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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34255 条记录，以下是211-220 订阅

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Edge computing model for coupled monitoring of conductor sag and safety distance risk 6

Edge computing model for coupled monitoring of conductor sag...

引用

6th international conference on Wireless Communications and Smart grid, ICWCSG 2024

作者： Ye, Jufen Yang, Ting Hu, Jianghua Tan, Junyuan Chen, Yiming Dong, Zhixing Dongguan Power Supply Bureau of Guangdong Power Grid Guangdong Dongguan523000 China School of Electrical Engineering and Automation Wuhan University Wuhan Hubei430000 China

ISBN: (纸本)9781510682962

The safe operation of transmission lines is crucial for the transmission of electrical energy, and arc sag and safety distance have attracted extensive research as important parameters for measuring whether transmission lines are operated safely or not. In this paper, we present an innovative edge computation model for monitoring arc sag and safety distance in transmission lines. The model combines real-time data collection with a localized computational process to ensure timely and accurate detection of risks faced by transmission lines. By utilizing the distributed capabilities of edge computing, the model reduces latency and improves monitoring response time. © 2024 SPIE.

关键词： Mobile edge computing

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Five-layer IoT and Fog computing Framework Towards Digital Twinning of Battery Management Systems for e-Transportation 4

Five-layer IoT and Fog Computing Framework Towards Digital T...

引用

4th international conference on Smart grid and Renewable Energy, SGRE 2024

作者： Karnehm, Dominic Samanta, Akash Neve, Antje Williamson, Sheldon University of the Bundeswehr Munich Institute of Intelligent Distributed Systems Neubiberg Germany Ontario Tech University Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350306262

It is needless to mention that the proven capability of cloud computing and digital twin-based monitoring and control systems will play a major role in the implementation of digital twin-based lithium-ion battery management systems (BMS) for e-transportation. In this context, the combination of the Internet of Things (IoT) and fog computing emerges as an enabling technology that facilitates bidirectional data communication and processing. This paper proposes a five-layer IoT framework for the implementation of cloud-based battery monitoring and BMS. Additionally, it critically analyzes the existing IoT frameworks applied to BMS and justifies the suitability of the five-layer architecture. Furthermore, the advantages of fog computing, such as low latency, fault tolerance, limitless systems, multiple applications, and shared responsibility, are discussed. The paper also explores four previously proposed computing-enabled architectures: centralized, hybrid, distributed, and hybrid-distributed computing. Moreover, the potential of utilizing hybrid computing architectures, incorporating fog computing as a supplement to cloud computing, is highlighted to address fault, privacy, or latency issues in cloud-based BMS. © 2024 IEEE.

关键词： Battery management systems

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Poster: Accessible, distributed Hydro-Surveillance through Integrated End-Edge-Cloud Architecture 43

Poster: Accessible, Distributed Hydro-Surveillance through I...

引用

43rd IEEE international conference on distributed computing Systems (ICDCS)

作者： Chen, Chen Yao, Guorun Cong, Li Han, Wei Bai, Shi He, Ci Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China State Grid Jilin Prov Elect Power Co Ltd Informat Changchun Peoples R China Xian Beilin Univ Based Innovat Grp Co Ltd Xian Peoples R China Inner Mongolia Autonomous Reg Hydrol & Water Reso Beijing Peoples R China 54th Reasearch Inst China Elect Technol Grp Corp Shijiazhuang Peoples R China

ISBN: (纸本)9798350339864

Flood damage is a devastating natural disaster that requires effective hydro-surveillance (HS) systems. However, the limited capabilities of edge servers (ES) make it challenging to develop such systems. Our study proposes an accessible, distributed HS and computing system to address flood damage. To increase inference efficiency on ES, we develop a HS model and combine it with a collaborative scheduling algorithm to construct an integrated end-edge-cloud (iEEC) computing pathway. Our system achieves 94.3% accuracy and a speed of 30.3 frames per second (FPS), outperforming state-of-the-art (SOTA) approaches, and can handle hard instances. The prototype system and simulation tool demonstrate the effectiveness of our approach at various scales.

关键词： Collaborative Scheduling Hydrological Surveillance Integrated End Edge Cloud Architecture

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Efficient GPU processing method to analyze large GWAS data sets 32

Efficient GPU processing method to analyze large GWAS data s...

