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检索条件"任意字段=International Symposium on Computer Networks and Distributed Systems"

共 64262 条记录，以下是4951-4960 订阅

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Improving Network Efficiency in Clustered Tree Topology through PSO Optimization in IEEE 802.15.4-DSME based IoT networks

Improving Network Efficiency in Clustered Tree Topology thro...

引用

international symposium on Advanced networks and Telecommunication systems (ANTS)

作者： Sonali Anand Nikumani Choudhury Tamoghna Ojha Anakhi Hazarika Jay Dave Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Mathematics & Computing Indian School of Mines Indian Institute of Technology Dhanbad India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPANs). Deterministic and Synchronous Multi-channel Extension (DSME) is one of the key Medium Access Control (MAC) modes of the IEEE 802.15.4 standard that was introduced to address the need for higher reliability, lower latency, and better scalability in network communication in Internet of Things (IoT) applications. However, in dynamic network conditions (topology and channel traffic), the network suffers from several limitations, such as low packet delivery ratio, high latency and packet collisions, and finally, higher power consumption. This is because of sub-optimal parameter settings. The standard does not define any mechanism to adapt the parameters based on network conditions. In this paper, we propose a mechanism to adapt/fine-tune the network parameters DSME along with the optimization of QoS using an algorithm based on Particle Swarm Optimization(PSO) for dense and dynamic DSME-based IoT networks. Through simulations, the proposed mechanisms’ performance is analyzed in terms of energy efficiency, transmission overhead, throughput, and latency and is shown to outperform the existing DSME MAC scheme.

关键词： IEEE 802.15 Standard Network topology Heuristic algorithms Wireless personal area networks Throughput Topology Telecommunications Internet of Things Standards Optimization

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An Efficient Motor Imagery BCI Classification Architecture Using Non-linear Network Tuning suitable for Cloud-Edge Environments

An Efficient Motor Imagery BCI Classification Architecture U...

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international symposium on Telecommunications, IST

作者： Abolfazl Danayi Hamid Soltanian-Zadeh Electrical Engineering Department University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

This paper proposes a novel deep learning architecture for EEG-based Brain-computer Interface (BCI) systems, optimized for real-world deployment. BCI is fundamentally a communication problem, yet many works focus on lab-based implementations. Our approach addresses practical implementation considerations crucial for use beyond controlled environments. By incorporating a non-linear Network Tuning Block (NTB) and applying a two-phase training process, our model enhances classification accuracy while minimizing computational demands. Tested on the BCI Competition IV-2b dataset, our architecture achieves a $\mathbf{9 3. 2 \%}$ accuracy, surpassing state-of-the-art methods with fewer parameters, underscoring its potential for practical, real-world applications.

关键词： Training Accuracy Computational modeling Deep architecture computer architecture Brain modeling Brain-computer interfaces Telecommunications Tuning Biological neural networks

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Intelligent elastic resource allocation strategy based on supervised learning

Intelligent elastic resource allocation strategy based on su...

引用

computer and Information Processing Technology (ISCIPT), international symposium on

作者： Lina Li Chuncheng Hu Shuxin Wang Junan Lv Yiming Xu Nianfeng Li College of Computer Science and Technology Changchun University Changchun China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

In streaming computing systems, flexible resource allocation for time-varying data is the key to ensure application and system performance. Traditional resource allocation methods and existing intelligent methods have the problems of limited practicability and high training costs. In order to minimize application delay, this paper proposes an intelligent elastic resource allocation strategy based on supervised learning. The strategy uses the deep neural network as the strategy model and conducts model training through supervised learning. After obtaining the basic system status and application load, it automatically performs near-optimal application resource allocation. We conducted policy performance evaluation on three real datasets. The experimental results show that compared with reinforcement learning strategy, our strategy has better performance in terms of delay violation rate and throughput, and is close to the performance of the optimal strategy.

关键词： Training Computational modeling Supervised learning Reinforcement learning Artificial neural networks Throughput Data processing

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FedCD: A Hybrid Centralized-Decentralized Architecture for Efficient Federated Learning

FedCD: A Hybrid Centralized-Decentralized Architecture for E...

