Intelligent and Converged Networks最新文献_第2页

Honeycomb Lung Segmentation Network Based on P2T with CNN Two-Branch Parallelism 基于P2T和CNN两分支并行的蜂窝肺分割网络

Intelligent and Converged Networks Pub Date : 2024-12-01 DOI: 10.23919/ICN.2024.0023

Zhichao Li;Gang Li;Ling Zhang;Guijuan Cheng;Shan Wu

{"title":"Honeycomb Lung Segmentation Network Based on P2T with CNN Two-Branch Parallelism","authors":"Zhichao Li;Gang Li;Ling Zhang;Guijuan Cheng;Shan Wu","doi":"10.23919/ICN.2024.0023","DOIUrl":"https://doi.org/10.23919/ICN.2024.0023","url":null,"abstract":"Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obtain the global features of the lesions, the convolutional branch is used to extract the lesions' local feature information, and the feature fusion module is designed to effectively fuse the features in the dual branches; subsequently, in the decoder, the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region. To resolve the problem of model accuracy degradation caused by the class imbalance of the dataset, an adaptive weighted hybrid loss function is designed for model training. Finally, extensive experimental results show that the method in this paper performs well on the Honeycomb Lung Dataset, with Intersection over Union (IoU), mean Intersection over Union (mloU), Dice coefficient, and Precision (Pre) of 0.8750,0.9363,0.9298, and 0.9012, respectively, which are better than other methods. In addition, its IoU and Dice coefficient of 0.7941 and 0.8875 on the Covid dataset further prove its excellent performance.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"336-355"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Received Value Flipping Based Sphere Decoding Algorithm for Polar Codes 基于接收值翻转的极坐标码球面译码算法

Intelligent and Converged Networks Pub Date : 2024-11-04 DOI: 10.23919/ICN.2024.0025

Rui Wang;Haiqiang Chen;Yan Chen;Yuanbo Liu;Xiangcheng Li;Youming Sun;Qingnian Li

{"title":"Received Value Flipping Based Sphere Decoding Algorithm for Polar Codes","authors":"Rui Wang;Haiqiang Chen;Yan Chen;Yuanbo Liu;Xiangcheng Li;Youming Sun;Qingnian Li","doi":"10.23919/ICN.2024.0025","DOIUrl":"https://doi.org/10.23919/ICN.2024.0025","url":null,"abstract":"Polar codes are considered as one of the most competitive channel coding schemes for the future wireless communication system. To improve the performance of polar codes with short code-length for control channels, a sphere decoding algorithm based on received value flipping is proposed in this paper. When a codeword fails the cyclic redundancy check, the algorithm flips the received value with low reliability and forms a new received sequence. Then, this new sequence is sent to the decoder for another decoding attempt. In addition, we also compare the performance of different flipping sets and evaluate the influence of the associated flipping set sizes. Simulation results show that, the proposed algorithm can achieve performance improvement over additive white Gaussian noise channel with acceptable complexity. For the (64, 16) polar code, the proposed algorithm can achieve about 0.23 dB performance gain at frame error rate = \u0000<tex>$10^{-3}$</tex>\u0000, compared to the conventional sphere decoding algorithm. Finally, we also verify the applicability of the proposed algorithm over Rayleigh fading channel and observe similar results.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"370-379"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Machine Learning Techniques for Optical Wireless Communication in Intelligent Transport Systems 智能交通系统中光学无线通信的机器学习技术综述

Intelligent and Converged Networks Pub Date : 2024-11-04 DOI: 10.23919/ICN.2024.0019

