2021 IEEE Global Communications Conference (GLOBECOM)最新文献_第5页

MPTCP under Virtual Machine Scheduling Impact 虚拟机调度影响下的MPTCP

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685569

Phuong Ha, Lisong Xu

{"title":"MPTCP under Virtual Machine Scheduling Impact","authors":"Phuong Ha, Lisong Xu","doi":"10.1109/GLOBECOM46510.2021.9685569","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685569","url":null,"abstract":"Multipath TCP (MPTCP) has captured the networking community's attention in recent years since it simultaneously transfers data over multiple network interfaces, thus increases the performance and stability. Existing works on MPTCP study its performance only in traditional wired and wireless networks. Meanwhile, cloud computing has been growing rapidly with lots of applications deployed in private and public clouds, where virtual machine (VM) scheduling techniques are often adopted to share physical CPUs among VMs. This motivates us to study MPTCP's performance under VM scheduling impact. For the first time, we show that VM scheduling negatively impacts all MPTCP subflows' throughput. Specifically, VM scheduling causes the inaccuracy in computing the overall aggressiveness parameter of MPTCP congestion control, which leads to the slow increment of the congestion windows of all MPTCP subflows instead of just a single subflow. This finally results in a poor overall performance of MPTCP in cloud networks. We propose a modified version for MPTCP, which considers VM scheduling noises when MPTCP computes its overall aggressiveness parameter and its congestion windows. Experimental results show that our modified MPTCP performs considerably better (with up to 80% throughput improvement) than the original MPTCP in cloud networks.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124592745","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

Exploiting Ensemble Learning for Edge-assisted Anomaly Detection Scheme in e-healthcare System 基于集成学习的电子医疗系统边缘辅助异常检测方案

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685745

Wei Yao, Kuan Zhang, Chong Yu, Hai Zhao

{"title":"Exploiting Ensemble Learning for Edge-assisted Anomaly Detection Scheme in e-healthcare System","authors":"Wei Yao, Kuan Zhang, Chong Yu, Hai Zhao","doi":"10.1109/GLOBECOM46510.2021.9685745","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685745","url":null,"abstract":"With the thriving of wearable devices and the widespread use of smartphones, the e-healthcare system emerges to cope with the high demand of health services. However, this integrated smart health system is vulnerable to various attacks, including intrusion attacks. Traditional detection schemes generally lack the classifier diversity to identify attacks in complex scenarios that contain a small amount of training data. Moreover, the use of cloud-based attack detection may result in higher detection latency. In this paper, we propose an Edge-assisted Anomaly Detection (EAD) scheme to detect malicious attacks. Specifically, we first identify four types of attackers according to their attacking capabilities. To distinguish attacks from normal behaviors, we then propose a wrapper feature selection method. This selection method eliminates the impact of irrelevant and redundant features so that the detection accuracy can be improved. Moreover, we investigate the diversity of classifiers and exploit ensemble learning to improve the detection rate. To reduce high detection latency in the cloud, edge nodes are used to concurrently implement the proposed lightweight scheme. We evaluate the EAD performance based on two real-world datasets, i.e., NSL-KDD and UNSW-NB15 datasets. The simulation results show that the EAD outperforms other state-of-the-art methods in terms of accuracy, detection rate, and computational complexity. The analysis of detection time validates the fast detection of the proposed EAD compared with cloud-assisted schemes.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697346","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: 4

Interference Cooperation based Resource Allocation in NOMA Terrestrial-Satellite Networks 基于干扰合作的NOMA地星网资源分配

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685107

Yaomin Zhang, Haijun Zhang, Huang‐Cheng Zhou, Wei Li

引用次数: 1

Semantic Analysis and Preference Capturing on Attentive Networks for Rating Prediction 基于关注网络的语义分析和偏好捕获用于评级预测

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685227

Cheng-Han Chou, Bi-Ru Dai

{"title":"Semantic Analysis and Preference Capturing on Attentive Networks for Rating Prediction","authors":"Cheng-Han Chou, Bi-Ru Dai","doi":"10.1109/GLOBECOM46510.2021.9685227","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685227","url":null,"abstract":"Nowadays, people receive an enormous amount of information from day to day. However, they are only interested in information which matches their preferences. Thus, retrieving such information becomes an significant task, in our case, the reviews composed by users. Matrix Factorization (MF) based methods achieve fairly good performances on recommendation tasks. However, there exist several crucial issues with MF - based methods such as cold-start problems and data sparseness. In order to address the above issues, numerous recommendation models are proposed which obtained stellar performances. Nonetheless, we figured that there is not a more comprehensive framework that enhances its performance through retrieving user preference and item trend. Hence, we propose a novel approach to tackle the aforementioned issues. A hierarchical construction with user preference and item trend capturing is employed in this proposed framework. The performance excels in comparison to state-of-the-art models by testing on several real-world datasets. Experimental results verified that our framework can extract useful features even under sparse data.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130522384","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