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international Conference on Parallel and distributed systems (ICPADS)

作者： Pengcheng Qu Jianchun Liu Zhiyuan Wang Qianpiao Ma Jinyang Huang School of Computer Science and Technology University of Science and Technology of China Hefei China Purple Mountain Laboratories Nanjing China School of Computer and Information Hefei University of Technology Hefei China

With billions of IoT devices producing vast data globally, privacy and efficiency challenges arise in AI applications. Federated learning (FL) has been widely adopted to train deep neural networks (DNNs) without privacy leakage. Existing centralized and decentralized FL architectures have limitations, including memory burden, huge bandwidth pressure and non-IID data issues. This paper introduces a novel framework, named FedCD, merging the benefits of both centralized and decentralized FL architectures. FedCD strategically distributes the model based on layer sizes and consensus distances (measuring the deviation between the local models and the global average models), effectively relieving network bandwidth pressures and accelerating training speed even under the non-IID setting. This method significantly mitigates resource constraints and improves model accuracy, offering a promising solution to the challenges in distributed machine learning. Extensive experiment results show the high effectiveness of FedCD. The total completion time of FedCD is reduced by 16.3%-53% and the average accuracy improvement is 1.85% compared to the existing FL systems.

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Enhancing Intrusion Detection systems with Digital Twin Technology for Cyber-Physical Security

Enhancing Intrusion Detection Systems with Digital Twin Tech...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international Conference on

作者： S. P. Santhoshkumar P. Sakthivel A. Seetha Manikandan R Mukesh Soni Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Sona College of Technology Salem India Department of Information Technology S.A. Engineering College Chennai India Department of CSE Nandha Engineering College (Autonomous) Erode India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The need for strong security measures to ward off complex cyber-attacks has grown in tandem with the exponential growth of cyber-physical systems (CPS) and Internet of Things (IoT) devices. This paper lays forth a thorough plan to improve cyber-physical security by bolstering intrusion detection systems (IDS) with the use of digital twin technology (DTT). Virtual representations of physical systems, or "digital twins," allow for sophisticated modelling, predictive analytics, and real-time monitoring. Incorporating DTT with IDS allows for proactive and dynamic threat detection and response in this system. Datasets generated by an ICS testbed— a device designed to monitor storage reservoir water levels and turbulence—form the basis of the suggested architecture. The SCADA system uses a number of indicators to assess the efficacy of several machine learning (ML) models that are used to detect and categorize intrusions. The findings show that ML approaches are great at detecting intrusions, with low false positive rates and high detection rates. Also, the IDS can now identify more complex and unique assaults because to DTT's incorporation, which enables continuous system learning and adaptability. An important step forward in cyber-physical security, this research shows that digital twins have the ability to change intrusion detection systems (IDS) from reactive to proactive. The framework offers a durable and scalable approach to safeguard vital infrastructures from ever-changing cyber threats, and its ramifications reach into a wide range of industrial applications. The integration of Digital Twin Technology (DTT) with Intrusion Detection systems (IDS) for cyber-physical security showed significant improvements in threat detection. Test results revealed a 40% increase in early intrusion detection accuracy compared to traditional IDS models. Additionally, proactive system adaptations through DTT reduced response time to potential threats by 25%, enhancing overall syste

关键词： Adaptation models Intrusion detection SCADA systems Predictive models Reservoirs Threat assessment Real-time systems Digital twins Internet of Things Integrated circuit modeling

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Design of Temperature Monitoring System Using distributed Intelligent CAN Bus networks 8th

Design of Temperature Monitoring System Using Distributed In...

引用

8th international Conference on Artificial Intelligence and Security (ICAIS)

作者： Su, Yu Hou, Lingjuan Li, Sinan Jiang, Zhaochang China Elect Technol Grp Corp Res Inst 54 Shijiazhuang 050081 Hebei Peoples R China Tianjin Normal Univ Sch Management Tianjin 300387 Peoples R China Sehan Univ Grad Sch Mokpo 58613 South Korea

ISBN: (纸本)9783031067945;9783031067938

Temperature sensing and control remain challenges since temperature is a critical index of the increasingly growing in agriculture and smart planting. CAN (Controller Area Network) bus, due to its high stability, anti-interference ability, timeliness, adaptability to complex industrial environment, good performance, it has been widely used industrial communication among networked instrument. In this project a STC89C52 microcontroller is selected as the master chip, using as an independent CAN controller SJA1000, PCA82C250 as the CAN bus driver to achieve CAN bus communication, designed and implemented based on CAN bus network multi-node temperature monitoring system, including the master node controller, from the node controller and PC server-side procedures. The system in real time from a node can collect temperature sensor DS18B20 temperature signal transmitted via the CAN controller SJA1000 CAN controller to the receiver, and then before sending the master node, the master node receives the signal can be displayed on the LCD screen, you can also via RS-232 bus serial transmission to the host computer program computer display, the other computer can also send commands through the host computer to control the master/slave node, enabling on-line monitoring distributed CAN bus network in site-specific farming.

关键词： Controller Area Network bus (CAN-bus) Temperature sensor Temperature measurement and conditioning Fieldbus Embedded systems (ES)

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Maximizing Profit of the Service Providers through Efficient Content Placement and Retrieval in an IoT-enabled Edge-Cloud Platform

Maximizing Profit of the Service Providers through Efficient...

