2022 IEEE Symposium on Computers and Communications (ISCC)最新文献_第8页

Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks 异构蜂窝网络中的联合缓存放置与请求路由优化

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9913008

Marisangila Alves, Guilherme Piĉgas Koslovski

{"title":"Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks","authors":"Marisangila Alves, Guilherme Piĉgas Koslovski","doi":"10.1109/ISCC55528.2022.9913008","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913008","url":null,"abstract":"The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134495543","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

A Comparative Study of ML Algorithms for Scenario-agnostic Predictions in Healthcare 医疗保健中场景不可知预测的ML算法比较研究

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912808

Argyro Mavrogiorgou, S. Kleftakis, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis

{"title":"A Comparative Study of ML Algorithms for Scenario-agnostic Predictions in Healthcare","authors":"Argyro Mavrogiorgou, S. Kleftakis, N. Zafeiropoulos, Konstantinos Mavrogiorgos, Athanasios Kiourtis, D. Kyriazis","doi":"10.1109/ISCC55528.2022.9912808","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912808","url":null,"abstract":"The extraction of useful knowledge from collected data has always been the holy grail for enterprises and researchers, supporting efficient decision making, provided service's optimization and profit maximization. However, this task is easier said than done, since it presupposes the application of complex mathematical models/algorithms. Data Analysis has prospered due to the continuous demand to simplify and optimize the knowledge extraction process. Several mechanisms in different domains have been developed, consisting of various techniques to analyze specific data. The need for such mechanisms is even greater in healthcare, since there exist data of different complexity that may provide high-valuable knowledge, if properly analyzed. Considering these challenges, this paper proposes a mechanism for performing Data Analysis in diverse scenarios' healthcare data to extract valuable insights. The mechanism can collect data and apply several Machine Learning algorithms to ensure the best result about the prediction of certain features of the provided data.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130983674","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: 2

Assessing Anonymous and Selfish Free-rider Attacks in Federated Learning 评估联邦学习中的匿名和自私搭便车攻击

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912903

Jianhua Wang, Xiaolin Chang, Ricardo J. Rodríguez, Yixiang Wang

引用次数: 3

Evaluating TCP Throughput Predictability from Packet Traces using Recurrent Neural Network 利用递归神经网络从数据包轨迹评估TCP吞吐量可预测性

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912956

Ryu Kazama, H. Abe, Chunghan Lee

引用次数: 0

GreenLoRaWAN: An energy efficient and resilient LoRaWAN communication protocol GreenLoRaWAN:一种节能、弹性的LoRaWAN通信协议

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912972

Thomas Dimakis, M. Louta, Thomas S. Kyriakidis, Alexandros-Apostolos A. Boulogeorgos, Konstantina Banti, Ioanna Karampelia, Nikos Papadimitriou

引用次数: 2

StinAttack: A Lightweight and Effective Adversarial Attack Simulation to Ensemble IDSs for Satellite- Terrestrial Integrated Network StinAttack:一种针对星地集成网络集成入侵防御系统的轻量级有效对抗攻击仿真

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912891

Shangyuan Zhuang, Jiyan Sun, Hangsheng Zhang, Xiaohui Kuang, Ling Pang, Haitao Liu, Yinlong Liu

{"title":"StinAttack: A Lightweight and Effective Adversarial Attack Simulation to Ensemble IDSs for Satellite- Terrestrial Integrated Network","authors":"Shangyuan Zhuang, Jiyan Sun, Hangsheng Zhang, Xiaohui Kuang, Ling Pang, Haitao Liu, Yinlong Liu","doi":"10.1109/ISCC55528.2022.9912891","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912891","url":null,"abstract":"Effective adversarial attacks simulation is essential for the deployment of ensemble Intrusion Detection Systems (en- semble IDSs) in Satellite-Terrestrial Integrated Network (STIN). This is because it can automatically generate a large amount of adversarial samples to evaluate the robustness of different classifiers. Based on the result, it can further guide the STIN engineers to select proper classifiers in ensemble IDSs. Moreover, it can help the IDSs improve detect performance by their self- learning property in the adversarial attack process. However, the existing adversarial attack approaches suffer from the problems of low success rate and high overhead of communication and calculation due to the limited computing resources and long communication links of STIN. This results in their inefficiency in STIN. To address the above problems, we provide StinAttack as a robustness evaluation scheme for STIN. First, StinAttack provides a comprehensive and automatic robustness evaluation framework for IDSs in STIN with only few times interactions between terrestrial and satellite nodes. Second, StinAttack proposes an effective adversarial attack simulation based on lightweight gradient evaluation for ensemble IDSs. Third, we conduct experiments on 11 typical IDSs, 4 baseline popular adversarial attacks and our StinAttack. Experimental results show that our approach can effectively attack ensemble IDSs and the evaluation results based on real STIN dataset are instructive for designing secure networks.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132482834","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

Demo: Usage Control using Controlled Privacy Aware Face Recognition 演示:使用控制隐私意识人脸识别的使用控制

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912953

Arpad Müller, Wisam Abbasi, A. Saracino

引用次数: 0

LL-VAS: Adaptation Method for Low-Latency 360-degree Video Streaming over Mobile Networks 移动网络低延迟360度视频流适配方法

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912995

D. Nguyen, L. Ngan, Lai Huyen Thuong, Truong Thu Huong

引用次数: 1

Deep Reinforcement Learning based Mobility-Aware Service Migration for Multi-access Edge Computing Environment 基于深度强化学习的多访问边缘计算环境下移动感知业务迁移

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912842

Yaqiang Zhang, Rengang Li, Yaqian Zhao, Ruyang Li

{"title":"Deep Reinforcement Learning based Mobility-Aware Service Migration for Multi-access Edge Computing Environment","authors":"Yaqiang Zhang, Rengang Li, Yaqian Zhao, Ruyang Li","doi":"10.1109/ISCC55528.2022.9912842","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912842","url":null,"abstract":"Multi-access Edge Computing (MEC) plays an im-portant role for providing end users with high reliability and low latency services at the edge of mobile network. In the scenario of Internet of Vehicles (IoV), vehicle users continually access nearby base stations to offload real-time tasks for reducing their computing overhead, while the ongoing services on current deployed edge nodes may be far away from users with the vehicles moving, potentially resulting in a high delay of data transmission. To address this challenge, in this paper, we propose a Deep Reinforcement Learning (DRL)-based mobility-aware service migration mechanism for effectively reducing the service delay and migration delay of the network. The proposed technique is adopted by re-calibrating required services at edge locations near the mobile user. Edge network state and user movement information are considered to ensure the generation of real-time service migration decision. Extensive experiments are conducted, and evaluation results demonstrate that our proposed DRL-based technique can effectively reduce the long-term average delay of the MEC system, compared with the state-of-the-art techniques.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134491463","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: 2

A Comparative Study on Shared Precomputed Restoration and Shared Backup Path Protection in EONs EONs共享预计算恢复与共享备份路径保护的比较研究

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912774

F. Arpanaei, Shayan Hajipour, H. Beyranvand, J. A. Hernández, D. Larrabeiti

引用次数: 1