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检索条件"任意字段=1st IEEE International Conference on Distributed Computing in Sensor Systems"

共 3334 条记录，以下是101-110 订阅

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Mechanism for Optimizing Resource Allocation in VANETs Based on the PSO Bio-inspired Algorithm 18

Mechanism for Optimizing Resource Allocation in VANETs Based...

引用

18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Lieira, Douglas D. Quessada, Matheus S. Cristiani, Andre L. De Grande, Robson E. Meneguette, Rodolfo, I Sao Paulo State Univ Sao Jose Do Rio Preto SP Brazil Fed Inst Sao Paulo IFSP Catanduva SP Brazil Fed Univ Sao Carlos UFSCar Sao Carlos SP Brazil Brock Univ BrockU St Catharines ON Canada Univ Sao Paulo Sao Carlos SP Brazil

ISBN: (纸本)9781665495127

With the increase of vehicles in cities and the technology used by these vehicles, there is also a need to use the technology made available by intelligent transport systems in an efficient and agile way. Edge computing services assist in the agile process of exchanging and sharing information and resources between vehicles. However, the limitations of edge services bring the need to optimize resource allocation processes. Thus, in this article we propose the MARIA, a mechanism for optimizing computational resources in Vehicular Ad Hoc Networks based on the particle swarm optimization bio-inspired algorithm. The ease of adaptation to various scenarios by a bio-inspired algorithm is presented in the work. In addition, the MARIA mechanism proved to be efficient when compared with techniques frequently used in the literature and was able to increase the amount of services accepted and reduced the amount of refused services.

关键词： bio-inspire algorithm edge computing particle swarm optimization resource allocation

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CDQKL: Consensus-Based distributed Quantum Kernel Learning 1

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Ma, Wenxuan Liu, Mengxiang Deng, Ruilong State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350366778

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL. © 2024 ieee.

关键词： Quantum computers

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EDDIS: Accelerating distributed Data -Parallel DNN Training for Heterogeneous GPU Cluster

EDDIS: Accelerating Distributed Data -Parallel DNN Training ...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Ahn, Shinyoung Ahn, Hooyoung Choi, Hyeonseong Lee, Jaehyun ETRI Supercomp Syst Res Sect Daejeon South Korea MangoBoost Software Team Seoul South Korea Puzzle AI Inc Res Inst Daejeon South Korea

ISBN: (纸本)9798350364613;9798350364606

EDDIS is a novel distributed deep learning library designed to efficiently utilize heterogeneous GPU resources for training deep neural networks (DNNs), addressing scalability and conuminication challenges in distributed training environments. It offers three training modes (synchronous, asynchronous, and hybrid) and supports TensorFlow and PyTorch frameworks. EDDIS significantly accelerates DNN training in heterogeneous GPU settings, achieving up to 17.5x faster training with 16 nodes compared to a single node. Remarkably, the Hybrid training mode surpasses Horovod, achieving training speeds 2.53 times faster for the ResNet50 model.

关键词： Terms Deep Neural Network DNN Heterogeneous GPU distributed Training Data -parallel Training EDDIS SoftMemoryBox SSGD EASGD Hybrid SGD

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ICCDS 2024 - international conference on computing and Data Science

ICCDS 2024 - International Conference on Computing and Data ...

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1st ieee international conference on computing and Data Science, ICCDS 2024

ISBN: (纸本)9798350365337

The proceedings contain 102 papers. The topics discussed include: EMG-based wrong posture detection system using Raspberry Pi and cloud-based visualization;a novel trust-enabled data-gathering technique based on modified Golden Eagle optimization in wireless sensor network;earlier detection of microaneurysms in fundus image using VGG-19 with dual attention mechanism;intelligent load monitoring and control in railway wagons using IOT;a novel approach to carrier guidance system using machine learning and blockchain;the intersection of AI, ethics, and education: a bibliometric analysis;stacked regressor for crop yield prediction;a comprehensive review on underwater object detection techniques;and enhanced privacy in data aggregation with secret sharing techniques.