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international symposium on Advanced networks and Telecommunication systems (ANTS)

作者： Preet Gandhi Abhishriya Mahanta Manojit Ghose Aryabartta Sahu Department of Computer Science and Engineering Indian Institute of Technology Guwahati India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In today’s 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time processing and retrieval of these data is time-consuming. Therefore, an edge-cloud framework comes in handy where the content generated is placed on the edge nodes of the network, and it can reach the endusers in a time-bound manner. A majority of the previous research works on content placement under the edge-cloud architecture primarily aimed at improving the latency of the system. However, the approach proposed in this paper aims to maximize the profit of the service provider by effectively placing the content in a hierarchically organized edge-cloud network. We propose two strategies and compare them with random and greedy approaches for both synthetic and real data sets. The experimental results demonstrate the effectiveness of the proposed strategies, and the profit of the service providers increases by 38.81% (maximum) and 10.51% (on average) for the synthetic dataset and by 65.74% (maximum) and 22.83% (on average) for the real-world dataset when compared with the greedy approach.

关键词： Cloud computing 5G mobile communication computer architecture Real-time systems Telecommunications Internet of Things Synthetic data

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Proceedings - 2021 IEEE 34th international symposium on computer-Based Medical systems, CBMS 2021

Proceedings - 2021 IEEE 34th International Symposium on Comp...

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34th IEEE international symposium on computer-Based Medical systems, CBMS 2021

ISBN: (纸本)9781665441216

The proceedings contain 103 papers. The topics discussed include: securing embedded medical devices using dual-factor authentication;black hole algorithm with convolutional neural networks for the creation of a brain-switch using visual perception;a fully automated deep learning pipeline to assess muscle mass in brain tumor patients;how healthcare professionals comprehend process models - an empirical eye tracking analysis;semi-supervised learning for cervical precancer detection;classification of autism spectrum disorder severity using eye tracking data based on visual attention model;detecting covid-19 from breathing and coughing sounds using deep neural networks;detecting COVID-19 from Breathing and coughing sounds using deep neural networks;and user-centric vs whole-stream learning for EMA prediction.

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Safety and Sustainability Indicators of Variable Speed Limit Control in Intelligent Driver Models

Safety and Sustainability Indicators of Variable Speed Limit...

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international symposium on High Capacity Optical networks and Enabling Technologies, HONET

作者： Mădălin-Dorin Pop Viorel Cărbune Viorica Sudacevschi Department of Computer and Information Technology Politehnica University of Timişoara Timişoara România Department of Computer Sciences and Systems Engineering Technical University of Moldova Chişinău Republic of Moldova

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

The variable speed limit (VSL) control is responsible for real-time adjustment of speed limits based on traffic conditions in sensor-based environments. Integration of this traffic management strategy with intelligent driver models (IDMs) enables the development of control systems for autonomous vehicles. This article identifies the indicators used for the performance evaluation of VSL strategies implemented in IDM. A classification of these indicators into safety and sustainability categories simplifies the identification of the targeted measures of effectiveness (MOEs) of a proposed VSL strategy, such as safety, mobility, or environmental sustainability. Furthermore, this article reviews existing VSL strategies developed for IDMs and discusses how their assessment was carried out based on identified indicators.

关键词： Performance evaluation Smart cities Reviews Robot sensing systems Control systems Real-time systems Safety Sustainable development Autonomous vehicles Vehicles

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Implementation and Performance Evaluation of Load Balanced Routing in SDN based Fat Tree Data Center

Implementation and Performance Evaluation of Load Balanced R...

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international Conference on Information systems and computer networks (ISCON)

作者： Raj P. Dhanya V. S. Anitha Department of Computer Science and Engineering Government Engineering College Wayanad APJ Abdul Kalam Technological University Kerala India

As the number of data intensive and distributed applications increase, data centers are highly utilized for their storage, processing and accessing. These applications cause throughput sensitive incast traffic in the data center. Traditional shortest path routing algorithms cause traffic hotspots in the shortest path links and lead to congestion. Therefore, load balanced routing strategies need to be employed to spread the traffic across multiple paths. This work employs the idea of multi path routing to achieve load balancing in SDN based Fat tree Data center. It implements Equal Cost Multi Path routing algorithm on Fat tree Data Center Network in Ryu SDN controller and evaluates its load balancing performance against traditional Shortest Path Routing algorithm under different data center benchmark traffic scenarios. We have conducted an exhaustive study about the load balancing effect of ECMP on Ryu based SDN Fat tree data center and found that ECMP increases Average network throughput by 68% and Average Link utilization by 60%. Also packet drop is decreased by 44% with ECMP.

关键词： Performance evaluation Data centers Network topology Benchmark testing Traffic control Routing Throughput

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