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检索条件"任意字段=Conference on Performance and Control of Network Systems II"

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A Deep Learning Approach for In-network Synchrophasor Missing Data Recovery Using Programmable network Switches

A Deep Learning Approach for In-Network Synchrophasor Missin...

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2024 International conference on Communications control and Computing Technologies for Smart Grids

作者： Guo, Xiaoyu Qu, Yanfeng Chen, Gong Jin, Dong Univ Arkansas Elect Engn & Comp Sci Fayetteville AR USA Illinois Inst Technol Comp Sci Chicago IL USA

ISBN: (纸本)9798350318562;9798350318555

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today's electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

关键词： Phasor Measurement Unit Machine Learning Programmable network P4 Smart Grid

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GRACE: Blockchain and Game-based Resource Allocation Scheme for SDN controllers in IoT 59

GRACE: Blockchain and Game-based Resource Allocation Scheme ...

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59th Annual IEEE International conference on Communications (IEEE ICC)

作者： Akhyani, Jenil Patel, Janam Desai, Vartika Gupta, Rajesh Tanwar, Sudeep Bhatia, Jitendra Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India

ISBN: (纸本)9798350304060;9798350304053

Software-defined networks (SDN) are revolutionizing network technology, it is more scalable, secure and low-latency network protocol as compared to traditional systems. SDN efficiently manages the network due to the isolation of the control and data plane. Internet of Things (IoT) has a significant share in Industry 4.0, healthcare, and agriculture. IoT communication powered by SDN significantly improves communication in the network. Motivated by issues related to efficient resource allocation and security in an SDN-IoT environment, we proposed a blockchain and coalition game theory-based scheme GRACE. The game theory enhances the distribution of SDN controllers' resources among IoT devices. Coalition game allow devices to collaborate and share resources efficiently. This results in collaborative decision-making and improved network performance. A smart contract SC is developed and deployed by the central controller, where the devices register themselves, and the controller regulates the network. Data stored on blockchain makes it a secure and immutable system. Our results demonstrates improved time convergence and average switch operations for varying number of IoT. Furthermore, our results include gas cost comparison of SC functions. This proves the efficiency, security, and cost-effectiveness of the GRACE scheme.

关键词： Coalition game Blockchain Game theory Internet of Things Software defined network

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DRL-based Latency-Aware network Slicing in O-RAN with Time-Varying SLAs

DRL-based Latency-Aware Network Slicing in O-RAN with Time-V...

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International conference on Computing, networking and Communications (ICNC)

作者： Raftopoulos, Raoul D'Oro, Salvatore Melodia, Tommaso Schembra, Giovanni Univ Catania CNIT Res Unit Catania Italy Northeastern Univ Inst Wireless Internet Things Boston MA USA

ISBN: (纸本)9798350371000;9798350370997

The Open Radio Access network (Open RAN) paradigm, and its reference architecture proposed by the O-RAN Alliance, is paving the way toward open, interoperable, observable and truly intelligent cellular networks. Crucial to this evolution is Machine Learning (ML), which will play a pivotal role by providing the necessary tools to realize the vision of self-organizing O-RAN systems. However, to be actionable, ML algorithms need to demonstrate high reliability, effectiveness in delivering high performance, and the ability to adapt to varying network conditions, traffic demands and performance requirements. To address these challenges, in this paper we propose a novel Deep Reinforcement Learning (DRL) agent design for O-RAN applications that can learn control policies under varying Service Level Agreement (SLAs) with heterogeneous minimum performance requirements. We focus on the case of RAN slicing and SLAs specifying maximum tolerable end-to-end latency levels. We use the OpenRAN Gym open-source environment to train a DRL agent that can adapt to varying SLAs and compare it against the state-of-the-art. We show that our agent maintains a low SLA violation rate that is 8.3x and 14.4x lower than approaches based on Deep Q-Learning (DQN) and Q-Learning, while consuming respectively 0.3x and 0.6x less resources without the need for re-training.

关键词： O-RAN network Slicing Deep Reinforcement Learning Latency-aware Service Level Agreement

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An Open RAN Framework for the Dynamic control of 5G Service Level Agreements

An Open RAN Framework for the Dynamic Control of 5G Service ...

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4th IEEE conference on network Function Virtualization / 9th Software Defined networks conference (IEEE NFV-SDN)

作者： Moro, Eugenio Polese, Michele Capone, Antonio Melodia, Tommaso Polytech Univ Milan Dept Elect Informat & Bioengn Milan Italy Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

