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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Ontology Mapping-Based Semantic Reasoning with OPC UA for Heterogeneous Industrial Devices

Ontology Mapping-Based Semantic Reasoning with OPC UA for He...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science in Lyle School of Engineering DallasTX United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9781665410205

The advent of smart manufacturing in Industry 4.0 signifies the arrival of the era of connections. As an excellent communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, a method named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. It extracts the information models of OPC UA servers into resource description framework triples, utilizes web ontology language for semantic enrichment and inference, and employs a semantic similarity model for event ontology mapping to improve query efficiency. The method's effectiveness is validated through functional queries using the SPARQL protocol in Apache Jena. The query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2024 IEEE.

关键词： Structured Query Language

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Multi-Indicator Water Quality Prediction Using Multimodal Bottleneck Fusion and ITransformer with Attention

Multi-Indicator Water Quality Prediction Using Multimodal Bo...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Li, Yibo Zhang, Xuan Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science in Lyle School of Engineering DallasTX United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9781665410205

Water quality prediction methods forecast the future short or long-term trends of its changes, providing proactive advice for water pollution prevention and control. Existing water quality prediction methods only consider the historical data of single-type or multi-type water quality. However, meteorology and other factors also have a significant impact on water quality indicators. Therefore, only considering the historical data of water quality is not feasible. Unlike existing studies, this work proposes a hybrid water quality prediction model called CMI to solve the above problem. Before prediction, CMI incorporates a multimodal fusion mechanism of water quality time series and remote sensing images of meteorological rainfall. Moreover, CMI integrates the model of ConvNeXt V2 and a multimodal bottleneck transformer to extract image features for fusing the time series and images. Furthermore, it utilizes an emerging model of iTransformer to realize prediction with the fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMI outperforms other state-of-the-art fusion algorithms, and the water quality prediction accuracy with fused meteorological data is 13% higher on average than that with only water quality time series. © 2024 IEEE.

关键词： Rain

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A New Technique of Soluble NPK Nutrient Detection System Using Deep Learning-Based Recurrent Neural Networks (RNN) 12

A New Technique of Soluble NPK Nutrient Detection System Usi...

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12th International Electrical engineering Congress, iEECON 2024

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University Of Technology Krungthep Department Of Electrical And Telecommunication Engineering Bangkok Thailand Thammasat School Of Engineering Thammasat University Faculty Of Engineering Department Of Electrical And Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department Of Robotic Engineering And Automation System Bangkok Thailand

ISBN: (纸本)9798350383591

Modern agriculture relies heavily on effective process monitoring, control, and remote data collection, making accurate and cost-effective sensors crucial for innovation. This research focuses on the development of a spectroscopy-based device for real-time and remote monitoring of nutrient levels in hydroponic systems. The device utilizes IoT architecture, incorporating principles of light absorbance to quantify concentrations of essential nutrients during crop growth. Three deep learning techniques, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) were adopted for data analysis. Results demonstrate that tailored implementation of deep learning can significantly enhance the performance of sensor network system (SNS). Specifically, the RNN format achieved an impressive 98% model accuracy during both training and testing phases. The technological advancements in addressing contract variation and photometric levels contribute to increased accuracy as more sensors become available. The findings serve as a comprehensive guide for collecting extensive agricultural data, encompassing environmental factors throughout the entire cultivation period, using state-of-the-art remote control sensor equipment. This work provides a practical, efficient, and cost-effective solution for precise nutrient control and continuous data monitoring in agricultural production. © 2024 IEEE.

