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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149975 条记录，以下是4521-4530 订阅

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Equitable Extended Reality via Cooperative NOMA in Decentralized Mobile Edge computing systems 58

Equitable Extended Reality via Cooperative NOMA in Decentral...

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58th Asilomar conference on Signals, systems and Computers, ACSSC 2024

作者： Reifert, Robert-Jeron Dahrouj, Hayssam Sezgin, Aydin Institute of Digital Communication Systems Ruhr University Bochum Germany University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350354058

Extended reality (XR) applications pose strict technical demands on future communication systems in terms of rapid connectivity and resource-intensive task computations. To this end, this paper addresses the benefits of an optimized mobile edge computing system using battery-limited edge computers (ECs) so as to meet the intricate requirements of future XR applications. The paper classifies well-connected and poorly-connected XR devices as strong and weak devices, respectively. Such platform is then provisioned through the use of a cooperative non-orthogonal multiple access (Co-NOMA) optimized scheme. With the aid of device cooperation, the paper illustrates how Co-NOMA improves the weak devices' performance by flexibly selecting either the direct or relay transmission link. More specifically, to achieve equitable XR services, the paper maximizes the minimum rate among all XR devices by jointly managing computation, communication, and link selection resources subject to power consumption, computational capacity, and delay constraints. The proposed solution then utilizes discrete relaxations, successive-convex approximation, and fractional programming techniques, and is shown to be amenable for distributed implementation across the multiple ECs. Simulations illustrate the proposed approach efficacy, especially in terms of minimum rate and delay performance in decentralized XR systems, as compared to several state-of-the-art methods. © 2024 ieee.

关键词： Mobile edge computing

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Deep Reinforcement Learning for Distribution Power System Cyber-Resilience via distributed Energy Resource Control 14

Deep Reinforcement Learning for Distribution Power System Cy...

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14th ieee international conference on Communications, Control, and computing Technologies for Smart Grids, SmartGridComm 2023

作者： Corrado, Nicholas E. Livesay, Michael Bailey, Tyson Johnson, Jay Levin, Drew University of Wisconsin Department of Computer Sciences Madison United States Sandia National Laboratories United States

ISBN: (纸本)9781665455541

Interoperable, internet-connected distributed energy resource (DER) devices in power systems create a new attack vector for malicious cyber-actors. Modern control systems primarily focus on transmission and sub-transmission operations and rarely incorporate DER or other distribution-connected equipment. While recent advances have expanded grid operator visibility and control functionality to the distribution level, these control systems struggle to scale with thousands of networked devices. Thus, to defend against potential attacks on DER devices, we must develop new real-time control algorithms to ensure grid stability. In this work, we present a new approach to power distribution control based on deep reinforcement learning (RL) algorithms which can learn optimal control policies through experience. We evaluate four RL algorithms in novel voltage stabilization tasks based on the ieee 13-bus and EPRI Ckt5 power distribution models. We demonstrate that RL can successfully perform voltage regulation and outperform a greedy algorithm. Our results coupled with the established capability of RL to scale in high-dimensional settings opens a path towards real-time cyber defense of power systems with RL agents. © 2023 ieee.

关键词： Reinforcement learning

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TWAFR-GRU: An Integrated Model for Real-time Charging Station Occupancy Prediction 19

TWAFR-GRU: An Integrated Model for Real-time Charging Statio...

引用

2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Qiyang Liu, Sheng Qu, Haohao Zhu, Rui You, Linlin Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Systems Science Department Singapore

ISBN: (纸本)9798350346558

The fast-growing number of electric vehicles has resulted in a variety of charging-related issues, such as the shortage of public charging piles, the inconvenience of finding available charging piles, the additional traffic congestion caused by unnecessary cruising to search for available charging piles, etc. Since only constructing new charging piles may not significantly improve the charging efficiency, methods of predicting charging station occupancy are widely discussed as an effective means to address these issues. However, several challenges are encountered in training an efficient and effective model by utilizing distributed data, improving convergence speed, and enhancing model generalization ability. To address these issues, this paper proposes a novel mechanism, named TWAFR-GRU, which integrates Temporally Weighted Asynchronous Federated Learning (TWAFL) with Reptile and Gated Recurrent Unit (GRU). As shown by the holistic evaluation based on the charging station occupancy dataset, compared with other state-of-the-art baselines, TWAFR-GRU can 1) decrease MAE, RMSE and RAE by 19%, 15% and 17% separately and improve R2, by 67%;2) cut rounds for the model to converge by 75%;3) save training time by 44%;and 4) reduce 17% in forecasting error after the personalization of the initial model to serve a specific charging station. © 2022 ieee.

关键词： Piles

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Development Anonymous Authentication Maria et al.'s Scheme of VANETs Using Blockchain and Fog computing with QR Code Technique 10

Development Anonymous Authentication Maria et al.'s Scheme o...

