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检索条件"任意字段=6th IEEE International Conference on Distributed Computing in Sensor Systems"

共 6280 条记录，以下是41-50 订阅

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Proceedings - 2020 ieee 22nd international conference on High Performance computing and Communications, ieee 18th international conference on Smart City and ieee 6th international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

Proceedings - 2020 IEEE 22nd International Conference on Hig...

引用

22nd ieee international conference on High Performance computing and Communications, 18th ieee international conference on Smart City and 6th ieee international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

ISBN: (纸本)9781728176499

the proceedings contain 183 papers. the topics discussed include: design of direct read from sparse segments in MPI-IO;descriptive and predictive analysis of aggregating functions in serverless clouds: the case of video streaming;a proactive uncertainty driven model for data synopses management in pervasive applications;on-line traffic scheduling optimization in ieee 802.1Qch based time-sensitive networks;multi-layer and heterogeneous resource management in SDN-based space-terrestrial integrated networks;structure preserved graph reordering for fast graph processing without the pain;an efficient approach to vectorize the hybrid breadth-first search;a novel developer portrait model based on Bert-capsule network;job placement strategy with opportunistic resource sharing for distributed deep learning clusters;and batched pattern-aware cache management strategy for astronomical time series sub-images retrieval.

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A Systematic Approach for Performance Efficiency of distributed Networks using Machine Learning Techniques and Network Simulator 2 6

A Systematic Approach for Performance Efficiency of Distribu...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Parveen Akhther, A. Veera Subrahmanyam, N. Charan Sai, A. Prasanth, V.S. Dept. of CSE Sathyabama Institute of Science & Technology Chennai India Dept. of ECE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (纸本)9798331522667

In the rapidly evolving domain of distributed networks, including cloud computing and edge systems, achieving optimal performance efficiency is critical. Existing methods often fail to effectively handle dynamic policies, resource allocation, and scalability. this project presents a robust framework leveraging machine learning (ML) techniques integrated with NS2 simulation to address these challenges. Data collection from network nodes is the first step in the process. Preprocessing is then done to manage missing values, eliminate noise, and normalize the data for model training. Supervised learning methods like regression and classification optimize traffic prediction and routing protocols, while unsupervised techniques, including clustering, identify resource patterns for better allocation. Reinforcement learning dynamically adjusts policies to improve fault tolerance and resource management. NS2 simulation validates the model’s effectiveness across various network scenarios.A remarkable accuracy of 98.77% and a minimal error rate of 1.22% are attained by the claimed framework. © 2025 ieee.

关键词： Self-supervised learning

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ONU: A Reinforcement Load Balancing Approach for Resource-Aware Task Allocation in distributed systems 6

ONU: A Reinforcement Load Balancing Approach for Resource-Aw...

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6th ieee international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Shahakar, Minal Mahajan, S.A. Patil, Lalit Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University India IOM Department of Information Technology Savitribai Phule Pune University India Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Department of Information Technology India

ISBN: (纸本)9798350306613

distributed systems have revolutionized modern computing by enabling the parallel execution of large-scale tasks across interconnected nodes. However, the heterogeneity of resources in distributed systems often leads to load imbalances and underutilization of certain nodes, resulting in suboptimal performance. To address these challenges, this research paper presents a novel approach for resource-aware task distribution based on reinforcement load balancing in distributed systems with over utilized, normally utilized, and underutilized nodes. the proposed approach leverages reinforcement learning algorithms to dynamically distribute tasks among nodes based on their resource utilization levels. ONU module is designed to cater to the specific needs of each node category: the ONU Algorithm focuses on mitigating overburdened nodes, moderate resource usage nodes, and the underutilized nodes operating below capacity. the results demonstrate significant improvements in load balancing and resource utilization compared to traditional load balancing techniques. © 2024 ieee.

关键词： distributed systems Heterogeneous Resources Load Distribution Reinforcement Load Balancing Resource Utilization Task Allocation

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Research on Signal Processing and sensor Fusion Technology in Electrical Engineering Automation 6

Research on Signal Processing and Sensor Fusion Technology i...

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6th ieee international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Chen, Rong Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350389418

this study introduces an electrical measurement system grounded in multi-sensor fusion. through the incorporation of information fusion techniques and parallel computing strategies, the system achieves reconstruction and optimization. the system is designed with a dynamic clustering algorithm that effectively merges data from various sensors, thereby enhancing both the precision and real-time performance of the measurements. To validate the system's effectiveness, this paper conducts simulations and carries out detailed data analysis of the simulation outcomes. the findings demonstrate that the proposed sensor fusion approach substantially increases the measurement accuracy of the electrical measurement system while maintaining robust stability in challenging environments. Furthermore, the system's parallel computing capabilities notably boost processing efficiency, reducing the time required for data handling. these outcomes indicate that the signal processing and sensor fusion technologies explored in this research hold significant potential for applications in electrical engineering automation, offering a solid theoretical foundation and technical support for future engineering implementations. © 2024 ieee.