Spider: Deep Learning-driven Sparse Mobile Traffic Measurement Collection and Reconstruction 蜘蛛:深度学习驱动的稀疏移动流量测量收集与重建

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685804

Yin Fang, A. Diallo, Chaoyun Zhang, P. Patras

{"title":"Spider: Deep Learning-driven Sparse Mobile Traffic Measurement Collection and Reconstruction","authors":"Yin Fang, A. Diallo, Chaoyun Zhang, P. Patras","doi":"10.1109/GLOBECOM46510.2021.9685804","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685804","url":null,"abstract":"Data-driven mobile network management hinges on accurate traffic measurements, which routinely require expensive specialized equipment and substantial local storage capabilities, and bear high data transfer overheads. To overcome these challenges, in this paper we propose Spider, a deep-learning-driven mobile traffic measurement collection and reconstruction framework, which reduces the cost of data collection while retaining state-of-the-art accuracy in inferring mobile traffic consumption with fine geographic granularity. Spider harnesses Reinforcement Learning and tackles large action spaces to train a policy network that selectively samples a minimal number of cells where data should be collected. We further introduce a fast and accurate neural model that extracts spatiotemporal correlations from historical data to reconstruct network-wide traffic consumption based on sparse measurements. Experiments we conduct with a real-world mobile traffic dataset demonstrate that Spider samples 48% fewer cells as compared to several benchmarks considered, and yields up to 67% lower reconstruction errors than state-of-the-art interpolation methods. Moreover, our framework can adapt to previously unseen traffic patterns.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130583027","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

Trade-offs in large blockchain-based IoT system design 基于区块链的大型物联网系统设计中的权衡

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685119

J. Misic, V. Mišić, Xiaolin Chang

引用次数: 0

Dual-Net for Joint Channel Estimation and Data Recovery in Grant-free Massive Access 双网联合信道估计与无授权海量接入数据恢复

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685696

Yanna Bai, Wei Chen, Yuan Ma, Ning Wang, Bo Ai

引用次数: 2

Caching Assisted Correlated Task Offloading for IoT Devices in Mobile Edge Computing 移动边缘计算中物联网设备的缓存辅助相关任务卸载

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685828

Chaogang Tang, Chunsheng Zhu, Huaming Wu, Chunyan Liu, J. Rodrigues

引用次数: 3

CNN-Based Signal Detector for IM-OFDMA 基于cnn的IM-OFDMA信号检测器

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9685285

Özgür Alaca, S. Althunibat, Serhan Yarkan, Scott L. Miller, K. Qaraqe

引用次数: 6

Thermal Profiling by WiFi Sensing in IoT Networks 物联网网络中WiFi传感的热分析

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI: 10.1109/GLOBECOM46510.2021.9686022

Junye Li, Aryan Sharma, Deepak Mishra, Aruna Seneviratne

{"title":"Thermal Profiling by WiFi Sensing in IoT Networks","authors":"Junye Li, Aryan Sharma, Deepak Mishra, Aruna Seneviratne","doi":"10.1109/GLOBECOM46510.2021.9686022","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9686022","url":null,"abstract":"Extensive literature has shown the possibility of using WiFi to sense large scale environmental features such as people, movement, and human gestures. To our best knowledge, there has been no investigation on identifying the microscopic changes in a channel due to atmospheric temperature variations. We identify this as a real world use case, since there are scenarios such as Data Centres where WiFi traffic is omnipresent and temperature monitoring is important. We develop a framework for sensing temperature using WiFi Channel State Information (CSI), proposing that the increased kinetic energy of ambient gas particles will affect the wireless link. To validate this, our paper uses low wavelength 5GHz WiFi CSI from commodity hardware to measure how the channel changes as the ambient temperature is raised. Empirically, we demonstrate that the CSI amplitude value drops at a rate of 13 per degree Celsius rise in the ambient temperature based on the testing platform, and developed regressions models with ± 1°C accuracy in the majority of cases. Moreover, we have shown that WiFi subcarriers exhibit a frequency-selective behaviour in their varying responses to the rise in ambient temperature.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208849","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