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A Cloud computing Approach for Minimal Exposure Routing in Resource-Constrained Wireless sensor Networks 1

A Cloud Computing Approach for Minimal Exposure Routing in R...

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1st ieee international conference on computing and Data Science, ICCDS 2024

作者： Venkateswararao, K. Aravinth, S.S. Sai, Dampanaboyina Gowtham Sai Yashaswini, Emandi Venkata Saisha, Perugu Naga Bhavana, Enuganti Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350365337

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the constraint capabilities of sensor nodes. Determining routing paths in WSNs with minimal exposure to adversaries is critical for preserving privacy and combating threats. However, the complexity of computing optimal minimal exposure paths (MEP) exceeds the limited processing capacity of embedded sensor devices. This research proposes a cloud computing-based methodology to address this challenge by leveraging the vast storage and computational resources of the cloud. A coordinated cloud control scheme is devised for global optimization of concealable routing configurations in WSNs. The methodology models the dynamic WSN topology and monitors exposure levels of links in a cloud database. Leveraging parallel processing capabilities, the cloud platform efficiently executes an exposure-aware routing algorithm to find the current MEP between sensors and propagates the route to edge nodes. This adaptive technique refines the path in real-time based on updated exposure data analytics from the field for contextual security maximization. Comprehensive experiments validate the efficiency, scalability and robustness of the proposed technique over conventional localized approaches. This research comprehensively investigates a cloud-assisted exposure-aware routing paradigm to enhance security, privacy and resilience in resource-constrained WSNs. The key focus areas of exposure routing, cloud leverage, resource constraints, and security maximization are highlighted while articulating the core research problem, method, innovations, and significance in the compact abstract. © 2024 ieee.

关键词： Digital storage

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distributed and Intelligent API Mediation Service for Enterprise-Grade Hybrid-Multicloud computing

Distributed and Intelligent API Mediation Service for Enterp...

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1st ieee international conference on Software Services Engineering (ieee SSE)

作者： Bian, Hongyi Chang, Rong N. Bhaskaran, Kumar Zhang, Wensheng Chang, Carl K. Iowa State Univ Ames IA 50011 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9798350340754

In an enterprise-grade hybrid-multicloud computing environment, capability-providing as-a-service endpoints (or aaS-endpoints) can be deployed across diverse computing platforms, e. g., public clouds and on-prem enterprise private clouds. To ensure a seamless, unified, and enterprise-compliant acquisition of the capabilities by client applications, the presence of a cross-cloud API mediation service is crucial. However, as the number and heterogeneity of aaS-endpoints increase, delivering the API mediation service at scale becomes increasingly costly. This paper presents a robust approach to API service mediation in enterprise-grade hybrid-multicloud computing environments. It tackles the challenges, offering a distributed architecture comprising dynamically composed managed microservices, microservice zones, intelligent endpoint selection, and adaptive statistical learning (aiming to exploit localities in performance history of aaS-endpoint invocations and to facilitate adding or removing active aaS-endpoints). The successful reference implementation and 24x7x365 delivery in real-world settings of the approach validate its efficacy as a practical solution for API service mediation.

关键词： API service mediation enterprise microservice zone WCM QoE endpoint selection adaptive statistical learning

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distributed Output Bounded Bilinear Tracking Control of a Parabolic Trough Solar Collector 12

Distributed Output Bounded Bilinear Tracking Control of a Pa...

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2024 12th international conference on systems and Control, ICSC 2024

作者： Mechhoud, S. Belkhatir, Z. University of 20 August 1955 Skikda Department of Electrical Engineering Skikda Algeria University of Southampton Department of Electronics and Computer Science United Kingdom

ISBN: (纸本)9798331531812

This paper deals with the problem of constrained bilinear control of a parabolic trough solar collector (PTSC) in the infinite-dimensional setting. The PTSC is modeled by a 1st-order hyperbolic PDE, and the objective is to drive the tube's outlet temperature to track a desired profile. The fluid's velocity is the manipulated variable, and that leads to a bilinear control problem. In addition, due to physical and technical considerations, control constraints are imposed. First, based on Lyapunov's stability theory, we design a distributed state-feedback controller that guarantees point-wise exponential stabilization of the tracking error. Then, since only boundary measurements are available, we develop an observer-based controller that stabilizes the tracking error asymptotically. Simulation results attest to the performance of the proposed control method. © 2024 ieee.

