IEEE Networking Letters最新文献_第3页

Serve Yourself! Federated Power Control for AI-Native 5G and Beyond 为你自己!AI-Native 5G及以后的联合功率控制

IEEE Networking Letters Pub Date : 2024-12-02 DOI: 10.1109/LNET.2024.3509792

Saad Abouzahir;Essaid Sabir;Halima Elbiaze;Mohamed Sadik

引用次数: 0

Latency Bounds for TSN Scheduling in the Presence of Clock Synchronization 有时钟同步时TSN调度的时延限制

IEEE Networking Letters Pub Date : 2024-11-27 DOI: 10.1109/LNET.2024.3507792

Aviroop Ghosh;Saleh Yousefi;Thomas Kunz

引用次数: 0

Wireless MAC Protocol Synthesis and Optimization With Multi-Agent Distributed Reinforcement Learning 基于多智能体分布式强化学习的无线MAC协议综合与优化

IEEE Networking Letters Pub Date : 2024-11-20 DOI: 10.1109/LNET.2024.3503289

Navid Keshtiarast;Oliver Renaldi;Marina Petrova

引用次数: 0

Maestro: LLM-Driven Collaborative Automation of Intent-Based 6G Networks 大师：llm驱动的基于意图的6G网络协同自动化

IEEE Networking Letters Pub Date : 2024-11-20 DOI: 10.1109/LNET.2024.3503292

Ilias Chatzistefanidis;Andrea Leone;Navid Nikaein

引用次数: 0

Jamming Attack Mitigation in Wireless Federated Learning Networks Using Bayesian Games 利用贝叶斯博弈缓解无线联邦学习网络的干扰攻击

IEEE Networking Letters Pub Date : 2024-11-14 DOI: 10.1109/LNET.2024.3499360

Sofia Barkatsa;Maria Diamanti;Panagiotis Charatsaris;Eirini Eleni Tsiropoulou;Symeon Papavassiliou

引用次数: 0

Queue Modeling for Geospatial Service on Edge-Cloud Architecture 边缘云架构下地理空间服务的队列建模

IEEE Networking Letters Pub Date : 2024-11-12 DOI: 10.1109/LNET.2024.3496842

Fabio Franchi;Fabio Graziosi;Francesco Smarra;Eleonora Di Fina

{"title":"Queue Modeling for Geospatial Service on Edge-Cloud Architecture","authors":"Fabio Franchi;Fabio Graziosi;Francesco Smarra;Eleonora Di Fina","doi":"10.1109/LNET.2024.3496842","DOIUrl":"https://doi.org/10.1109/LNET.2024.3496842","url":null,"abstract":"The exponential growth and complexity of geospatial data necessitate innovative management strategies to address the increasing computational demands of Geographical Information System (GIS) services. GIS is connected to the social context, and its use as a decision-support tool is gaining broader acceptance with the need to ensure high Quality of Service (QoS). While cloud computing offers new capabilities for GIS, the physical distance between cloud infrastructure and end-users often leads to high network latency, compromising QoS. Multi-Access Edge Computing (MEC) emerges as a promising solution to limit latency and enhance system performance, particularly for real-time and multi-device applications. However, integrating GIS services into edge-cloud architectures presents significant challenges in terms of task scheduling and service placement. This letter proposes a queueing theory-based model designed to optimize the performance of GIS workloads within edge-cloud architectures. The model, based on a closed Jackson network, is designed to assist in the efficient design and deployment of edge systems that meet QoS and Service Level Agreement (SLA) requirements. The proposed framework is validated through a real-world case study, with performance metrics such as throughput and response time evaluated to ensure optimal system sizing and performance. The results underscore the potential of this approach for designing scalable and efficient edge-cloud architectures tailored to geospatial services.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"36-40"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relay Type Link Fabrication Attack in SDN: A Review SDN 中的中继型链路制造攻击：综述

IEEE Networking Letters Pub Date : 2024-11-07 DOI: 10.1109/LNET.2024.3493942

Getahun Metaferia;Frezewd Lemma

{"title":"Relay Type Link Fabrication Attack in SDN: A Review","authors":"Getahun Metaferia;Frezewd Lemma","doi":"10.1109/LNET.2024.3493942","DOIUrl":"https://doi.org/10.1109/LNET.2024.3493942","url":null,"abstract":"Software-defined Networking (SDN) is an innovative network architecture tailored to address the modern demands of network virtualization and cloud computing, which require features such as programmability, flexibility, agility, and openness to foster innovation. However, this architecture also brings forth new security challenges, particularly due to the separation of the data plane from the control plane. Our investigation centers on a specific vulnerability termed link fabrication, which can lead to topology poisoning. A compromised network topology can cause substantial disruptions across the entire network infrastructure. Through a systematic survey, we identified that significant research efforts have been directed towards mitigating link fabrication attacks. We classified the existing studies into six categories of vulnerabilities: Host-based, port amnesia, invisible assailant attack, topology freezing, switch-based link fabrication, and link latency. Furthermore, our survey highlights several open challenges in areas such as Programmable dataplane, dedicated attack trees and threat models, active defense and mitigation strategies, as well as controller awareness and machine learning. To address the vulnerabilities identified, we propose the implementation of a distance-bounding protocol concept at the control plane as a potential solution.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 1","pages":"51-55"},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading 通过最佳MEC-Device卸载在6G中部署设备上AIGC推理服务

IEEE Networking Letters Pub Date : 2024-11-04 DOI: 10.1109/LNET.2024.3490954

Changshi Zhou;Weiqi Liu;Tao Han;Nirwan Ansari

引用次数: 0

IEEE COMMUNICATIONS SOCIETY IEEE 通信学会

IEEE Networking Letters Pub Date : 2024-10-25 DOI: 10.1109/LNET.2024.3482833

引用次数: 0

Multi-Modal Transformer and Reinforcement Learning-Based Beam Management 基于多模态变压器和强化学习的波束管理

IEEE Networking Letters Pub Date : 2024-10-25 DOI: 10.1109/LNET.2024.3486260

Mohammad Ghassemi;Han Zhang;Ali Afana;Akram Bin Sediq;Melike Erol-Kantarci

{"title":"Multi-Modal Transformer and Reinforcement Learning-Based Beam Management","authors":"Mohammad Ghassemi;Han Zhang;Ali Afana;Akram Bin Sediq;Melike Erol-Kantarci","doi":"10.1109/LNET.2024.3486260","DOIUrl":"https://doi.org/10.1109/LNET.2024.3486260","url":null,"abstract":"Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the recently emerging multi-modal transformer (MMT) is a promising technique that can process multi-modal data by capturing long-range dependencies. While MMT is highly effective in handling multi-modal data and providing robust beam management, integrating reinforcement learning (RL) further enhances their adaptability in dynamic environments. In this letter, we propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In the first step, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. In the second step, we employ RL for fast beam decision-making within each group, which in return maximizes throughput. Our proposed framework is tested on a 6G dataset. In this testing scenario, it achieves higher beam prediction accuracy and system throughput compared to both the MMT-only based method and the RL-only based method.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 4","pages":"222-226"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0