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A Review on Reinforcement Learning enabled Cooperative Spectrum Sensing 基于强化学习的协同频谱感知研究进展
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048946
Thi Thu Hien Pham, Sungrae Cho
{"title":"A Review on Reinforcement Learning enabled Cooperative Spectrum Sensing","authors":"Thi Thu Hien Pham, Sungrae Cho","doi":"10.1109/ICOIN56518.2023.10048946","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048946","url":null,"abstract":"As the number of devices joining the network explodes, new radio frequency spectrum bands are in greater demand. It is envisaged that cognitive radio networks would solve this issue by providing secondary users (SUs) with opportunistic access to licensed frequency bands from the main network. In order to overcome multi-path fading and shadowing issues, cooperative spectrum sensing (CSS) had been introduced, which allows SUs to share their sensing results and make decisions in a cooperative manner. Reinforcement learning then enters the scene as a highly potent technology that enables SUs to choose the best possible actions that conserve time and energy while guaranteeing a good performance. This paper presents an overview of existing reinforcement learning-based cooperative spectrum sensing schemes and includes a brief description of several existing challenges as well as possible future directions.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"19 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":"116133120","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
VNDN-Fuzzy - A strategy to mitigate the forwarding interests broadcast storm problem in VNDN networks VNDN- fuzzy -一种缓解VNDN网络转发兴趣广播风暴问题的策略
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049030
I. Cunha, J. Celestino, M. Fernandez, Ahmed Patel, M. Monteiro
{"title":"VNDN-Fuzzy - A strategy to mitigate the forwarding interests broadcast storm problem in VNDN networks","authors":"I. Cunha, J. Celestino, M. Fernandez, Ahmed Patel, M. Monteiro","doi":"10.1109/ICOIN56518.2023.10049030","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10049030","url":null,"abstract":"Named Data Networking (NDN) has been considered a promising network architecture for Vehicular Ad Hoc Networks (VANETs), what became known as Vehicular Named-Data Networking (VNDN). This new paradigm brings the potential to improve Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) that are inefficient in urban intelligent transport scenarios. Despite the advantages, VNDN brings inherent problems, such as the routing interest packages on NDN, which causes serious problem in the vehicular environment. The broadcast storm attack results in a huge amount of packet loss, provoking transmission overload. In addition, the link disconnection caused by the highly dynamic topology leads to a low package delivery rate. In this article, we propose a strategy for forwarding packages of interest in VNDN networks, using fuzzy logic to mitigate the broadcast storm. The proposal also aims to avoid packet collision and efficient data recovery, which the approach is based on metrics such as the nodes distance, the link stability and the signal quality. The results show a reduction in the number of Interest and Data packets without disrupting network performance maintaining adequate Interest delays.","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":"116449654","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 New Chapter for Medical Image Generation: The Stable Diffusion Method 医学图像生成的新篇章:稳定扩散法
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049010
Loc X. Nguyen, P. Aung, H. Q. Le, Seong-Bae Park, C. Hong
{"title":"A New Chapter for Medical Image Generation: The Stable Diffusion Method","authors":"Loc X. Nguyen, P. Aung, H. Q. Le, Seong-Bae Park, C. Hong","doi":"10.1109/ICOIN56518.2023.10049010","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10049010","url":null,"abstract":"Data collecting and sharing have been widely accepted and adopted to improve the performance of deep learning models in almost every field. Nevertheless, in the medical field, sharing the data of patients can raise several critical issues, such as privacy and security or even legal issues. Synthetic medical images have been proposed to overcome such challenges; these synthetic images are generated by learning the distribution of realistic medical images but completely different from them so that they can be shared and used across different medical institutions. Currently, the diffusion model (DM) has gained lots of attention due to its potential to generate realistic and high-resolution images, particularly outperforming generative adversarial networks (GANs) in many applications. The DM defines state of the art for various computer vision tasks such as image inpainting, class-conditional image synthesis, and others. However, the diffusion model is time and power consumption due to its large size. Therefore, this paper proposes a lightweight DM to synthesize the medical image; we use computer tomography (CT) scans for SARS-CoV-2 (Covid-19) as the training dataset. Then we do extensive simulations to show the performance of the proposed diffusion model in medical image generation, and then we explain the key component of the model.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"21 4 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":"123502812","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: 5
Exaggerated Advertisement Inspection System for Judging the Suitability of Advertisements in Social Media Environment 判断社交媒体环境下广告适宜性的夸张广告检测系统
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048933
Yohan Park, Yongjin Kim, Jonghyeok Mun, Jongsun Choi, Jaeyoung Choi, Yongyun Cho