关键词： Information fusion

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distributed photovoltaic reliability research 6

Distributed photovoltaic reliability research

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6th international conference on Energy, Power and Grid, ICEPG 2024

作者： Li, Jiehui Lu, Junlin Guo, Lin Zou, Jianqiang Li, Zhifeng Sun, Lijuan Zhou, Jiating Wang, Zhidong Guangzhou City University of Technology School of Electrical Engineering Guangzhou China South China University of Technology College of Electric Power Guangzhou China Cvc Testing Technology Co.Ltd. Guangzhou China

ISBN: (纸本)9798350377798

With the increasing emphasis on renewable energy, the importance of distributed photovoltaic systems in the energy sector is increasing. In this paper, the fault tree analysis (FTA) method is used to study the reliability of distributed photovoltaic systems. the failure modes and interrelationships of components such as photovoltaic modules, combiner boxes, inverters, energy storage devices and monitoring systems are discussed, and the weak links of the system are revealed. the results show that the string PV system has more advantages in matching the optimal working point of the PV array. this paper provides a theoretical basis for improving the stability and prolonging the service life of distributed photovoltaic systems. © 2024 ieee.

关键词： distributed Fault tree analysis Photovoltaic systems Reliability analysis

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

2024 6th International Conference on Next Generation Data-Dr...

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

ISBN: (纸本)9798350388374

the proceedings contain 93 papers. the topics discussed include: a data quality quantitative evaluation of marine policy based on PMC model;text guided road marking point cloud completion;EFSNet: a threat detection method based on system event feature sequence;multi-test knowledge tracing prediction model based on learning cycle;MAGIC: memory advanced general index compression for learned indexes;microservices crowdsourcing platform for industrial control systems;IoT-based smart farming system with security powered by solar energy;MMLT: a novel multimodal lightweight convolutional transformer for fault diagnosis with imbalanced data in IoT;GPMBC-FZ: a query optimization method in distributed database;and energy and effective computing contract toward heterogeneity in terminal-edge-cloud computing network.

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Analysis of the distributed Network Integrated Online Sports Detection System

Analysis of the Distributed Network Integrated Online Sports...

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6th international conference on Intelligent computing and Control systems, ICICCS 2022

作者： Wang, Yongjun Huazhong University of Science and Technology Sports Institute Wuhan 430000 China

ISBN: (纸本)9781665410359

this paper uses a variety of research methods to investigate the implementation effect of online physical education teaching in a primary school during the new crown pneumonia epidemic. the results found that the online teaching strategy is insufficient in the specific implementation process, focusing on the local decision algorithm of wireless sensor network distributed sports online detection and the data fusion algorithm of the system fusion center, and the classical Bayesian and Neyman- Pearson's theory is analyzed, aiming to build a variety of communication and docking platforms, improve the professional quality of physical education teachers, cultivate students' habit of self-exercise, realize the orderly development of the daily online teaching system of physical education classes, and provide the basis for the future development of physical education teaching during the epidemic period. Provide reference and reference. © 2022 ieee.

关键词： Wireless sensor networks

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025 - Proceedings

6th International Conference on Mobile Computing and Sustain...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

ISBN: (纸本)9798331522667

the proceedings contain 289 papers. the topics discussed include: a comparative analysis of machine learning techniques for the detection and classification of lung cancer;empirical examination of mobile ad hoc routing protocol on wireless sensor network;optimizing performance and cost efficiency in AI-driven cloud infrastructures: a multi-objective approach;healthcare data protection and biometric access on cloud platforms;network anomaly detection using similarity-aware ensemble learning;a review of advancements in NLP and speech recognition for enhanced operating systems;comparative analysis of edge-based and cloud-based intrusion detection systems: a systematic literature review;utilization of virtual reality technology for enhancement of e-commerce experience;and breast cancer detection using ultrasound images based and convolutional neural network.

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Collaborative User Mobility Prediction in distributed Edge computing Framework 21

Collaborative User Mobility Prediction in Distributed Edge C...

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21st ieee international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Ali, Sardar Jaffar Raza, Syed M. Choo, Hyunseung Sungkyunkwan Univ Dept AI Syst Engn Suwon South Korea Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9798350363999;9798350364002

User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cloud to facilitate DL model learning causes extensive overhead and privacy concerns. distributed edge cloud-based federated learning solves these issues, but it faces challenges in handling out-of-distribution data from decentralized edges at the network periphery, and the model biases due to data heterogeneity. this paper addresses these limitations by proposing a fully distributed Collaborative User Mobility Prediction (CUMP) framework that mitigates the out-of-distribution data issue through collaboration among initial layers of DL models in edges that are selected using inter-edge mobility rates. the remaining part of each model only trains on local data, preserving biases towards their respective edges. this enhances the generalization, robustness, and predictive performance of the DL models. Results show that CUMP outperforms conventional global learning and state-of-the-art distributed personalized federated learning and cyclic incremental institutional learning by 63%, 12%, and 10% in predicting the next Point of Attachment (PoA) of a user and by 70%, 22%, and 28% in predicting user dwell time in current PoA, respectively. thus, CUMP improves prediction performance while reducing network and storage overheads while preserving privacy.

关键词： distributed edge architecture multi-access edge computing collaborative learning proactive mobility B5G/6G

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TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks 44

TrimCaching: Parameter-sharing AI Model Caching in Wireless ...

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44th ieee international conference on distributed computing systems (ICDCS)

作者： Qu, Guanqiao Lin, Zheng Liu, Fangming Chen, Xianhao Huang, Kaibin Univ Hong Kong Dept Elect & Elect Engn Pok Fu Lam Hong Kong Peoples R China Peng Cheng Lab Shenzhen Peoples R China Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9798350386066;9798350386059

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement scheme, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To overcome this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with (1 - epsilon)/2-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

关键词： Edge AI model caching edge computing edge intelligence 6G model downloading

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