2022 IEEE Future Networks World Forum (FNWF)最新文献_第9页

Towards 3D Flexible 6G Networks: Status, Challenges and Technical Enablers 迈向3D灵活6G网络:现状、挑战和技术推动因素

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00122

Sokratis Barmpounakis, A. Georgakopoulos, P. Vlacheas, P. Demestichas

引用次数: 0

A Score Function Heuristic for Crosstalk- and Fragmentation-Aware Dynamic Routing, Modulation, Core, and Spectrum Allocation in SDM-EONs sdm - eon中串扰和片段感知动态路由、调制、核心和频谱分配的分数函数启发式算法

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00023

Jaya Lakshmi Ravipudi, M. Brandt-Pearce

引用次数: 1

Pre-hospital Triage Improvement for Ambulances via 5G Video and Vital Data Communication 通过5G视频和重要数据通信改善救护车院前分诊

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00104

Haibin Zhang, Y. Toh, Iñaki Martín Soroa, D. Morris, Marie-Pauline Roukens

引用次数: 0

Decoupling Statistical Trends from Data Volume on LDP-Based Spatio-Temporal Data Collection 基于ldp时空数据采集的数据量统计解耦趋势

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00053

Taisho Sasada, Yuzo Taenaka, Y. Kadobayashi

{"title":"Decoupling Statistical Trends from Data Volume on LDP-Based Spatio-Temporal Data Collection","authors":"Taisho Sasada, Yuzo Taenaka, Y. Kadobayashi","doi":"10.1109/FNWF55208.2022.00053","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00053","url":null,"abstract":"Spatio-temporal data is useful for various applications such as urban planning, epidemiology, and natural disasters, but causes exposure of private information, such as home/workplace addresses, because it involves people's trajec-tories. Local Differential Privacy (LDP) based processing is a promising technology for removing sensitive information from spatio-temporal data. A LDP-based processing adds a certain amount of noise to make each piece of data indistinguishable while keeping its intrinsic value. However, LDP is vulnerable to data amplification. When a data store receives data from any device, the data store only appends the received data to existing data. This allows anyone to inject any amount of data into the data and manipulate the trend of the whole data. To tackle this problem, we design a data collection method enabling a data store to collect statistical trends of data from every device irrespective of the data volume. We utilize an Oblivious Transfer (OT) protocol that performs a packet sampling at the reception side, the data store. This sampling enables the collection of statistical trends but requires adjusting LDP processing because the amount of noise is determined by the assumption that the data store receives every piece of LDP-processed data. We then propose an adjustment method for LDP-based process based on the Euclidean algorithm. We conducted qualitative and experimental overhead analysis and showed that the proposed method decouples the relationship between statistical trend and data volume. We also show the processing load can be acceptable on small devices such as smartphones and loT.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128115396","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: 1

Network Traffic as a Federated Testbed Service 作为联邦测试平台服务的网络流量

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00086

Jack Brassil

{"title":"Network Traffic as a Federated Testbed Service","authors":"Jack Brassil","doi":"10.1109/FNWF55208.2022.00086","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00086","url":null,"abstract":"We describe a prototype of Science Traffic as a Service (STAAS), a decentralized, cooperative system to collect, filter and distribute a diverse collection of real and synthetic data traffic to the global experimental research testbed user community. Available on-demand to networking experimenters through a web dashboard, the tool promises to elevate traffic selection and distribution to a first class experimental instrumentation resource. We believe the alternatives to providing this service on large-scale federated testbeds are increasingly unworkable for experimenters. As backbone networks increasingly deploy 100–1000 Gbps communications links we are moving beyond the point where experimenters can reasonably be asked to independently, safely, and efficiently create test traffic that provides the realism that their investigations will demand. We seek to deploy our prototype at campuses and testbeds attached to the emerging FABRIC mid-scale networking research infrastructure. We describe prototype design, operation and implementation, and how it is integrated with existing campus networking infrastructure. We explain how remote experimenters will request and acquire network traffic to study. We detail our process for forwarding campus traffic onto the experimental testbed, while striving to preserve both the timing integrity of the flows and the data privacy of their payloads.†","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121349658","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

Cloud Native Applications Profiling using a Graph Neural Networks Approach 使用图神经网络方法分析云原生应用程序

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00046

Amine Boukhtouta, Taous Madi, M. Pourzandi, H. Alameddine

{"title":"Cloud Native Applications Profiling using a Graph Neural Networks Approach","authors":"Amine Boukhtouta, Taous Madi, M. Pourzandi, H. Alameddine","doi":"10.1109/FNWF55208.2022.00046","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00046","url":null,"abstract":"The convergence of Telecommunication and industry operational networks towards cloud native applications has enabled the idea to integrate protection layers to harden security posture and management of cloud native based deployments. In this paper, we propose a data-driven approach to support detection of anomalies in cloud native application based on a graph neural network. The essence of the profiling relies on capturing interactions between different perspectives in cloud native applications through a network dependency graph and transforming it to a computational graph neural network. The latter is used to profile different deployed assets like micro-service types, workloads' namespaces, worker machines, management and orchestration machines as well as clusters. As a first phase of the profiling, we consider a fine-grained profiling on microservice types with an emphasis on network traffic indicators. These indicators are collected on distributed Kubernetes (K8S) deployment premises. Experimental results shows good trade-off in terms of accuracy and recall with respect to micro-service types profiling (around 96%). In addition, we used predictions entropy scores to infer anomalies in testing data. These scores allow to segregate between benign and anomalous graphs, where we identified 19 out of 23 anomalies. Moreover, by using entropy scores, we can conduct a root cause analysis to infer problematic micro-services.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123447137","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: 1

Efficient Transfer Learning in 6G 6G中的高效迁移学习

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00062

S. Parsaeefard, A. Leon-Garcia

引用次数: 0

No Limits – Smart Cellular Edges for Cross-Border Continuity of Automotive Services 无极限——汽车服务跨界连续性的智能蜂窝边缘

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00089

Girma M. Yilma, Nina Slamnik-Kriještorac, M. Liebsch, A. Francescon, J. Márquez-Barja

引用次数: 1

Multi-Stage, Dynamic Control of Wireless Network Slices with Polymorphic Algorithms 基于多态算法的无线网络切片多阶段动态控制

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00085

Ravi Potluri, Kristen Young, Jin Yang

引用次数: 0

5G-MOBIX: The Greece–Turkey Cross–Border Corridor 5G Deployment and Use Cases Results 5G- mobix:希腊-土耳其跨境走廊5G部署和用例结果

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00016

G. Kalem, Fofy Setaki, A. Georgakopoulos, Serhat Col, Sotiris Messinis, Tahir Sari, Maciej Muehleisen, K. Katsaros, Arda Taha Candan

引用次数: 0