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检索条件"任意字段=25th International Conference on Distributed Computing and Networking, ICDCN 2024"

共 137 条记录，以下是1-10 订阅

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25th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

25th International Conference on Parallel and Distributed Co...

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25th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

ISBN: (纸本)9789819642069

the proceedings contain 55 papers. the special focus in this conference is on Parallel and distributed computing: Applications and Technologies. the topics include: A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control;Securing Energy Transactions for Electric Vehicles: the Blockchain Approach and Encrypted NFTs;optimizing Task Allocation in Heterogeneous Agent Manufacturing Systems;MPG: Multi-modal Personal Health Graph for Alzheimer’s Disease Diagnosis;SMAC: A Secure Multi-authority Access Control Scheme with Attribute Unification for Fog Enabled IoT in E-Health;Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference;handling Non-stationarity with Distribution Shifts and Data Dependency in Time Series Forecasting;the Two-Stage Stochastic Facility Location Game;regularized Non-monotone γ-weakly Submodular Maximization;fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning;WaitIO-Hybrid: Communication for Coupling MPI Programs Among Heterogeneous Systems;the Material Delivery Route Prediction Method Based on Deep Reinforcement Learning;privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications;research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios;optimizing Production Component Scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs;multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios;containerized Data-Flow Processing for Scalable Real-Time Analytics on Edge Devices;fast Approximation for Scheduling Malleable Jobs on Parallel Batch Machines with Rejection;real-Time and In-Situ Temperature Profiling for Determining Detonation of White Dwarf Mergers;accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives;Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams;long-Term and Periodicity-Aware Spatio

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distributed Graph Computations via Mobile Robots 24

Distributed Graph Computations via Mobile Robots

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25th international conference on distributed computing and networking, icdcn 2024

作者： Chand, Prabhat Kumar Molla, Anisur Rahaman Indian Statistical Institute Kolkata India

ISBN: (纸本)9798400716737

the realm of distributed computing by mobile robots on graphs has witnessed significant advancements in recent years, offering solutions to various real-world problems. this research delves into two fundamental problems in this domain, focusing on the development of efficient algorithms and crash tolerance. To date, we have been able to provide (i) efficient, crash-tolerant dispersion algorithms for mobile robots [1], and (ii) the exploration of small dominating sets and maximal independent sets through mobile robots [2]. © 2024 Owner/Author.

关键词： Mobile agents

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distributed Machine Learning 24

Distributed Machine Learning

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25th international conference on distributed computing and networking, icdcn 2024

作者： Chatterjee, Bapi Iiit New Delhi Delhi India

ISBN: (纸本)9798400716737

We explore the landscape of distributed machine learning, focusing on advancements, challenges, and potential future directions in this rapidly evolving field. We delve into the motivation for distributed machine learning, its essential techniques, real-world applications, and open research questions. the theoretical discussion will give an overview of proving the convergence of popular Stochastic Gradient Descent (SGD) Algorithms to train contemporary machine learning models, including the deep learning models with the assumption of non-convexity, in a distributed setting. We will specify the convergence of data parallel SGD for various distributed systems properties, such as asynchronous and compressed communication. We will also discuss distributed machine learning techniques such as model parallelism and tensor parallelism to train large language models (LLMs). © 2024 ACM.

关键词： Stochastic systems

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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25th international conference on distributed computing and networking, icdcn 2024

ISBN: (纸本)9798400716737

the proceedings contain 63 papers. the topics discussed include: post quantum communication over the internet infrastructure;efficient and resilient edge computing: algorithms, techniques and research opportunities;self-stabilizing byzantine multivalued consensus;a further study on weak byzantine gathering of mobile agents;distributed uniform partitioning of a region using opaque ASYNC luminous mobile robots;space and move-optimal arbitrary pattern formation on a rectangular grid by robot swarms;maximal independent set via mobile agents;collision-free linear time mutual visibility for asynchronous fat robots;arbitrary pattern formation on a continuous circle by oblivious robot swarm;sublinear message bounds of authenticated implicit byzantine agreement;and LightKey: lightweight and secure key agreement protocol for effective communication in internet of vehicles.

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Efficient and Resilient Edge computing: Algorithms, Techniques and Research Opportunities 24

Efficient and Resilient Edge Computing: Algorithms, Techniqu...