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10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Alzaidi, Zahraa Sh Yassin, Ali A. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Univ Basrah Coll Educ Pure Sci Dept Comp Sci Basrah 61004 Iraq Jaram Oginga Odinga Univ Sci & Technol Dept Comp Sci & Software Engn Bando 40601 Kenya SIMATS Dept Appl Elect Saveetha Sch Engn Chennai 600124 Tamil Nadu India

ISBN: (纸本)9798350373981;9798350373974

The evolution of intelligent transport systems (ITS) has significantly enhanced driving experiences in vehicular ad-hoc networks (VANET), crucial for improving road safety and traffic efficiency. Despite these advancements, challenges persist. Trust management solutions often assume user legitimacy without verification. Moreover, the need for reauthentication as vehicles move between RSUs adds computational complexity. VANET is relatively vulnerable to security threats from multiple parties given its highly dynamic topology and its wireless and heterogeneous communication mode. We have expanded upon Maria et al.'s schema by specifically targeting and resolving significant shortcomings in their research. More precisely, we improve the features related to confirming the identity of users, preventing vehicles from being used without authorization, and continuously monitoring their current location in order to strengthen the security of vehicles and increase user involvement. We suggest implementing a schema that utilizes QR code verification to authenticate vehicle users, specifically targeting these difficulties. In VANETs, blockchain has been adopted with the authentication process for fog computing. The result is that there is no need for trusted authority (TA) while achieving privacy within the blockchain network and ensuring of the vehicle user authentication and the integrity of messages. Several key benefits have been achieved, such as blocking malicious entities and the potential damage they cause, enabling the revocation of malicious vehicles by conditional anonymization, as well as simplifying conflict resolution. Likewise, the proposed work was characterized by achieving an effective balance between effectiveness and complexity, protecting privacy, conditional tracking, ensuring integrity, and anonymous authentication.

关键词： Vehicular ad hoc networks

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Crop Disease Detection with Autonomous Robotic systems 1

Crop Disease Detection with Autonomous Robotic Systems

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Rithani, M. Shabarirajan, K.J. Shivanesh, B. Syamdev, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India New Horizon College of Engineering Department of AI & ML Bangalore India

ISBN: (纸本)9798331518943

Crop disease detection is an important maintenance aspect for the upkeep of agricultural productivity. This paper presents a novel autonomous robot system designed to navigate and monitor crop health. The system was developed within a ROS framework using a camera to capture real-time video feeds of crops and a YOLO v8 model for detecting the presence of three specific plant diseases. Such a configuration resembles a farm in the presence of crops and trees and hence can be termed as a dirty farm. Automated navigation will cover all the area. The detection model processes each frame to detect plant disease. Such an approach has the potential to make improvements in early detection of diseases, thus leading to proper management and informed decisions about precision agriculture. © 2024 ieee.

关键词： Microrobots

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A Comprehensive Literature Review on Gesture-Based Adaptive systems for Early Detection of ADHD in Children: Technical Trends and Research Directions

A Comprehensive Literature Review on Gesture-Based Adaptive ...

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2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Kulkarni, Abhilasha Devdatta Prasad, Jayashree Rajesh MIT Art Design and Technology University CSE School of Computing Pune India

ISBN: (纸本)9798350362879

Attention Deficit Hyperactivity Disorder (ADHD) profoundly impacts children, often extending into adulthood. Timely identification and intervention are critical. This paper explores ADHD assessment by integrating child assessment tools with advanced gesture-based systems. The Centers for Disease Control and Prevention (CDC) compiled a comprehensive manual of diverse assessment tools, meticulously designed to evaluate cognitive, motor, language, and social development. Gesture-based systems, powered by computer vision, depth sensing, and motion tracking, hold promise in refining ADHD assessment. Integration with traditional tools enables real-Time monitoring, capturing motor movements, analyzing social interactions, and assessing emotional responses. This integration enhances ADHD detection precision. This paper explores the fusion of child assessment tools and gesture-based systems, highlighting their potential to revolutionize ADHD assessment and early intervention. Research employs digital resources, including Web of Science, PubMed, Google Scholar, JSTOR, Scopus, and ieee Xplore. The goal is to illuminate gesture recognition technology's transformative role in ADHD assessment, stressing the need for comprehensive validation *** summary, this paper envisions a future where integrated assessment tools and gesture recognition technology enable more accurate, timely interventions for children with neurodevelopmental challenges. © 2024 ieee.

关键词： Diseases

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Fuzzy c-Lines for Vertically distributed Database with Missing Values 12

Fuzzy c-Lines for Vertically Distributed Database with Missi...