引用

32nd Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Agapito, Giuseppe Guardasole, Gaetano Cannataro, Mario Magna Graecia Univ Catanzaro Dept Law Econ & Social Sci Catanzaro Italy Univ Calabria ex Expert ICT Arcavacata Di Rende Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci Catanzaro Italy

ISBN: (纸本)9798350363074;9798350363081

Genome-wide association studies (GWAS) have expanded rapidly, generating large genetic data sets that require advanced computational strategies for efficient analysis. This study introduces a novel method that uses Graphics Processing Units (GPUs) to analyze large scale GWAS data sets, enhancing computational efficiency and reducing processing time. Our approach leverages GPUs' inherent parallel processing capabilities to significantly accelerate computation-intensive tasks commonly encountered in GWAS, such a Genetic Risk Score (GRS). We explain how our GPU-GRS computational approach outperforms traditional CPU (Central Processing Unit) methods, outlining the architectural optimizations that enable parallel processing to handle large genomic data sets more effectively. We also demonstrate the scalability of our approach by analyzing increasingly large synthetic GWAS data sets, showcasing its ability to manage the growing size and complexity of genetic data efficiently. Our findings suggest that adopting GPU-based methods in GWAS analysis can play a pivotal role in the era of big genomic data, offering a path towards more time-efficient and scalable solutions.

关键词： Genome-wide association studies Genetic Risk Score parallel computing CPU GPU

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PS-Based Heterogeneous computing Framework 6

PS-Based Heterogeneous Computing Framework

引用

6th international conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Guo, Xi He, Ming Qin, Shouhao Wang, Dexuan Li, Chenjun China United Network Communications Corporation Research Institute Beijing China GuoChuang Software Co. Ltd. Hefei China

ISBN: (纸本)9798350388374

In the current heterogeneous computing environment, different types of accelerated computing resources such as GPU(Graphics Processing Unit) and NPU(Neural Processing Unit) coexist in the cluster. However, because these resources use different communication protocols and architectures, heterogeneous computing resources cannot effectively communicate and collaborate. To solve this problem, we propose a heterogeneous distributed computing framework based on the Parameter Server(PS) architecture and achieve heterogeneous parallel computing capabilities across hardware devices. This allows users to uniformly use all available heterogeneous computing resources in the cluster to perform large-scale data parallel deep learning training tasks. We have conducted experiments and evaluations on this framework in multiple scenarios, and the results show that the NPU and GPU computing power in a heterogeneous computing cluster can be used simultaneously to complete a same computing task without affecting accuracy, while ensuring high training efficiency. Overall, this framework provides an effective solution for high-performance distributed computing in heterogeneous computing environments. © 2024 IEEE.

关键词： computing power

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Constructing and optimizing a digital intelligent power grid synchronous platform integrating big data and artificial intelligence

Constructing and optimizing a digital intelligent power grid...

引用

2024 international conference on Electronics, Electrical and Information Engineering, ICEEIE 2024

作者： Xudong, Liu Jinzhu, Cui Qiubo, Ma Chengbi, Xia Xin, Tang State Grid Gansu Province Electric Power Company Qingyang Power Supply Company Gansu Qingyang745000 China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

With the continuous growth of power demand and the increasingly complex operating environment of power grid, the traditional power grid management model has been difficult to meet the needs of modern management. Therefore, by integrating big data technology and AI (artificial intelligence) algorithm, this study aims to build a high-performance and intelligent power grid management platform, namely, DIPGVRSP (Digital Intelligent Power grid Virtual-Real Synchronous Platform). During the construction of DIPGVRSP, a layered architecture design is adopted, including data acquisition layer, data processing layer, data analysis layer and application layer. In order to further optimize the performance and efficiency of DIPGVRSP, this study puts forward a number of strategies, including data processing efficiency optimization, intelligent scheduling and optimization, and security and reliability improvement. By introducing parallel processing and distributed computing technology, the data processing speed is significantly improved. At the same time, based on historical data and machine learning model, a predictive scheduling algorithm is developed to realize intelligent scheduling and optimization of power grid. The experimental verification shows that DIPGVRSP based on LSTM has achieved high accuracy in power grid load forecasting, and the forecasting performance is improved by about 20% compared with the traditional linear regression model. This result not only verifies the effectiveness of DIPGVRSP, but also provides strong support for the further popularization and application of digital intelligent power grid technology. © 2024 SPIE.

关键词： Photons

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Containerization of the Security Application on Electric Power IoT Terminal 4

Containerization of the Security Application on Electric Pow...

引用

4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Wang, Tengyan Shao, Zhipeng Li, Nige Xu, Zheng State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350337181

The development of Internet of Things (IoT) technology brings convenience to the development of the electric power business but also brings security risks. Moreover, owing to the massive heterogeneity of IoT terminals, traditional security protection measures are restricted in terms of deployment, adaptation, operation, maintenance, and other aspects. Docker containers take up fewer resources and are easy to transplant;therefore, they are suitable for deploying secure ap-plications in the IoT computing environment with limited resources and power. This study fo-cuses on the container virtualization technology of the security applications on electric power IoT terminals. We propose a general process for application containerization to standardize the work done in each stage of containerization. The union file system is used to build a multilayer container architecture to reduce resource usage and improve deployment speed. Thus, the secu-rity applications can be run in containers to realize their real-time deployment and real-time op-eration in the heterogeneous intelligent electric power IoT terminals to achieve the security ca-pabilities of on-demand orchestration and timely response of electric power IoT terminals. © 2023 IEEE.

