2023 International Conference on Information Networking (ICOIN)最新文献

A Software-Defined Networks Approach for Cyber Physical Systems 网络物理系统的软件定义网络方法

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049028

Jumana Khrais, Mariam Al-Issa, Reham Al-Omari, A. Al-Hammouri

引用次数: 0

A Review on Congestion Control for Internet of Deep Space Things Communication 深空物联网通信拥塞控制研究进展

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048993

Arooj Masood, Taeyun Ha, D. Lakew, Nhu-Ngoc Dao, D. Hua, Geeranuch Woraphonbenjakul, Sungrae Cho

引用次数: 0

Layer-wise Knowledge Distillation for Cross-Device Federated Learning 面向跨设备联邦学习的分层知识蒸馏

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049011

Huy Q. Le, Loc X. Nguyen, Seong-Bae Park, C. Hong

{"title":"Layer-wise Knowledge Distillation for Cross-Device Federated Learning","authors":"Huy Q. Le, Loc X. Nguyen, Seong-Bae Park, C. Hong","doi":"10.1109/ICOIN56518.2023.10049011","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10049011","url":null,"abstract":"Federated Learning (FL) has been proposed as a decentralized machine learning system where multiple clients jointly train the model without sharing private data. In FL, the statistical heterogeneity among devices has become a crucial challenge, which can cause degradation in generalization performance. Previous FL approaches have proven that leveraging the proximal regularization at the local training process can alleviate the divergence of parameter aggregation from biased local models. In this work, to address the heterogeneity issues in conventional FL, we propose a layer-wise knowledge distillation method in federated learning, namely, FedLKD, which regularizes the local training step via the knowledge distillation scheme between global and local models utilizing the small proxy dataset. Hence, FedLKD deploys the layer-wise knowledge distillation of the multiple devices and the global server as the clients’ regularized loss function. A layer-wise knowledge distillation mechanism is introduced to update the local model to exploit the common representation from different layers. Through extensive experiments, we demonstrate that FedLKD outperforms the vanilla FedAvg and FedProx on three federated datasets.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129418550","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

Robustness to Digital Power Adjustment in Transmit Power Allocation for Poor Conditioned LPWA End Devices 条件差的LPWA终端设备发射功率分配中数字功率调整的鲁棒性

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049053

S. Narieda, T. Fujii

引用次数: 0

FedBeam: Federated learning based privacy preserved localization for mass-Beamforming in 5GB FedBeam:基于联邦学习的5GB质量波束形成隐私保护定位

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048980

Deepti Sharma, Adarsh Kumar, Ramesh Babu Battula

{"title":"FedBeam: Federated learning based privacy preserved localization for mass-Beamforming in 5GB","authors":"Deepti Sharma, Adarsh Kumar, Ramesh Babu Battula","doi":"10.1109/ICOIN56518.2023.10048980","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048980","url":null,"abstract":"The overall enhancement of 5G and beyond (5GB) communication accelerates the rise of humongous devices/user equipment’s (UE’s) per-unit area. Massive MIMO (mMIMO) beamforming generates highly directed beams to serve massive UE’s in any area. In dense areas, generating closely distant beams require accurate localization of UEs. Ultra-accurate localization is demanded by implementing the directional beams since even a slight deviation in location leads to significant data loss. With such escalating device density and massive resource demands, the formation of multiple directional beams causes harmful radiation and colossal interference. To optimize beam allocation, a novel idea of mass-beamforming is introduced where a group of users with similar resource demands are served through a single beam. The centroid of massive UE’s in any indoor location is used to create a beam towards a user group. Also, it is essential to maintain users’ location and data privacy. Therefore, this paper proposes a privacy-preserving federated learning-based localization framework, FedBeam, for mass-beamforming in 5GB communication. FedBeam utilizes a deep learning model to acquire precise position location while preserving users’ data privacy. A localization-specific mass-beamforming dataset is modelled to evaluate the proposed framework. The simulation was conducted to validate the accuracy achieved by the proposed framework.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128757385","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 QoS-Aware Routing Mechanism for SDN-Based Integrated Networks 基于sdn集成网络的qos感知路由机制