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Joint 12th international conference on Soft computing and Intelligent systems / 23rd international Symposium on Advanced Intelligent systems (SCIS and ISIS)

作者： Kunisawa, Kohei Honda, Katsuhiro Ubukata, Seiki Notsu, Akira Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan Osaka Metropolitan Univ Grad Sch Informat Sakai Osaka 5998531 Japan Osaka Metropolitan Univ Grad Sch Sustainable Syst Sci Sakai Osaka 5998531 Japan

ISBN: (纸本)9781665499248

Privacy preserving data clustering is a useful method for extracting intrinsic cluster structures from distributed databases keeping personal privacy. In a previous research, a model of performing Fuzzy c-Lines clustering was proposed, where a privacy preserving scheme of k-means-type model was adopted with cryptographic calculation. This paper further improves the model for handling incomplete data ignoring the influences of missing values. The element-wise clustering criterion enables to derive local principal component vectors in each data sources by considering minimization of low-rank approximation of observed elements only. Then, fuzzy memberships of each object are calculated in a collaborative manner among organizations, where partial distances between objects and prototypes are derived with cryptographic framework so that intra-organization information is kept secret. The characteristic features of the proposed method are demonstrated through numerical experiments.

关键词： Fuzzy clustering Linear clustering distributed database

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2023 ieee international conference on Dependable, Autonomic and Secure computing, international conference on Pervasive Intelligence and computing, international conference on Cloud and Big Data computing, international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

2023 IEEE International Conference on Dependable, Autonomic ...

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2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

ISBN: (纸本)9798350304602

The proceedings contain 174 papers. The topics discussed include: analyzing the effects of driving experience on backing maneuvers based on data collected by eye-tracking devices;recommendation algorithm based on social information and global average matrix decomposition;data analytics for dependable transportation systems in a smart city;stabilizing technology for online user dynamics by controlling one-way weights of links;a novel feature extraction technique for ECG arrhythmia classification using ml;fault-tolerant data aggregation with error-checking for smart grids;characterization of execution time variability in FPGA-based ai-accelerators;an improved deep learning-based approach to urban weather radar echo extrapolation;balanced hashgraph based on dynamic MCMC: efficient, practical and compatible;a blockchain of blockchains structure based on asset trading scheme;and research on malicious traffic detection based on word embedding algorithms and neural networks.

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TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency 23

TriplePlay: Enhancing Federated Learning with CLIP for Non-I...

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23rd ieee international conference on Machine Learning and Applications, ICMLA 2024

作者： Imteaj, Ahmed Hossain, Md Zarif Zaman, Saika Shahid, Abdur R. School of Computing Southern Illinois University CarbondaleIL United States United States

ISBN: (纸本)9798350374889

The recent advancement of pretrained models shows great potential as well as challenges for privacy-preserving distributed machine learning technique called Federated Learning (FL). With the growing demands of foundation models, it is now an urgent need to explore the potential of such foundation models in a distributed setting. In this paper, In this paper, we delve into the complexities of leveraging foundation models, like CLIP into FL frameworks to preserve data privacy, and efficiently training distributed network clients across heterogeneous data landscapes. We specifically aim to address the issues related to non-IID data distributions, skewed class representation of FL clients' local dataset, communication overhead and high resource consumption due to large, complex model training in an FL setting. To address these, we propose TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model's performance and adaptability across heterogeneous data distributions among the clients. Besides, we address the long-tail distribution problem in an FL environment to maintain fairness and optimize the computational resource demands of the FL clients through quantization and low-rank adaptation techniques. A comprehensive simulations results with two distinct datasets and different FL settings demonstrate that TriplePlay efficiently reduces GPU usage and accelerates the convergence time that ultimately reduces the communication cost. © 2024 ieee.

关键词： Federated learning

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Machine Learning (ML)-Assisted Beam Management in Millimeter (mm)Wave distributed Multiple Input Multiple Output (D-MIMO) systems 16

Machine Learning (ML)-Assisted Beam Management in Millimeter...

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16th international conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Karthik, R.M. Hegde, Dhiraj Nagaraja Sarajlic, Muris Sarkar, Abhishek Ericsson Research Chennai India Ericsson Research Lund Sweden

ISBN: (纸本)9798350383119

Beam management (BM) protocols are critical for establishing and maintaining connectivity between network radio nodes and User Equipments (UEs). In distributed Multiple Input Multiple Output systems (D-MIMO), a number of access points (APs), coordinated by a central processing unit (CPU), serves a number of UEs. At mmWave frequencies, the problem of finding the best AP and beam to serve the UEs is challenging due to a large number of beams that need to be sounded with Downlink (DL) reference signals. The objective of this paper is to investigate whether the best AP/beam can be reliably inferred from sounding only a small subset of beams and leveraging AI/ML for inference of best beam/AP. We use Random Forest (RF), MissForest (MF) and conditional Generative Adversarial Networks (c-GAN) for demonstrating the performance benefits of inference. © 2024 ieee.

关键词： 6G beam-manangement L1-RSRP missing-values

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