关键词： Cost effectiveness

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Digital twin-based Intrusion Detection in Smart Grid : A Multi-kernel Knowledge Replay Approach

Digital twin-based Intrusion Detection in Smart Grid : A Mul...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Qihao Li, Mingyan Kang, Jiawen Hu, Fengye Jilin Univeristy College of Communication Engineering Changchun China Chongqing University College of Computer Science Chongqing China Guangdong University of Technology School of Automation Guangdong China

ISBN: (纸本)9798350351255

In this paper, we propose a novel intrusion detection scheme within smart grid networks, which is named digital twin-based multi-kernel knowledge replay (DtMKR) scheme. The scheme is designed to improve resilience against the noise and interference, and efficiently address the clustering of diverse and multi-sourced phasor measurement units (PMUs) packets. Specifically, we create channel-vectors to represent the packet features using the power gain and delay spread properties of the channel impulse response derived from the received packets. Then, we investigate the DtMKR scheme to alleviate the impact of noise and interference effects and enhance the precision of the malicious PMU packets detection from benign ones without the need for a comprehensive pre-established database of channel features for all PMUs in the network. In the scheme, the designed multi-kernel enabled Markov decision process (MDP) clustering functions are implemented to map the channel-vectors into a new feature space, thereby the dispersive effects on the channel-vectors are minimized. In addition, we consider both the realtime packet clustering and the paired learning scheme on the digital twin side, and utilize memory recall to mitigate the model overestimation problem and enhance local model robustness to optimize from a more diverse set of situations by replaying underrepresented experiences. Simulation results demonstrate the efficacy of the proposed DtMKR scheme in accurately identifying malicious packets originating from PMUs attackers, distinguishing them from benign traffic, and mitigating the impact of transmission impairments typical in environment. © 2024 IEEE.

关键词： Markov processes

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On non-von Neumann flexible neuromorphic vision sensors

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npj Flexible Electronics 2024年第1期8卷 638-663页

作者： Hao Wang Bin Sun Shuzhi Sam Ge Jie Su Ming Liang Jin Institute for Future Shandong Key Laboratory of Industrial Control TechnologySchool of AutomationQingdao UniversityQingdao 266071China College of Electronic and Information Engineering Qingdao UniversityQingdao 266071China Department of Electrical and Computer Engineering and Institute for Functional Intelligent MaterialsNational University of SingaporeSingapore 117583Singapore

The structure andmechanismof thehuman visual system contain rich treasures,and surprising effects can be achieved by simulating the human visual *** this article,starting from the human visual system,we compare and discuss the discrepancies between the human visual system and traditional machine vision *** the wide variety and large volume of visual information,the use of nonvon Neumann structured,flexible neuromorphic vision sensors can effectively compensate for the limitations of traditional machine vision systems based on the von Neumann ***,this article addresses the emulation of retinal functionality and provides an overview of the principles and circuit implementation methods of non-von Neumann computing ***,in terms of mimicking the retinal surface structure,this article introduces the fabrication approach for flexible sensor ***,this article analyzes the challenges currently faced by non-von Neumann flexible neuromorphic vision sensors and offers a perspective on their future development.

关键词： Neumann system starting

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A Comprehensive Framework for Decoding Salty Taste Information from EEG Signals: Distinguishing Brain Reactions to Saltiness of Comparable Intensity

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Food Science and Human Wellness 2024年

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering， Northeast Electric Power University Department of Computer Science， the University of Georgia School of Life Science and Technology， University of Electronic Science and Technology of China

The perception of salty taste is crucial for individuals to make healthy food choices. Yet， the brain electrophysiological signals underlying salty taste perception have been poorly described. In this study， electroencephalography (EEG) was used to record brain activity induced by NaCl solution as a salty taste stimulus. A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly. A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response signals. Applying this method to the EEG processing workflow can form a complete data processing framework. The results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period (0.2s)， and there was a sustained periodic response within 0.75s after the stimulation. Moreover， the salty taste information in the EEG signal was decoded， and discrimination of two similar concentrations of salty taste solutions was achieved far above the chance level (average identification rate: 89.66%). This study demonstrated experimental paradigms and research methods for understanding salty taste perception， which could provide references for research on other basic tastes.