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048989

Yu Zhang, Mengze Cui, M. Abadeer, S. Gorlatch

引用次数: 2

Performance Analysis of Uplink IM-OFDMA Systems in the Presence of CFO and Rx-IQI 在CFO和Rx-IQI存在下上行IM-OFDMA系统性能分析

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048914

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

{"title":"Performance Analysis of Uplink IM-OFDMA Systems in the Presence of CFO and Rx-IQI","authors":"Özgür Alaca, S. Althunibat, Serhan Yarkan, Scott L. Miller, K. Qaraqe","doi":"10.1109/ICOIN56518.2023.10048914","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048914","url":null,"abstract":"Index modulation-based orthogonal frequency division multiple access (IM-OFDMA) has been recently proposed as a promising candidate for future wireless communication systems due to its superior spectral efficiency and error performance compared to conventional multiple access schemes. Therefore, it has been under investigation by researchers analyzing its performance considering different scenarios and assumptions. Following this direction, this paper presents the error performance of the uplink IM-OFDMA in the presence of carrier frequency offset (CFO) and receiver (Rx) in-phase and quadrature imbalance (IQI). In particular, the individual and joint effects of the CFO and Rx-IQI on the bit-error-rate (BER) performance of the uplink IM-OFDMA scheme are investigated, and then the IM-OFDMA is compared with the conventional OFDMA scheme by considering the detrimental effect of CFO and Rx-IQI. Accordingly, this paper shows that IM-OFDMA achieves better BER performance results than the OFDMA scheme in the presence of CFO and Rx-IQI. However, the obtained results also reveal that the IM-OFDMA scheme is very sensitive to RF impairments even though it performs better than conventional OFDMA.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121220299","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: 3

Resource Allocation Reinforcement Learning for Quality of Service Maintenance in Cloud-Based Services 面向云服务质量维护的资源分配强化学习

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048905

Dupyo Hong, Dongwan Kim, Oh Jung Min, Yongtae Shin

{"title":"Resource Allocation Reinforcement Learning for Quality of Service Maintenance in Cloud-Based Services","authors":"Dupyo Hong, Dongwan Kim, Oh Jung Min, Yongtae Shin","doi":"10.1109/ICOIN56518.2023.10048905","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048905","url":null,"abstract":"Recently, in order to improve the service quality of cloud-based services, research on a reinforcement learning model that predicts an appropriate amount of cloud resources by identifying patterns of user demands is being conducted. Reinforcement learning Q-learning algorithms rely on building a table (Q-table) for Q values, so if the state space and action space are vastly larger, they do not obtain optimal policies. In addition, learning errors in false experiences from the correlation of successive data in reinforcement learning may exist. In this paper, we study reinforcement learning modeling techniques that achieve higher accuracy than existing models by reducing the state definition space of hardware resources arising from services. It is possible to maximize service quality by allocating cloud resources or returning unnecessary resources with accurate resource demand prediction. For performance analysis, the service request prediction results according to the number of learnings were confirmed, and the service request prediction accuracy of three different models according to the neural network was compared. In the experiment, the model applying the proposed Convolutional Neural Network(CNN) neural network modeling technique is found to predict the amount of cloud resources in close proximity to the actual service request as the number of learning increases. We also compare the average of service request prediction accuracy of different models applying three neural networks, Deep Neural Network(DNN), Long Short-Term Memory(LTSM), and CNN, and find that the proposed technique has 3.36% higher prediction accuracy than LSTM-based models, and 40.2% higher than DNN-based models. In the future, additional research is needed, such as building various learning datasets or applying other reinforcement learning algorithms. Further research is also needed on cloud resource rental costs and provisioning latency.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114211662","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 Heuristic Intrusion Detection Approach Using Deep Learning Model 基于深度学习模型的启发式入侵检测方法

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049024

Ching-seh Wu, Sam Chen

引用次数: 1

Network-Assisted Full-Duplex Millimeter-Wave Cell-Free Massive MIMO with Localization-Aided Inter-User Channel Estimation 网络辅助全双工毫米波无小区大规模MIMO与定位辅助用户间信道估计

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048919

Shuto Fukue, G. Abreu, K. Ishibashi

引用次数: 0