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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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Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT

Natural Language to Verilog: Design of a Recurrent Spiking N...

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2024 International Conference on Neuromorphic systems, ICONS 2024

作者： Vitolo, Paola Psaltakis, George Tomlinson, Michael Licciardo, Gian Domenico Andreou, Andreas G. Johns Hopkins University Department of Electrical & Computer Engineering BaltimoreMD United States University of Salerno Department of Industrial Engineering IT Fisciano Italy Hellenic Mediterranean University Department of Electrical & Computer Engineering Heraklion Greece

ISBN: (纸本)9798350368659

This paper investigates the use of Large Language Models (LLMs) and natural language prompts to generate hardware description code, namely Verilog. Building on our prior work, we employ employ OpenAI's ChatGPT4 and natural language prompts to synthesize an RTL Verilog module of a programmable recurrent spiking neural network, while also generating test benches to assess the system's correctness. The resultant design was validated in three simple machine learning tasks, the exclusive OR, the IRIS flower classification and the MNIST hand-written digit classification. Furthermore, the design validated on on a Field-Programmable Gate Array (FPGA) and subsequently synthesized in the SkyWater 130 nm technology by using an open-source electronic design automation flow. The design was submitted to Efabless Tiny Tapeout 6. © 2024 IEEE.

关键词： Recurrent neural networks

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Medibot-Healthcare Robot Assistant for Remote Communities 20

Medibot-Healthcare Robot Assistant for Remote Communities

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Chakma, Andrea Noel, Emile University of Waterloo Department of Systems Design Engineering Waterloo Canada University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350378931

Across the world, the lack of proper healthcare resources in rural areas significantly impacts the physical and economic well-being of individuals living there. There is a need for multi-faceted solutions to help alleviate healthcare issues in these areas. Medibot is a robot which monitors the health of individuals in these areas and takes appropriate measures. Patients visit Medibot for daily non-invasive monitoring of their vitals and then receive updated guidance or are connected with a healthcare professional for immediate support. The robot provides an easy way to get continuous care for those with limited access to healthcare resources or facilities. © 2025 IEEE.

关键词： Nutrition

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Mitigating Overfitting in Bimodal Data Integration with GLOW for Large and Sparse Datasets 5th

Mitigating Overfitting in Bimodal Data Integration with GL...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Yu, Wentao Kolossa, Dorothea Nickel, Robert Electronic Systems of Medical Engineering TU Berlin Berlin Germany Electrical and Computer Engineering Bucknell University Lewisburg United States

ISBN: (纸本)9789819747108

The end-to-end training of neural networks with multimodal data poses challenges beyond those observed for the training with unimodal data. The difficulty lies frequently in a network’s capacity to overfit and generalize at different rates in each modality, resulting networks may thus perform worse than networks trained on unimodal data alone. In the present study, we introduce a late fusion methodology designed for the bimodal binary classification task under the assumption that the logit spaces of both modalities can be modeled by a joint Gaussian distribution. On the one hand, it is suitable when ample bimodal data is available, avoiding the effect of catastrophic fusion, where unimodal models may perform better than the bimodal systems. On the other hand, the proposed technique is particularly well suited for situations in which significant amounts of unimodal data are available to train separate networks for each modality, but where only a limited amount of joint, bimodal data is available to train the fusion mechanism. The proposed method is assessed over the PAN 2018 gender identification dataset, which involves the identification of an authors’ (binary) gender label through their tweets and posted images. The cross-validation results demonstrate that our proposed hybrid model-based method exhibits superior performance and robustness compared to competing state-of-the-art neural-network-based approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gaussian distribution

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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GHA: A Gated Hierarchical Attention Mechanism for the Detection of Abusive Language in Social Media

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IEEE Transactions on Affective Computing 2025年第2期16卷 946-959页

作者： Jarquín-Vásquez, Horacio Escalante, Hugo Jair Montes-Y-Gómez, Manuel González, Fabio A. Óptica y Electrónica Department of Computer Science Instituto Nacional de Astrofisica Puebla72840 Mexico Universidad Nacional de Colombia Department of Computer Systems and Industrial Engineering Bogotá16486 Colombia

The use of attention mechanisms in deep learning solutions has become popular within natural language processing tasks. The use of these mechanisms allows managing the relevance of the elements of a sequence in accordance with their context, however, this relevance has been observed independently between the pairs of elements of a sequence (self-attention) or between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues, we propose a dual attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of the sequence. Additionally, we propose the extension of the dual attention mechanism into a multi-layer perspective, through the weighted fusion of the different encoding layers of deep architectures. As the interpretation of abusive language is highly context-dependent, its identification is an ideal task to evaluate the proposed attention mechanism. Accordingly, we considered six standard collections for the abusive language identification task. The obtained results are encouraging;the proposed hierarchical attention mechanism outperformed the current self-attention and contextual attention mechanisms coupled with recurrent neural networks and Transformers, as well as, state-of-the-art approaches in detecting abusive language. © 2010-2012 IEEE.

关键词： Recurrent neural networks

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Cloud–Edge Collaboration for industrial Internet of Things: Scalable Neurocomputing and Rolling-Horizon Optimization

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IEEE Internet of Things Journal 2025年第12期12卷 19929-19943页

作者： Li, Qiyue Li, Lijuan Liu, Zhi Sun, Wei Li, Weitao Li, Jie Zhao, Wei Hefei University of Technology Engineering Technology Research Center of Industrial Automation Anhui Province School of Electrical Engineering and Automation Anhui Hefei230009 China University of Electro-Communications Department of Computer and Network Engineering Chofu182-8585 Japan Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Anhui University of Technology School of Computer Science and Technology Maanshan243099 China

Cloud–edge collaboration and edge intelligence have greatly driven the growth of the industrial Internet of Things (IIoT). However, the jittery network delay and limited computational resources of edge servers make it difficult to meet the stringent latency requirements in IIoT, and so far there is no good solution to solve this problem. To this end, we introduce scalable neurocomputing, which provides neural networks with different utilities and computation resource requirements, to be deployed on edge servers of cloud–edge IIoT systems. We then optimize such systems by formulating data scheduling and system computational resource allocation as an infinite horizon optimization problem, considering that the data collection from end devices is an infinite long-term process. To solve this hard problem, we design a rolling prediction-optimization framework that transforms the infinite horizon problem into a truncated finite horizon optimization that maximizes the average system utility while satisfying the stringent delay constraints. We have conducted extensive simulations and built a prototype system, which verify the feasibility and performance of our proposed scheme. © 2014 IEEE.

关键词： Cloud platforms

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TopoEEG: A Timesformer-Based Topographic Image Representation Method for Single-Trial Early Detection of P300 22

TopoEEG: A Timesformer-Based Topographic Image Representatio...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Hong, Jiazhen Najafizadeh, Laleh Rutgers University Integrated Systems and NeuroImaging Laboratory Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331520526

Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, detecting P300 from single trials has been challenging. As a result, multiple repetitions are typically needed, extending the BCI decision time. To address this problem, in this paper, we present TopoEEG, a novel method that generates a sequence of topography-based images from EEG, to capture both spatial and temporal information. These sequences are subsequently processed using the state-of-the-art video classification technique, TimeSformer. Evaluated using the BCI Competition II (IIb) dataset, results demonstrate that TopoEEG achieves single-trial P300 detection with F1-scores of 0.6830 and 0.7134 within the first 500 ms and 600 ms, respectively. These results highlight TopoEEG's potential to enhance the efficiency and speed of BCIs by reducing the need for multiple repetitions and the time required for decision-making in single-trial P300 detection. © 2025 IEEE.

关键词： Image enhancement

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