ISBN: (纸本)9798350302547

The heterogeneity of use cases that next-generation wireless systems need to support calls for flexible and programmable networks that can autonomously adapt to the application requirements. Specifically, traffic flows that support critical applications (e.g., vehicular control or safety communications) often come with a requirement in terms of guaranteed performance. At the same time, others are more elastic and can adapt to the resources made available by the network (e.g., video streaming). To this end, the Open Radio Access network (RAN) paradigm is seen as an enabler of dynamic control and adaptation of the protocol stack of 3rd Generation Partnership Project (3GPP) networks in the 5th generation (5G) and beyond. Through its embodiment in the O-RAN Alliance specifications, it introduces the RAN Intelligent controllers (RICs), which enable closed-loop control, leveraging a rich set of RAN Key performance Measurements (KPMs) to build a representation of the network and enforcing dynamic control through the configuration of 3GPP-defined stack parameters. In this paper, we leverage the Open RAN closed-loop control capabilities to design, implement, and evaluate multiple data-driven and dynamic Service Level Agreement (SLA) enforcement policies, capable of adapting the RAN semi-persistent scheduling patterns to match users' requirements. To do so, we implement semi-persistent scheduling capabilities in the OpenAirInterface (OAI) 5G stack, as well as an easily extensible and customizable version of the Open RAN E2 interface that connects the OAI base stations to the near-real-time RIC. We deploy and test our framework on Colosseum, a large-scale hardware-in-the-loop channel emulator. Results confirm the effectiveness of the proposed Open RAN-based solution in managing SLA in near-real-time.

关键词： Radio access networks

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Improving Spiking Neural network performance Using Astrocyte Feedback for Farsi Digit Recognition 31

Improving Spiking Neural Network Performance Using Astrocyte...

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31st International conference on Electrical Engineering, ICEE 2023

作者： Nazari, Malihe Bahrami, Fariba Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Advanced Control Systems Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Advanced Control Systems Lab Tehran Iran

ISBN: (纸本)9798350312560

The third generation of neural networks is called spiking neural networks. Spiking neural networks can not only answer all the problems that can be solved by common neural networks, they can also be computationally more powerful than them. Also, these networks are closer to the biological reality of neurons. According to these reasons, spiking neural networks have received much attention in recent years. In addition to neurons, other elements are also used in spiking neural networks, whose connection with neurons has been proven biologically. These elements are called astrocytes. In this study, we have used astrocyte local feedback in a spiking neural network to improve the accuracy of the network in a digit recognition process. To reach this goal, we use the network proposed by Diehl et al in 2015, then we added astrocyte local feedback to this network. We run the network in both states. The classification accuracy of the network in the absence of astrocytes was 88.48% on Farsi handwritten digits. After adding astrocyte local feedback, it is observed that astrocytes control the activity of neurons and modified the weight of connections based on neuron activity. In this state classification accuracy reached 90.26%. The idea was to create a neural network for learning, where, inspired by the physiological role of astrocytes at the tripartite synapse improves the performance of the neural network and we demonstrate that systems that include neurons and astrocytes perform better than systems incorporating only neurons. © 2023 IEEE.

关键词： Neurons

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Optimizing Lighting Efficiency Through Automated Smart Lighting systems: A Study on network-Based performance 6

Optimizing Lighting Efficiency Through Automated Smart Light...

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6th International conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sari, Lestari Ningratna Anwariningsih, Sri Huning Khusnuliawati, Hardika Susilo, Dahlan Informatics Departement Universitas Sahid Surakarta Surakarta Indonesia

ISBN: (纸本)9798331539603

Electrical lighting systems contribute to approximately 20% of global electricity consumption and 6% of carbon dioxide (CO2) emissions, underlining the urgent need for more efficient and adaptable lighting solutions. Addressing the inefficiency of traditional manual lighting controls is crucial for reducing energy waste, especially in the global energy crisis and increasing carbon emission reduction targets. Despite advances in lighting systems, existing solutions often lack integration of intelligent control and real-time adaptability across diverse networks. This research addresses these gaps by developing a novel automated smart lighting system to optimize energy usage through intelligent control across multiple network environments. The proposed prototype integrates Raspberry Pi modules, relay modules, JavaScript Object Notation (JSON), Thonny IDE, and a web-based Application Programming Interface (API) to enable seamless automation and remote operation. The proposed system was tested under various conditions: local area network (LAN), wide area network (WAN), offline functionality, and across multiple web browsers to evaluate latency and operational reliability. The research results demonstrate that the system performs efficiently online and offline within the same or different networks. For online operations within the same network, delay times for light A ranged between 27 to 39 seconds, light B from 37 to 39 seconds, and light C at 33 seconds. The fastest delay on different networks was 5 seconds, with the longest at 39 seconds. Chrome browser exhibited the fastest performance with a delay of 6 seconds, while Samsung Internet had the longest delay at 39 seconds. These findings highlight the system's adaptability and potential for energy savings, though further work is required to optimize latency and extend network coverage. © 2024 IEEE.

关键词： Web browsers

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Domain Adaptation in Visual Reinforcement Learning via Self-Expert Imitation with Purifying Latent Feature

Domain Adaptation in Visual Reinforcement Learning via Self-...