关键词： Internet of things

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Secure multi-party variance estimation in unbalanced resource environments 4

Secure multi-party variance estimation in unbalanced resourc...

引用

4th international conference on Advanced Optics and Photonics Research in Engineering, AOPR 2024

作者： Hu, Cong Yao, Zhen Sun, Jiali Liu, Cuiling Zhang, Cuicui Lu, Ruixuan Zhai, Yujia State Grid Anhui Electric Power Co. LTD. Information and Communication Branch Hefei230022 China State Grid Smart Grid Research Institute Co. LTD. Nanjing210037 China

ISBN: (纸本)9781510682610

In big data statistical analysis tasks, the diversity of data types and the complexity of computing tasks are both increasing. distributed computing, which involves processing data across multiple nodes, is widely used but raises security concerns such as data leakage, tampering, and forgery. Hence, it is vital to explore privacy protection in distributed computing environments. This paper investigates secure computation mechanisms suitable for multi-party scenarios characterized by unbalanced computing resources. We integrate local differential privacy with secure multi-party computation technologies to develop a hybrid variance estimation method. The proposed hybrid method weights the estimation results of the local differential privacy algorithm to mitigate its error impact on result accuracy. Furthermore, we design experiments to assess the computing effectiveness of this hybrid method. The results demonstrate that the hybrid variance estimation method achieves a superior balance between accuracy and computational overhead compared to methods relying solely on a single privacy protection technique. © 2024 SPIE.

关键词： Differential privacy

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Secure Synchronized Spatio-Temporal Trajectory Similarity Search 22

Secure Synchronized Spatio-Temporal Trajectory Similarity Se...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Teng, Yiping Qi, Jiawei Liu, Lei Wang, Shiqing Xu, Li Fan, Chunlong Shenyang Aerosp Univ Sch Comp Large Scale Distributed Syst Lab Shenyang Liaoning Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Attracting research interests and applications in academic and industrial community due to the proliferation of mobile devices, computation and processing services on spatio-temporal trajectory data has usually been outsourced to cloud platforms to save the costs of data storage, computation and management. To prevent the privacy leakage of trajectories from the direct data outsourcing, in this paper, we study the secure similarity search problem on spatio-temporal trajectories and present a secure synchronized spatio-temporal trajectory similarity search approach. In the approach, adopting the Matching Point-point distance Similarity (MPS) measurement, we first propose a Secure Matching Point-point Distance Similarity Computation (SMPSC) Protocol to support the secure similarity calculation on encrypted trajectories. To improve the computational performance, we further propose a Secure grid Filtering (SGF) method by matching spatio-temporal grid codes to filter the dissimilar trajectories based on the distance threshold in MPS. At last, with SMPSC protocol and SGF method, we propose a Secure Synchronized Spatio-Temporal Trajectory similarity Search Processing (S-3 TS) method to retrieve similar trajectories based on MPS measurement. We theoretically analyze the computational complexity and security guarantees of the presented approach, and conduct extensive experiments on real and synthetic datasets to demonstrate its search performance.

关键词： Trajectory Secure computation protocol Synchronized Spatio-temporal similarity Privacy protection

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Implementation of Multimodal Information Fusion Methods for Intelligent Converter Stations 24

Implementation of Multimodal Information Fusion Methods for ...

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2024 2nd international conference on Internet of Things and Cloud computing Technology, IoTCCT 2024

作者： Li, Daoyu Zhou, Pei Li, Xuewu Liu, Yang Yuan, Ning Guiyang Bureau EHV Power Transmission Company China Southern Power Grid Co. Ltd. Guiyang China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798400710148

This paper proposes a multimodal information fusion device designed for use in converter station scenarios, aimed at improving the efficiency of information fusion technologies. The device performs effective partitioning and parallel processing of multimodal fusion tasks based on the spatial physical location of the demand terminal and the heterogeneous characteristics of communication and computing power across network nodes. This approach enables efficient multimodal information fusion tailored to the specific spatial location of demand terminals. By establishing a standardized set of operations for representation, translation, and alignment, the device constructs a stream computing graph to handle complex multimodal information fusion tasks. During this process, it gathers spatial information from different demand sources, while taking into account factors such as node computing power, I/O, and energy supply, ensuring rational partitioning and mapping of the computational flow. This method significantly reduces the computational intensity of multimodal data fusion and enhances the efficiency of distributed multimodal information fusion in intelligent sensing scenarios at converter stations. © 2024 Copyright held by the owner/author(s).

关键词： Information fusion

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