maintenance cost of EVs, an early fault diagnosis (FD) framework is crucial. Bearing and stator winding faults, which account for approximately 78% of induction machine (IM) incipient faults in EVs, remain rather elusive for conventional sensors to accurately classify them and their incipient values. To this end, this article presents a novel attention-enhanced autoencoder-gated recurrent unit (AAGRU) model that improves the accuracy and efficiency of fault analysis in IMs. Moreover, since bearing faults are usually captured through vibration sensors, which are expensive and require direct coupling with the IM, a hybrid signal reconstructor is devised based on merely stator current signals. The proposed model leverages empirical mode decomposition (EMD), fast Fourier transform (FFT), and discrete wavelet decomposition (DWT) to process the electric current data, which are then used by the AAGRU model to identify, detect, and classify the fault patterns. Experimental results demonstrate that the proposed model offers a 1%-6% improvement in fault detection and an 8%-28% improvement in interturn short-circuit (SC) fault severity classification relative to different shallow and deep-based benchmark models. The proposed model was also tested on different load conditions to ensure its applicability in practice.

关键词： Circuit faults Stator windings Data models Analytical models Fault diagnosis Integrated circuit modeling Convolutional neural networks Maintenance Feature extraction computational modeling Condition monitoring deep learning (DL) electric vehicles (EVs) incipient faults induction machines (IMs)

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Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring

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IEEE ACCESS 2025年 13卷 19302-19319页

作者： Kadhim, Estabraq Abdulreda Feizi-Derakhshi, Mohammad-Reza Aghdasi, Hadi S. Univ Tabriz Dept Comp Engn Computerized Intelligence Syst Lab Tabriz *** Iran Islamic Azad Univ Tabriz Dept Comp Engn Tabriz *** Iran

The most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence and accuracy. This study's improved summarization methodology overcomes these limitations by combining the linguistic and semantic resources. Moreover, although it is more computationally complex, it efficiently combines higher quality with faster summarization. Specifically, a method relies on a weighted feature score scheme. For example, various textual features such as Named Entity Counts, Noun Counts, and Sentence Position contribute to the summarization quality appropriately. This study's summarization algorithm was tested using the CNN, XSum and BBC Summarization datasets, which aggregate documents from different areas. The methodology was checked against traditional methods using ROUGE-1 and ROUGE-2, ROUGE-L and BERTScore. The last one, BERTScore, evaluates the semantic similarity of the generated summaries and the references. This study shows that the proposed methodology generates summaries that are not only informative but even semantically faithfully reproduce the original textual information;it achieves high scores in terms of F1-measure across different evaluations like BERTSCORE (0.8857) and ROUGE-1(0.6388), ROUGE-2(0.5662) and ROUGE-L (0.6421). It thus suggests that the approach is applicable in real-life situations and deserves further research.

关键词： Semantics Measurement Feature extraction Data mining Text summarization computational modeling Coherence Encoding computational efficiency Bidirectional control Extractive text summarization sentences scoring weighted features BBC-news dataset rouge metric BERTScore metric

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Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot

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IEEE ACCESS 2025年 13卷 11689-11703页

作者： Tan, Daiqing Zang, Hao Zhang, Xinyue Gao, Han Wang, Ji Wang, Zaijian Zhai, Xing Li, Huixia Tang, Yan Han, Aiqing Beijing Univ Chinese Med Sch Management Beijing 100029 Peoples R China Beijing Univ Chinese Med Sch Nursing Beijing 100029 Peoples R China Beijing Univ Chinese Med Sch Humanities Beijing 100029 Peoples R China Beijing Univ Chinese Med Natl Inst TCM Constitut & Prevent Med Beijing 100029 Peoples R China Beijing Univ Chinese Med Affiliated Hosp 3 Beijing 100029 Peoples R China

Objective: Tongue image segmentation is a crucial step in the intelligent recognition of tongue diagnosis in Traditional Chinese Medicine (TCM). Existing deep learning-based tongue image segmentation models face issues such as poor versatility and insufficient expressiveness in zero-shot tasks. This study aims to construct an efficient model with zero-shot suitable for tongue image segmentation in TCM. Methods: We developed the Tongue-LiteSAM model by improving the SAM (Segment Anything Model) framework to suit tongue segmentation. Based on the basic SAM model, the improvement involved modifying the image encoder by integrating two lightweight ViT-Tiny image encoders, effectively reducing the model's parameter count. Additionally, data perturbation techniques were employed to enhance the zero-shot segmentation capability of the model and ensure robust performance across different data sources. Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. Conclusion: The Tongue-LiteSAM model provides a more objective and consistent solution for tongue diagnosis, and has better zero-shot segmentation capabilities. By optimizing the model structure and data processing strategies, the accuracy and practicality of tongue diagnosis models are effectively improved, offering new technical support for the modernization and precision of TCM tongue diagnosis.