{"title":"Exaggerated Advertisement Inspection System for Judging the Suitability of Advertisements in Social Media Environment","authors":"Yohan Park, Yongjin Kim, Jonghyeok Mun, Jongsun Choi, Jaeyoung Choi, Yongyun Cho","doi":"10.1109/ICOIN56518.2023.10048933","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048933","url":null,"abstract":"Recently, as the social media markets are expanding, the amount of health functional food advertisements posted by individual users such as influencers and social media promoters is increasing. Therefore, users need a system that supports them to post false advertisements after inspecting them. In this paper, we propose an exaggerated advertisement inspection system that judges the suitable of advertisements and presents the grounds for disqualification. The proposed system consists of a module that classifies advertisements and explainable artificial intelligence(XAI). The system provides a rationale for judging the results of advertising classification and exaggerated advertisements. Therefore, the user may know why his or her writing is classified as exaggerated advertisement. The language model and embedding model, used in the exaggerated advertisement classification step, check the accuracy of the confusion matrix through the evaluation data. The XAI model checks performance by inputting data designated as exaggerated advertisement by health functional food-related institutions.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"174 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":"122337919","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
Topology Design for Data Center Networks Using Deep Reinforcement Learning 基于深度强化学习的数据中心网络拓扑设计
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048955
Haoran Qi, Zhan Shu, Xiaomin Chen
{"title":"Topology Design for Data Center Networks Using Deep Reinforcement Learning","authors":"Haoran Qi, Zhan Shu, Xiaomin Chen","doi":"10.1109/ICOIN56518.2023.10048955","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048955","url":null,"abstract":"This paper is concerned with the topology design of data center networks (DCNs) for low latency and fewer links using deep reinforcement learning (DRL). Starting from a K-vertex-connected graph, we propose an interactive framework with single-objective and multi-objective DRL agents to learn DCN topologies for given node traffic matrices by choosing link matrices to represent the states and actions as well as using the average shortest path length together with action penalty terms as reward feedback. Comparisons with commonly used DCN topologies are given to show the effectiveness and merits of our method. The results reveal that our learned topologies could achieve lower delay compared with common DCN topologies. Moreover, we believe that the method can be extended to other topology metrics, e.g., throughput, by simply modifying the reward functions.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"123 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":"122486412","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
Development of Activity Management System to Watch over Children 儿童监护活动管理系统的开发
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048950
Ichiu Inoue, Kayoko Yamamoto
{"title":"Development of Activity Management System to Watch over Children","authors":"Ichiu Inoue, Kayoko Yamamoto","doi":"10.1109/ICOIN56518.2023.10048950","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048950","url":null,"abstract":"In Japan, in recent years, the number of incidents in which children become victims has increased. Watching over children activities conducted by local residents are important measures for child-related crime prevention. However, the aging of local residents causes a serious shortfall in human resources for watching over children activities. Against such a backdrop, in this study, a system that integrates Web-Geographic Information Systems (Web-GIS) and a system for managing watching over children activities by local residents was designed and developed. The system can manage watching over children activities by local residents on a daily basis. In addition, the system enables local residents to discover and reduce the blank areas for watching over children activities. Thus, the system can contribute to the realization of safety school routes for children and child-related crime prevention. Administrators should be staffs of public organizations, and users are classified into two type; local residents who are always participate in child-related crime prevention activity and general local residents. The system has two functions for the users and three functions for the administrators in the frontend. In the backend, in order to operate the above five functions, four processes are performed. In regard to future research tasks, the system will be operated and evaluated in Wako City, Saitama Prefecture, Japan to identify the improvement strategies.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"243 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":"122511353","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
The Performance of Graph Neural Network in Detecting Fake News from Social Media Feeds 图神经网络在社交媒体源虚假新闻检测中的性能
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048961
Iftekharul Islam Shovon, Seokjoo Shin