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25th international conference on distributed computing and networking, icdcn 2024

作者： Palanisamy, Balaji Xu, Jinlai University of Pittsburgh PittsburghPA United States ByteDance Inc. SeattleWA United States

ISBN: (纸本)9798400716737

We are witnessing a huge proliferation of low-cost devices connected in the Internet of things. Given the large amounts of data generated by these devices at the edge of the network, there is an increasing need to process them near the network edge in order to meet the strict latency requirements of IoT applications. Edge computing is a promising paradigm to improve the quality of service for such applications by filling the latency gaps between the IoT devices and the typical cloud infrastructures. While Micro Data Centers provide computing resources that are geographically distributed, careful management of these resources near the edge of the network is vital for ensuring efficient, cost-effective and resilient operation of the system while providing low-latency access for applications executing near the network edge. this tutorial provides an introduction to edge computing and introduces the notion of Micro Data Centers and illustrates the edge computing architecture. We will discuss the algorithms, techniques and design methodologies focusing on efficient and resilient resource allocation for latency-sensitive edge computing applications. Finally, we will go through open research problems in this area and discuss potential directions of future work. © 2024 Owner/Author.

关键词： Edge computing

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Intelligent Resource Orchestration for Task Distribution in Hybrid Vehicular Networks 24

Intelligent Resource Orchestration for Task Distribution in ...

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25th international conference on distributed computing and networking, icdcn 2024

作者： Mittal, Shilpi Bali, Rasmeet Singh University Institute of Computing Chandigarh University PunjabGharuan Mohali India Computer Science & Engineering Department Chandigarh University PunjabGharuan Mohali India

ISBN: (纸本)9798400716737

the extensive growth of vehicular applications required highly computational resources to execute the application on time, but due to having limited capacity of resources such as vehicles themselves, it causes latency and more computation load. thereby, to reduce the overhead on fixed roadside servers in hybrid vehicular networks, we propose an intelligent resource orchestration scheme for task distribution among vehicles at traffic light junctions and parking areas in hybrid vehicular networks. An artificial intelligence based resource orchestration scheme is proposed for estimating edge members to execute task. Further, a task distribution model is proposed at traffic light junctions. Simulation is done using NS3 and the results shows better performance in term of lesser computational time and lower communication load with higher throughput and minimal latency in the proposed scheme. the scheme exhibits that it is efficient with the comparative benchmarks. © 2024 ACM.

关键词： Autonomous vehicles

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Workload Prediction for Edge computing 24

Workload Prediction for Edge Computing

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25th international conference on distributed computing and networking, icdcn 2024

作者： Shah, Kavish Gandhi, Ayush Gandhi, Kalgi Bhise, Minal Distributed Database Group Gujarat Gandhinagar India

ISBN: (纸本)9798400716737

In large-scale edge systems, along with the number of edge devices, the amount of data that needs to be processed has been increasing. Edge systems have limited processing power, memory, and network bandwidth. For such systems, handling resource utilization and data management during query processing is tricky. Moreover, joins being the costliest operations, when accelerated, can improve query processing substantially. the state-of-the-art technique for accelerating hash join, Column Imprint-Hash Join, is a data-aware approach which uses equi-height binning. Proposed Predicted Workload-Aware Column Imprint-Hash Join enhances the query performance and addresses real-time query processing by predicting the upcoming workload in advance. the algorithm is demonstrated on the TPC-H benchmark dataset. It shows an improvement of about 10% for the Percentage of Cachelines Read over CI-HJ, making the algorithm energy-efficient. An overall gain in Query Execution Time of 11% is observed. Although, Algorithm Execution Time shows an overhead of 6% as compared to the state-of-the-art which is due to an additional prediction phase. the proposed technique showcases the effectiveness of query prediction in optimizing edge system performance. © 2024 ACM.

关键词： Forecasting

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SAHA: An SDN Enabled Fog Architecture for Healthcare Applications 24

SAHA: An SDN Enabled Fog Architecture for Healthcare Applica...