关键词： state feedback

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distributed Finite-Time Bipartite Containment Control for Heterogeneous Fractional-Order Multi-Agent systems 1

Distributed Finite-Time Bipartite Containment Control for He...

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1st international conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2022

作者： Gao, Zhiyun Pi, Jun Cai, Yuliang Gu, Jie Civil Aviation University of China School of Transportation Science and Engineering Tianjin300300 China Liaoning University School of Mathematics and Statistics Liaoning Shenyang110036 China

ISBN: (纸本)9781665452151

This paper solves the finite-time bipartite output containment problem for heterogeneous fractional-order multi-agent systems (HFOMASs) with structurally balanced graph. First, the distributed finite-time bipartite observer is presented to estimate the convex hull of the outputs generated by leaders or the opposite convex hull of leaders' outputs. Then, the finite-time bipartite control algorithm is presented to achieve the bipartite output containment control within a finite time. Finally, the example is presented to demonstrate the effectiveness of theoretical conclusions. © 2023 ieee.

关键词： Multi agent systems

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MDLoader: A Hybrid Model-driven Data Loader for distributed Deep Neural Networks Training

MDLoader: A Hybrid Model-driven Data Loader for Distributed ...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Bae, Jonghyun Choi, Jong Youl Pasini, Massimiliano Lupo Mehta, Kshitij Ibrahim, Khaled Z. Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Oak Ridge Natl Lab Oak Ridge TN USA

In this work, we propose MDLoader, a hybrid in-memory data loader for distributed deep neural networks. MDLoader introduces a model-driven performance estimator to automatically switch between one-sided and collective... 详细信息

ISBN: (纸本)9798350364613;9798350364606

关键词： One-sided communication Collective communication Graph Neural Network Performance estimator

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IoT-Based Heart Disease Monitoring Using Neural Networks for Detecting Arrhythmia and Assessing Hypertrophic Cardiomyopathy(HCM) Risk 8

IoT-Based Heart Disease Monitoring Using Neural Networks for...

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8th ieee international conference on distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Hegde, Anagha L. Acharya, Shreenath Samrudh, A.U. Kottari, Suhag G. St. Joseph Engineering College Department of ICBS Mangalore India

ISBN: (纸本)9798350350593

Advancements in deep learning and IoT technology have the potential to revolutionize healthcare, as evidenced by recent developments in remote health monitoring systems. Even if they are cutting-edge, current systems frequently fall short regarding cost-effectiveness, patient-centered treatment, and real-time accuracy. Conventional approaches that rely on hospital-centric models are less accessible and resource-intensive, especially in rural areas. Furthermore, a lot of IoT-based solutions have trouble with early intervention and accurate data, both of which are necessary for managing chronic disorders like heart disease. This research offers a novel IoT system for monitoring remote cardiac illnesses, such as arrhythmia and hypertrophic cardiomyopathy (HCM). The MLX90614 non-contact body temperature sensor, the AD8232 ECG sensor, and the heart rate sensor are integrated into the system along with NodeMCU for data collecting and storage. A Deep Neural Network (DNN) model for heartbeat classification is trained using the information gleaned from these sensors, which is then saved in Excel sheets for examination. Our methodology, which shows an impressive 98% accuracy rate, emphasizes using a simplified DNN model trained on Excel data to achieve accurate heartbeat predictions, in contrast to traditional CNN-based approaches. Additionally, a web application has been provided to improve accessibility and facilitate early detection of potential health issues, optimizing patient care. With continued study and development, the suggested approach promises to improve accuracy and efficiency even more, marking a substantial improvement in home healthcare technology. © 2024 ieee.

关键词： Patient treatment

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