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Optimal Operator-based Modeling for Open Circuit Voltage Hysteresis of LiFePO4 Batteries

Optimal Operator-based Modeling for Open Circuit Voltage Hys...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yan, Lisen Peng, Jun Wu, Yue Zhu, Zeyu Li, Heng Huang, Zhiwu School of Computer Science and Engineering Central South University China School of Electronic Information Central South University China School of Automation Central South University China

ISBN: (纸本)9781665410205

Accurate modeling of open circuit voltage hysteresis for LiFePO4 batteries is crucial for establishing an advanced battery model. However, existing hysteresis modeling methods often yield suboptimal results due to inadequate parameterization. This paper proposes an optimal modeling method for open circuit voltage hysteresis based on the Prandtl-Ishlinskii model and an associated parameterization method. First, an asymmetric operator with cubic envelope functions is designed to enhance the classical Prandtl-Ishlinskii model, which originally features a symmetric and linear operator. This modification enables the proposed model to accurately capture intricate hysteresis. Second, a hierarchical parameterization method is proposed to identify optimal parameters. Specifically, an improved grey wolf optimizer is employed to determine the operator-related parameters. Then, the remaining parameters are calculated using the least squares algorithm, enhancing computational efficiency. Finally, the proposed model is validated on the experimental hysteresis data from three distinct scenarios. The modeling error of the proposed model decreased by 66.57 % and 32.51 % compared with two other benchmark models. © 2024 IEEE.

关键词： Parameterization

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Clustering Model-based Analysis of Sales in Vegetable Categories for Supermarkets 2

Clustering Model-based Analysis of Sales in Vegetable Catego...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Liao, Jiana Chen, Jiajun Zheng, Wenxu Ren, Yazhou Guangdong University of Technology School of Automation Guangzhou China University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798331515669

The development of effective vegetable sales strategies is crucial for enhancing supermarket profitability, and these strategies are heavily reliant on a nuanced understanding of the sales volume distribution patterns across different vegetable categories. However, most existing studies fail to thoroughly analyze these distribution patterns, often neglecting factors such as sales volume magnitude and fluctuations. To address this gap, we first propose a comprehensive framework based on a K-means++ clustering model and Spearman correlation coefficient to analyze the sales data. Firstly, we normalize the data to eliminate the impact of differing dimensions across vegetable categories. Next, we analyze the sales data from both quarterly and daily perspectives. Data visualization is performed utilizing line charts, followed by a descriptive analysis of the sales volume distribution considering both magnitude and fluctuation. Furthermore, we utilize a K-means++ clustering model and the Spearman correlation coefficient to further explore the distribution patterns and interrelationships among different vegetable categories. The results of the experiments reveal the daily and quarterly sales distribution patterns of various vegetable categories, providing valuable insights to support the development of effective vegetable sales strategies for supermarkets. © 2024 IEEE.

关键词： Sales

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Detection of Edge-blurred Small Targets of Steel Surface Defects Based on SAM-YOLO 2

Detection of Edge-blurred Small Targets of Steel Surface Def...

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2nd IEEE International Conference on Electrical, automation and computer engineering, ICEACE 2024

作者： Wu, Yanxu Liu, Kai Zhou, Xianghao Zhang, Xiumei University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China South-Central Minzu University School of Computer Science Wuhan China Beihang University School of Automation Science and Electrical Engineering Beijing China University of Science and Technology Liaoning School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350368208

To address the issue of challenging detection tasks for tiny and medium-sized objects because of backdrop confusion and inadequate feature representation of steel surface flaws, this paper proposes an efficient feature-enhanced, feature-fused SAM-YOLO detector based on the YOLOv8 model. It adds a new feature fusion module MSFF based on the original YOLOv8 model and replaces the bottleneck module in the C2f module with a feature enhancement module SACM. These two modules improve the enhancement and detection of defect edge features, local area sensing, and the network capability of multi-scale feature fusion, and the effect of SAM-YOLO is verified through several experiments on the NEU-DET steel surface defect dataset. The mAP50 of SAM-YOLO reaches 77.9%. mAP50:95 reaches 49.1%, exceeding the original YOLOv8 baseline model by 6.9% and surpassing several other benchmark models. To confirm the legitimacy of each SAM-YOLO model module, ablation tests were conducted on the model in the interim. © 2024 IEEE.

关键词： Target tracking

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