{"title":"The Performance of Graph Neural Network in Detecting Fake News from Social Media Feeds","authors":"Iftekharul Islam Shovon, Seokjoo Shin","doi":"10.1109/ICOIN56518.2023.10048961","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048961","url":null,"abstract":"Misinformation spread due to fake news can have an adverse effect on society and individuals. One of the primary sources through which fake news spreads is social media. Fake news detection in social media is critical and at the same time, it is challenging to solve as the articles are written to appear credible. The nature of deliberate writing makes it more challenging to recognize fake news based on only news content; therefore, it is challenging to detect fake news with only natural language processing (NLP). Adding the users’ activity history and other auxiliary information becomes essential. Hence, in recent years, graph neural networks (GNN) gained momentum in detecting fake news. In this paper, we analyze the performance of the GNN-based model on fake news detection from social media threads and compare them with a traditional machine learning model, LSTM. From our analysis, we can conclude that GNN based models can perform better than baseline LSTM in terms of accuracy, F1-Score, precision, and recall.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"61 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":"123326793","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
Towards Generating Semi-Synthetic Datasets for Network Intrusion Detection System 网络入侵检测系统半合成数据集生成研究
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048962
Ngoc-Truong Nguyen, Ton-Nhan Le, Khanh-Hoi Le Minh, Kim-Hung Le
{"title":"Towards Generating Semi-Synthetic Datasets for Network Intrusion Detection System","authors":"Ngoc-Truong Nguyen, Ton-Nhan Le, Khanh-Hoi Le Minh, Kim-Hung Le","doi":"10.1109/ICOIN56518.2023.10048962","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048962","url":null,"abstract":"We have witnessed the proliferation of machine learning and its applications, especially in network-based intrusion detection systems (NIDS). With the ability to learn complex informative systems from data, machine learning models play a crucial role in identifying and preventing network attacks. However, training these models requires a massive volume of labeled data, which is nontrivial to obtain. Moreover, public datasets are often unbalanced, outdated, and different with network traffic from the networks that need to be protected. Therefore, in this paper, we introduce a framework, namely DGIDS, for generating semi-synthetic datasets for NIDS, which combines synthetic data and regular network traffic collected from the local network. Our proposed framework is capable of producing both benign and attack network data with characteristics similar to those in real scenarios. In practical experiments, we show that the network data generated by DGIDS significantly increase the detection quality of NIDS trained by public datasets from 54% to 90.39%.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"47 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121000328","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
Implementation of Edge Servers on an Open 5G Core Network 开放5G核心网边缘服务器的实现
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049000
P. Vanichchanunt, Ittipon Yamyuan, P. Sasithong, L. Wuttisittikulkij, Sukritta Paripurana
{"title":"Implementation of Edge Servers on an Open 5G Core Network","authors":"P. Vanichchanunt, Ittipon Yamyuan, P. Sasithong, L. Wuttisittikulkij, Sukritta Paripurana","doi":"10.1109/ICOIN56518.2023.10049000","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10049000","url":null,"abstract":"In 5G networks, edge servers play a major role in reducing the latency of services that users experience by locating the service processing at the network border near the users. This paper aims to implement edge servers on a 5G core network by using available open-source software tools. However, there is no open-source software tools for implementing an edge server as an Application Function (AF) of 5G core network as suggested by European Telecommunications Standards Institute (ETSI). To cope with this problem, open-source orchestration platforms based on Kubernetes such as MicroK8s and K3sup which support High Availability (HA) feature with multi-master, have been explored and employed to create edge servers as an additional function to cooperate with 5G core network. In the experiment, the performance of the edge servers implemented by MicroK8s and K3sup are measured and compared in terms of time delay, delay jitter, upload speed, and download speed. From the experiment results, the edge servers created by both platforms show no explicit advantage over each other.","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":"128343695","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
Collaborative Computation Offloading Scheme Based on Deep Reinforcement Learning 基于深度强化学习的协同计算卸载方案
2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/icoin56518.2023.10048957
Jinho Park, K. Chung
{"title":"Collaborative Computation Offloading Scheme Based on Deep Reinforcement Learning","authors":"Jinho Park, K. Chung","doi":"10.1109/icoin56518.2023.10048957","DOIUrl":"https://doi.org/10.1109/icoin56518.2023.10048957","url":null,"abstract":"Deep Reinforcement Learning (DRL)-based computation offloading scheme has been proposed to improve the Quality of Experience (QoE). However, the existing DRL does not consider the temporal states because of the fully connected layer. Also, DRL learns the policy regardless of the importance of experience. To solve these problems, we propose a collaborative computation offloading scheme with DRL. First, we define the objective function about task service time and load balance. Second, we utilize the Least Absolute Shrinkage and Selection Operator (LASSO) regression in the backbone network for considering temporal states. Finally, we prioritize the experience according to the Temporal Difference (TD) error and learning loss. The simulation results show that the proposed scheme achieves high QoE due to low task service time and high load balance.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"125 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":"114789034","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
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