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2024 International conference on Intelligent Robots and systems

作者： Chen, Lin Huang, Jianan Zhou, Zhen Wang, Yaonan Mo, Yang Miao, Zhiqiang Zeng, Kai Feng, Mingtao Wang, Danwei Hunan Univ Sch Elect & Informat Engn Changsha 410082 Peoples R China Natl Engn Res Ctr Robot Visual Percept & Control Changsha 410082 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9798350377712;9798350377705

Generalizing visual reinforcement learning is fundamental to robot visual navigation, involving the acquisition of a policy from interactions with source environments to facilitate adaptation to analogous, yet unfamiliar target environments. Recent advancements capitalize on data augmentation techniques, self-supervised learning methods, and the generative adversarial network framework to train policy neural networks with enhanced generalizability. However, current methods, upon extracting domain-general latent features, further utilize these features to train the reinforcement learning policy, resulting in a decline in the performance of the learned policy guiding the agent to accomplish tasks. To tackle these challenges, a framework of self-expert imitation with purifying latent features was devised, empowering the policy to achieve robust and stable zero-shot generalization performance in visually similar domains previously unseen, without diminishing the performance of guiding the agent to accomplish tasks. The extraction method of domain-general latent features is proposed to enhance their quality based on the variational autoencoder. Extensive experiments have shown that our policy, compared with state-of-the-art counterparts, does not diminish the performance of the policy guiding the agent to accomplish tasks after generalization.

关键词： Adversarial machine learning

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Evaluation of control-to-control Communication in Industrial 5G network 19

Evaluation of Control-to-Control Communication in Industrial...

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IEEE 19th International Workshop on Factory Communication systems (WFCS)

作者： Ginthoer, David Harutyunyan, Davit Robert Bosch GmbH Gerlingen Germany

ISBN: (纸本)9781665464321

Ethernet-based industrial communication standards are dominating the communication landscape in factories. With the transition towards more flexible and wireless solutions, there is a strong interest to enable existing wired applications, such as control-to-control (C2C) use cases, over a wireless network. In this work, we investigate an example C2C application and validate its performance when operated over 5G. For this purpose, we present measurement results taken from a 5G standalone (SA) deployment in an operational factory. Our results show that 5G can replace existing wired solution, but at the cost of a lowered C2C application efficiency due to longer network latency. We furthermore investigate the impact of cross-traffic to the C2C application and effects of traffic prioritization.

关键词： 5G mobile communication systems

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network performance Analysis Of SCTP Protocol In Multihoming Transmission Through 5G network

Network Performance Analysis Of SCTP Protocol In Multihoming...

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2024 International Visualization, Informatics and Technology conference, IVIT 2024

作者： Baharudin, Shahidatul Arfah Taufik, Nurul Iman Muhammad Munisamy, Sri Banu Lajis, Adidah Universiti Kuala Lumpur Miit Computer Engineering Technology Section Kuala Lumpur Malaysia

ISBN: (纸本)9798350354447

The research presents that the Stream control Transmission Protocol (SCTP) provides a very interesting alternative to classical TCP and UDP, especially in multihoming settings. We therefore specifically sought to test SCTP's performance in multihoming scenarios within 5G networks and compare its reliability to TCP and UDP under various network Address Translation (NAT) settings through complete simulations. We took care to focus on the important performance metrics of throughput, latency, packet loss, and connection stability. The ability of multihoming with SCTP, where a single SCTP endpoint can support several IP addresses, was also tested. In the context that devices will be able to have more than one network interface, this feature is welcomed in 5G networks. An empirical investigation revealed that in NAT settings, while TCP and UDP see higher packet loss and more delay, SCTP maintains a more reliable connection. The as much as needed ultra-reliable and low-latency communications transport protocol for 5G must be very stable. For example, applications such as online gaming, video conferencing, and major IoT operations will be relying on this new network. This paper has specifically emphasized the potential that SCTP's multihoming feature and improved performance metrics hold for 5G networks. This means that SCTP should be recommended to increase network stability and enhance performance, based on the results presented here that have indicated its great potential. Further research will be done on enhancing SCTP for specific 5G use cases under different deployment scenarios and investigating its integration with promising new technologies like edge computing and network slicing. © 2024 IEEE.

关键词： 5G mobile communication systems

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Hierarchy-Aware Bilateral-Branch network for Imbalanced Hierarchical Text Classification 34th

Hierarchy-Aware Bilateral-Branch Network for Imbalanced Hier...

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34th International conference on Database and Expert systems Applications (DEXA)

作者： Zhao, Jiangjiang Lie, Jiyi Fukumoto, Fumiyo Hangzhou Dianzi Univ Hangzhou Peoples R China Univ Yamanashi Kofu Japan

ISBN: (纸本)9783031398209;9783031398216

Hierarchical text classification is an essential task in natural language processing. Existing studies focus only on label hierarchy structure, such as building classifiers for each level of labels or employing the label taxonomic hierarchy to improve the hierarchy classification performance. However, these methods ignore issues with imbalanced datasets, which present tremendous challenges to text classification performance, especially for the tail categories. To this end, we propose Hierarchyaware-Bilateral-Branch-network (HiBBN) to address this problem, where we introduce the bilateral-branch network and apply a hierarchy-aware encoder to model text representation with label dependencies. In addition, HiBBN has two network branches that cooperate with the uniform sampler and reversed sampler, which can deal with the data imbalance problem sufficiently. Therefore, our-model handles both hierarchical structural information and modeling of tail data simultaneously, and extensive experiments on benchmark datasets indicate that our model achieves better performance, especially for fine-grained categories.

关键词： Hierarchical Text Classification Imbalanced Data

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