关键词： Image segmentation Tongue Training Data models computational modeling Accuracy Medical diagnostic imaging Transformers Deep learning Feature extraction Segment anything model tongue image segmentation traditional Chinese medicine

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MoCo: Fuzzing Deep Learning Libraries via Assembling Code

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第2期51卷 371-388页

作者： Ji, Pin Feng, Yang Wu, Duo Yan, Lingyue Chen, Penglin Liu, Jia Zhao, Zhihong Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

The rapidly developing Deep Learning (DL) techniques have been applied in software systems of various types. However, they can also pose new safety threats with potentially serious consequences, especially in safety-critical domains. DL libraries serve as the underlying foundation for DL systems, and bugs in them can have unpredictable impacts that directly affect the behaviors of DL systems. Previous research on fuzzing DL libraries still has limitations in generating tests corresponding to crucial testing scenarios and constructing test oracles. In this paper, we propose MoCo, a novel fuzzing testing method for DL libraries via assembling code. The seed tests used by MoCo are code files that implement DL models, covering both model construction and training in the most common real-world application scenarios for DL libraries. MoCo first disassembles the seed code files to extract templates and code blocks, then applies code block mutation operators (e.g., API replacement, random generation, and boundary checking) to generate new code blocks that fit the template. To ensure the correctness of the code block mutation, we employ the Large Language Model to parse the official documents of DL libraries for information about the parameters and the constraints between them. By inserting context-appropriate code blocks into the template, MoCo can generate a tree of code files with intergenerational relations. According to the derivation relations in this tree, we construct the test oracle based on the execution state consistency and the calculation result consistency. Since the granularity of code assembly is controlled rather than randomly divergent, we can quickly pinpoint the lines of code where the bugs are located and the corresponding triggering conditions. We conduct a comprehensive experiment to evaluate the efficiency and effectiveness of MoCo using three widely-used DL libraries (i.e., TensorFlow, PyTorch, and Jittor). During the experiments, MoCo detects 77 new

关键词： Codes Libraries Testing computational modeling Computer bugs Load modeling Fuzzing Neurons Deep learning Training Deep learning libraries deep learning testing bug detection fuzzing

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Adversarial Domain Generalization Defense via Task-Relevant Feature Alignment in Cyber-Physical Systems

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IEEE TRANSACTIONS ON RELIABILITY 2025年第2期74卷 2515-2528页

作者： Zhang, Sicheng Liu, Jie Bao, Zhida Yang, Yandie Wang, Meiyu Lin, Yun Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Hangzhou Dianzi Univ Coll Commun Engn Hangzhou 310018 Peoples R China

Automatic modulation classification (AMC) is a key technology in cyber-physical systems (CPSs), which enables the monitoring and identification of communication signals exchanged between devices. One of the most recognized solutions for the AMC is deep learning (DL), which can automatically learn and extract feature representations in signals. However, data-driven DL models are susceptible to adversarial examples, which can cause significant instability in the CPS. To tackle this issue, in this article, we examine the distribution shift between original signals and adversarial examples from a domain distribution perspective and present a system model for addressing the defense problem. We propose the adversarial domain generalization defense (ADGD) framework. The ADGD framework adopts a dual-stream architecture with the AMC as its central task, and extracts and constrains the maximum mean discrepancy distance between the task-relevant features of original signals and adversarial examples to reduce the distribution shift and improve the adversarial robustness. Comprehensive experiments and ablations were conducted to demonstrate the superiority of the proposed ADGD framework on the RML2016.10a and miniRML2018.01a datasets. The results indicate that the ADGD framework shows promising results in improving the adversarial robustness of AMC systems, which is crucial for the stability of the CPS.