Thabelang Sefako;Fang Yang;Jian Song;Reevana Balmahoon;Ling Cheng

{"title":"A Review of Machine Learning Techniques for Optical Wireless Communication in Intelligent Transport Systems","authors":"Thabelang Sefako;Fang Yang;Jian Song;Reevana Balmahoon;Ling Cheng","doi":"10.23919/ICN.2024.0019","DOIUrl":"https://doi.org/10.23919/ICN.2024.0019","url":null,"abstract":"Intelligent Transport Systems (ITS) are crucial for safety, efficiency, and reduced congestion in transportation. They require efficient, secure, high-speed communication. Radio Frequency (RF) technologies like Fifth Generation (5G), Beyond 5G (B5G), and Sixth Generation (6G) are promising, but spectrum scarcity mandates coexistence with Optical Wireless Communication (OWC) networks, which offer high data rates and security, forming a strong foundation for hybrid RF/OWC applications in ITS. In this paper, we delve into the application of Machine Learning (ML) to enhance data communications within OWC systems in ITS. We commence by conducting an in-depth examination of the data communication prerequisites and the associated challenges within the ITS domain. Subsequently, we elucidate the compelling rationale behind the convergence of heterogeneous RF technologies with OWC for data communications in ITS scenarios. Our investigation then pivots towards elucidating the indispensable role played by ML in optimizing data communications via OWC within ITS. To provide a comprehensive perspective, we systematically evaluate and compare a spectrum of ML methodologies employed in OWC ITS data communications. As a culmination of our study, we proffer a set of valuable recommendations and illuminate promising avenues for future research endeavors that warrant further exploration within this critical intersection of ML, OWC, and ITS data communications.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"284-316"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Anomalous Traffic Detection System for Secure Vehicular Communications 用于安全车辆通信的快速异常流量检测系统

Intelligent and Converged Networks Pub Date : 2024-11-04 DOI: 10.23919/ICN.2024.0021

Qasem Abu Al-Haija;Abdulaziz A. Alsulami

{"title":"Fast Anomalous Traffic Detection System for Secure Vehicular Communications","authors":"Qasem Abu Al-Haija;Abdulaziz A. Alsulami","doi":"10.23919/ICN.2024.0021","DOIUrl":"https://doi.org/10.23919/ICN.2024.0021","url":null,"abstract":"In modern automotive systems, introducing multiple connectivity protocols has transformed in-vehicle network communication, resulting in the widely recognized Controller Area Network (CAN) standard. Despite its ubiquitous use, the CAN protocol lacks critical security features, making vehicle communications vulnerable to message injection attacks. These assaults might confuse original electronic control units (ECUs) or cause system failures, emphasizing the need for strong cybersecurity solutions in automobile networks. This study addresses this need by developing a quick and efficient abnormal traffic detection system to protect vehicular communications from cyber attacks. The proposed system utilizes four machine learning techniques: Adaboost Trees (ABT), Coarse Decision Trees (CDT), Naive Bayes Classifier (NBC), and Support Vector Machine (SVM). These models were carefully assessed on the Car-Hacking-2018 dataset, which simulates real-time vehicular communication scenarios. Specifically, the system considers five balanced classes, including one normal traffic class and four classes for message injection attacks over the in-vehicle controller area network: fuzzy attack, DoS attack, RPM attack (spoofing), and gear attack (spoofing). Our best performance outcomes belong to the ABT model, which notched 99.8% classification accuracy and \u0000<tex>$6.67 mutext{s}$</tex>\u0000 of classification overhead. Such results have outweighed existing in-vehicle intrusion detection systems employing the same/similar dataset.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 4","pages":"356-369"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Trusted Demand Response Bidding Mechanism Empowered by Blockchain 区块链赋能的分布式可信需求响应竞标机制

Intelligent and Converged Networks Pub Date : 2024-09-01 DOI: 10.23919/ICN.2024.0013