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25th international conference on distributed computing and networking, icdcn 2024

作者： Sarkar, Joy Lal Peng, Sheng-Lung Deb, Subhrajyoti Muthaiah, U. Mondal, Ayan Gopal, Gaddam Venu Techno College of Engineering Agartala India National Taipei University of Business Taiwan Icfai University Tripura India Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Iit Indore India Koneru Lakshmaiah Education Foundation India

ISBN: (纸本)9798400716737

In this paper, a software-defined Fog architecture is suggested to initiate an immediate course of treatment for a patient. Sensors on a patient outfitted with various sensors can read physiological data. this work uses two types of queries such as strict and flexible queries. Strict inquiries are used when an emergency is recognised due to a patient's criticality. Non-emergency queries are referred to as flexible inquiry. the strict queries are executed at a nearby edge node, and the data is sent to the fog node via a switch or SDN controller. Instead, a patient can move for treatment depending on the proximity of medical professionals, the quality of the facilities, and the travel time. the task of determining availability falls to a Local Medical Team (LMT). It is also the responsibility of a Global Medical Team (GMT) to keep track of the patient's health and provide the LMT with any information that is required. We can observe from the outcomes that the suggested algorithm outperforms the baseline algorithms. © 2024 ACM.

关键词： Travel time

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Application and Energy-Aware Data Aggregation using Vector Synchronization in distributed Battery-less IoT Networks 24

Application and Energy-Aware Data Aggregation using Vector S...

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25th international conference on distributed computing and networking, icdcn 2024

作者： Singhal, Chetna Barick, Subhrajit Sonkar, Rishabh Kharagpur India Iit Kharagpur India

ISBN: (纸本)9798400716737

the battery-less Internet of things (IoT) devices are a key element in the sustainable green initiative for the next-generation wireless networks. these battery-free devices use the ambient energy, harvested from the environment. the energy harvesting environment is dynamic and causes intermittent task execution. the harvested energy is stored in small capacitors and it is challenging to assure the application task execution. the main goal is to provide a mechanism to aggregate the sensor data and provide a sustainable application support in the distributed battery-less IoT network. We model the distributed IoT network system consisting of many battery-free IoT sensor hardware modules and heterogeneous IoT applications that are being supported in the device-edge-cloud continuum. the applications require sensor data from a distributed set of battery-less hardware modules and there is provision of joint control over the module actuators. We propose an application-aware task and energy manager (ATEM) for the IoT devices and a vector-synchronization based data aggregator (VSDA). the ATEM is supported by device-level federated energy harvesting and system-level energy-aware heterogeneous application management. In our proposed framework the data aggregator forecasts the available power from the ambient energy harvester using long-short-term-memory (LSTM) model and sets the device profile as well as the application task rates accordingly. Our proposed scheme meets the heterogeneous application requirements with negligible overhead;reduces the data loss and packet delay;increases the hardware component availability;and makes the components available sooner as compared to the state-of-the-art. © 2024 ACM.

关键词： Internet of things

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Performance Analysis of Tweet Summarization Techniques Considering Crisis Dynamics 24

Performance Analysis of Tweet Summarization Techniques Consi...

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25th international conference on distributed computing and networking, icdcn 2024

作者： Mahindrakar, Shrikant Manikarao Mondal, Tamal Dhakne, Amol Arosh, Shirshendu Bhattacharya, Indrajit D.Y.Patil International University Pune Maharashtra Pune India Pune India DR.D.Y.Patil Institute of Engineering Management and Research Pune India Kalyani Government Engineering College Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India

ISBN: (纸本)9798400716737

Micro-blog summary techniques are devised in recent past through language modeling, content specific features, and classical/deep learning approaches. Recent studies substantiated the analysis of such techniques in view of semantic relevance (Quality) and time concerning constant summary length (250 words most cases) irrespective of varying document size (# of tweets). However, the dynamics of trending topic streaming in twitter including crisis events should result real time selection of summary length and appropriate summary technique for summary generation. Considering this scenario, the current study produces an analytical survey over the performance of popular micro-blog summary techniques for two distinct crisis related tweet set i.e. Flood and Earthquake. the parameters i.e. Summary Quality, Time and Size are analyzed for each technique by considering distinct tweet arrival patterns which further decides the document length. Finally, a critical observation is formulated on such analysis that further would assist the choice of suitable summary length & summary approach out of n approaches for summary generation in crisis timeline. © 2024 ACM.

关键词： Earthquakes

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