关键词： Feature extraction Robustness Task analysis Training Modulation computational modeling Analytical models Adversarial defense automatic modulation classification (AMC) cyber-physical systems (CPSs) domain generalization (DG) features alignment

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Real-Time Diagnosis of Abrupt and Incipient Faults in IMU Using a Lightweight CNN-Transformer Hybrid Model

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IEEE SENSORS JOURNAL 2025年第7期25卷 12496-12510页

作者： Song, Jia Chen, Zhipeng Li, Wenling Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

The fault diagnosis is crucial for improving the reliability and safety of industrial sensors. Diagnosing faults in inertial measurement units (IMUs) is particularly challenging due to the complex nature of abrupt and incipient faults, which require the accurate and rapid diagnosis. This article presents a hybrid model that combines convolutional neural networks (CNNs) and Transformer encoder architectures. The CNN component effectively extracts local fault features, while the Transformer encoder captures long-range dependencies in time-series data, enabling the precise and rapid IMU fault diagnosis. To meet the autonomous and real-time operational demands of IMU fault diagnosis, the knowledge distillation is applied to develop a lightweight version of the model. This optimization facilitates efficient deployment on resource-limited hardware, maintaining the original model's accuracy and rapid processing speed. The effectiveness of the proposed approach is validated through comprehensive comparisons with other models, demonstrating the superior diagnostic accuracy, low fault diagnosis delay, and suitability for real-time applications.

关键词： Fault diagnosis Feature extraction Accuracy Real-time systems Sensors Transformers computational modeling Convolutional neural networks Trajectory Vibrations Convolutional neural network (CNN) inertial measurement unit (IMU) knowledge distillation real-time fault diagnosis sensor fault transformer

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FCER: A Federated Cloud-Edge Recommendation Framework With Cluster-Based Edge Selection

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 1731-1743页

作者： Wu, Jiang Yang, Yunchao Hu, Miao Zhou, Yipeng Wu, Di Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Macquarie Univ Fac Sci & Engn Dept Comp Sydney NSW 2112 Australia

The traditional recommendation system provides web services by modeling user behavior characteristics, which also faces the risk of leaking user privacy. To mitigate the rising concern on privacy leakage in recommender systems, federated learning (FL) based recommendation has received tremendous attention, which can preserve data privacy by conducting local model training on clients. However, devices (e.g., mobile phones) used by clients in a recommender system may have limited capacity for computation and communication, which can severely deteriorate FL training efficiency. Besides, offloading local training tasks to the cloud can lead to privacy leakage and excessive pressure to the cloud. To overcome this deficiency, we propose a novel federated cloud-edge recommendation framework, which is called FCER, by offloading local training tasks to powerful and trusted edge servers. The challenge of FCER lies in the heterogeneity of edge servers, which makes the parameter server (PS) deployed in the cloud face difficulty in judiciously selecting edge servers for model training. To address this challenge, we divide the FCER framework into two stages. In the first pre-training stage, edge servers expose their data statistical features protected by local differential privacy (LDP) to the PS so that edge servers can be grouped into clusters. In the second training stage, FCER activates a single cluster in each communication round, ensuring that edge servers with statistical homogenization are not repeatedly involved in FL. The PS only selects a certain number of edge servers with the highest data quality in each cluster for FL. Effective metrics are proposed to dynamically evaluate the data quality of each edge server. Convergence rate analysis is conducted to show the convergence of recommendation algorithms in FCER. We also perform extensive experiments to demonstrate that FCER remarkably out performs competitive baselines by 3.85%-9.14%on HR@10 and1.46%-11.77%on NDCG@10

关键词： Servers Training Privacy Recommender systems Data models Federated learning Clustering algorithms Convergence computational modeling Differential privacy recommender system privacy protections

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FairSTG: Countering Performance Heterogeneity via Collaborative Sample-Level Optimization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第5期24卷 4153-4168页

作者： Lin, Gengyu Zhou, Zhengyang Huang, Qihe Yang, Kuo Cheng, Shifen Wang, Yang Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Hefei 230026 Anhui Peoples R China State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 101408 Peoples R China

Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions on the overall dataset, they still neglect the significant performance heterogeneity across samples. In this work, we designate the performance heterogeneity as the reason for unfair spatiotemporal learning, which not only degrades the practical functions of models, but also brings serious potential risks to real-world urban applications. To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up. Specifically, FairSTG consists of a spatiotemporal feature extractor for model initialization, a collaborative representation enhancement for knowledge transfer between well-learned samples and challenging ones, and fairness objectives for immediately suppressing sample-level performance heterogeneity. Experiments on four spatiotemporal datasets demonstrate that our FairSTG significantly improves the fairness quality while maintaining comparable forecasting accuracy. Case studies show FairSTG can counter both spatial and temporal performance heterogeneity by our sample-level retrieval and compensation, and our work can potentially alleviate the risks on spatiotemporal resource allocation for underrepresented urban regions.

关键词： Spatiotemporal phenomena Mobile computing Forecasting Measurement Machine learning computational modeling Predictive models Resource management Performance evaluation Data models Fairness learning spatiotemporal forecasting representation learning self-supervised learning

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