Lei Wang;Tong Li;Chao Yang;Jiang Chen;Yang Liu;Shuai Ren

{"title":"Distributed Trusted Demand Response Bidding Mechanism Empowered by Blockchain","authors":"Lei Wang;Tong Li;Chao Yang;Jiang Chen;Yang Liu;Shuai Ren","doi":"10.23919/ICN.2024.0013","DOIUrl":"https://doi.org/10.23919/ICN.2024.0013","url":null,"abstract":"In the demand response process involving multi-agent participation, multiple parties' interests are involved and response execution status supervision is required. Traditional centralized demand response systems lack trust attributes. At the same time, traditional centralized cloud management can no longer support massive terminal services, resulting in delays in demand response services. We build a distributed trusted demand response architecture based on blockchain, illustrating the information interaction process in the demand bidding process and container-based edge-side heterogeneous resource management. We also propose a demand bidding algorithm that takes into account both the day-ahead market and the intraday market, aiming to maximize the aggregator's benefits. In addition, a virtual resource management algorithm to support demand response tasks is also proposed to optimize computing resource allocation and meet business latency requirements. Simulation results demonstrate that compared with only cloud computing or edge computing, the solution we proposed can reduce response delay by more than 39% for the sample system. Energy cost is saved by about 10.25% during container scheduling.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 3","pages":"181-191"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Slicing Based Joint Optimization of Beamforming and Resource Selection Scheme for Energy Efficient D2D Networks 基于网络切片的高能效 D2D 网络波束成形和资源选择联合优化方案

Intelligent and Converged Networks Pub Date : 2024-09-01 DOI: 10.23919/ICN.2024.0018

Biroju Papachary;Rajeev Arya;Bhasker Dappuri

{"title":"Network Slicing Based Joint Optimization of Beamforming and Resource Selection Scheme for Energy Efficient D2D Networks","authors":"Biroju Papachary;Rajeev Arya;Bhasker Dappuri","doi":"10.23919/ICN.2024.0018","DOIUrl":"https://doi.org/10.23919/ICN.2024.0018","url":null,"abstract":"The integration of network slicing into a Device-to-Device (D2D) network is a promising technological approach for efficiently accommodating Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) services. In this work, we aim to optimize energy efficiency and resource allocation in a D2D underlay cellular network by jointly optimizing beamforming and Resource Sharing Unit (RSU) selection. The problem of our investigation involves a Mixed-Integer Nonlinear Program (MINLP). To solve the problem effectively, we utilize the concept of the Dinkelbach method, the iterative weighted £1-norm technique, and the principles of Difference of Convex (DC) programming. To simplify the solution, we merge these methods into a two-step process using Semi-Definite Relaxation (SDR) and Successive Convex Approximation (SCA). The integration of network slicing and the optimization of short packet transmission are the proposed strategies to enhance spectral efficiency and satisfy the demand for low-latency and high-data-rate requirement applications. The Simulation results validate that the proposed method outperforms the benchmark schemes, demonstrating higher throughput ranging from 11.79% to 28.67% for URLLC users, and 13.67% to 35.89% for eMBB users, respectively.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 3","pages":"248-264"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolved Differential Model for Sporadic Graph Time-Series Prediction 用于零星图形时间序列预测的进化差分模型

Intelligent and Converged Networks Pub Date : 2024-09-01 DOI: 10.23919/ICN.2024.0017

Yucheng Xing;Jacqueline Wu;Yingru Liu;Xuewen Yang;Xin Wang

{"title":"Evolved Differential Model for Sporadic Graph Time-Series Prediction","authors":"Yucheng Xing;Jacqueline Wu;Yingru Liu;Xuewen Yang;Xin Wang","doi":"10.23919/ICN.2024.0017","DOIUrl":"https://doi.org/10.23919/ICN.2024.0017","url":null,"abstract":"Sensing signals of many real-world network systems, such as traffic network or microgrid, could be sparse and irregular in both spatial and temporal domains due to reasons such as cost reduction, noise corruption, or device malfunction. It is a fundamental but challenging problem to model the continuous dynamics of a system from the sporadic observations on the network of nodes, which is generally represented as a graph. In this paper, we propose a deep learning model called Evolved Differential Model (EDM) to model the continuous-time stochastic process from partial observations on graph. Our model incorporates diffusion convolutional network to parameterize continuous-time system dynamics by graph Ordinary Differential Equation (ODE) and graph Stochastic Differential Equation (SDE). The graph ODE is applied to accurately capture the spatial-temporal relation and extract hidden features from the data. The graph SDE can efficiently capture the underlying uncertainty of the network systems. With the recurrent ODE-SDE scheme, EDM can serve as an accurate online predictive model that is effective for either monitoring or analyzing the real-world networked objects. Through extensive experiments on several datasets, we demonstrate that EDM outperforms existing methods in online prediction tasks.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 3","pages":"237-247"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Crowdsourced Federated Learning Architecture with Personalized Privacy Preservation 具有个性化隐私保护功能的众包联合学习架构

Intelligent and Converged Networks Pub Date : 2024-09-01 DOI: 10.23919/ICN.2024.0014

Yunfan Xu;Xuesong Qiu;Fan Zhang;Jiakai Hao

{"title":"Crowdsourced Federated Learning Architecture with Personalized Privacy Preservation","authors":"Yunfan Xu;Xuesong Qiu;Fan Zhang;Jiakai Hao","doi":"10.23919/ICN.2024.0014","DOIUrl":"https://doi.org/10.23919/ICN.2024.0014","url":null,"abstract":"In crowdsourced federated learning, differential privacy is commonly used to prevent the aggregation server from recovering training data from the models uploaded by clients to achieve privacy preservation. However, improper privacy budget settings and perturbation methods will severely impact model performance. In order to achieve a harmonious equilibrium between privacy preservation and model performance, we propose a novel architecture for crowdsourced federated learning with personalized privacy preservation. In our architecture, to avoid the issue of poor model performance due to excessive privacy preservation requirements, we establish a two-stage dynamic game between the task requestor and clients to formulate the optimal privacy preservation strategy, allowing each client to independently control privacy preservation level. Additionally, we design a differential privacy perturbation mechanism based on weight priorities. It divides the weights based on their relevance with local data, applying different levels of perturbation to different types of weights. Finally, we conduct experiments on the proposed perturbation mechanism, and the experimental results indicate that our approach can achieve better global model performance with the same privacy budget.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 3","pages":"192-206"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Q-Learning Model for Sequential Task Offloading in Edge AI Systems 边缘人工智能系统中用于顺序任务卸载的深度 Q 学习模型

Intelligent and Converged Networks Pub Date : 2024-09-01 DOI: 10.23919/ICN.2024.0015

Dong Liu;Shiheng Gu;Xinyu Fan;Xu Zheng

{"title":"A Deep Q-Learning Model for Sequential Task Offloading in Edge AI Systems","authors":"Dong Liu;Shiheng Gu;Xinyu Fan;Xu Zheng","doi":"10.23919/ICN.2024.0015","DOIUrl":"https://doi.org/10.23919/ICN.2024.0015","url":null,"abstract":"Currently, edge Artificial Intelligence (AI) systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars, and supported diverse applications and services. This fundamental supports come from continuous data analysis and computation over these devices. Considering the resource constraints of terminal devices, multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for execution. Previous efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems, such as the encryption, decryption and consensus algorithm supporting the implementation of Blockchain techniques. Therefore, this paper proposes a new pipelined task scheduling algorithm (referred to as PTS-RDQN), which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task scheduling. Specifically, a co-optimization strategy based on Rainbow Deep Q-Learning (RainbowDQN) is proposed to allocate computation tasks for mobile devices, edge and cloud servers, which is able to comprehensively consider the balance of task turnaround time, link quality, and other factors, thus effectively improving system performance and user experience. In addition, a task scheduling strategy based on PTS-RDQN is proposed, which is capable of realizing dynamic task allocation according to device load. The results based on many simulation experiments show that the proposed method can effectively improve the resource utilization, and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 3","pages":"207-221"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated and Controlled Patch Generation for Enhanced Fixing of Communication Software Vulnerabilities 自动生成受控补丁，加强对通信软件漏洞的修复

Intelligent and Converged Networks Pub Date : 2024-09-01 DOI: 10.23919/ICN.2024.0016

Shuo Feng;Shuai Yuan;Zhitao Guan;Xiaojiang Du

引用次数: 0