IEEE Transactions on Network Science and Engineering最新文献_第4页

Energy-Efficient Federated Learning Through UAV Edge Under Location Uncertainties 位置不确定下无人机边缘节能联邦学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-01 DOI: 10.1109/TNSE.2024.3489554

Chen Wang;Xiao Tang;Daosen Zhai;Ruonan Zhang;Nurzhan Ussipov;Yan Zhang

{"title":"Energy-Efficient Federated Learning Through UAV Edge Under Location Uncertainties","authors":"Chen Wang;Xiao Tang;Daosen Zhai;Ruonan Zhang;Nurzhan Ussipov;Yan Zhang","doi":"10.1109/TNSE.2024.3489554","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3489554","url":null,"abstract":"Federated Learning (FL) and Mobile Edge Computing (MEC) technologies alleviate the burden of deploying artificial intelligence (AI) on wireless devices with low computational capabilities. However, they also introduce energy consumption challenges in FL model training and data processing. In this paper, we employ Unmanned Aerial Vehicles (UAVs) to collect data from wireless devices and carry edge servers to assist the central server located at the base station in training FL model. We also consider the deviation of UAVs' locations to address its impact on network performance. Specifically, we formulate a robust joint optimization problem to minimize the energy consumption of UAVs, considering the computational resources, transmit power, transmission time, and FL model accuracy. Moreover, Gaussian-distributed uncertainties caused by deviation in UAV locations result in probabilistic constraints on data offloading. We initially employ the Bernstein-type inequality (BTI) to transform probabilistic constraints into deterministic forms. Subsequently, we adopt the Block Coordinate Descent (BCD) to separate the problem into three subproblems. Simulation results demonstrate a significant reduction in energy consumption and superiority in robustness.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"223-236"},"PeriodicalIF":6.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interventional Causal Structure Discovery Over Graphical Models With Convergence and Optimality Guarantees 具有收敛性和最优性保证的图形模型的介入因果结构发现

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-01 DOI: 10.1109/TNSE.2024.3487301

Chengbo Qiu;Kai Yang

{"title":"Interventional Causal Structure Discovery Over Graphical Models With Convergence and Optimality Guarantees","authors":"Chengbo Qiu;Kai Yang","doi":"10.1109/TNSE.2024.3487301","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3487301","url":null,"abstract":"Learning causal structure from sampled data is a fundamental problem with applications in various fields, including healthcare, machine learning and artificial intelligence. Traditional methods predominantly rely on observational data, but there exist limits regarding the identifiability of causal structures with only observational data. Interventional data, on the other hand, helps establish a cause-and-effect relationship by breaking the influence of confounding variables. It remains to date under-explored to develop a mathematical framework that seamlessly integrates both observational and interventional data in causal structure learning. Furthermore, existing studies often focus on centralized approaches, necessitating the transfer of entire datasets to a single server, which lead to considerable communication overhead and heightened risks to privacy. To tackle these challenges, we develop a \u0000<bold>b\u0000i\u0000<bold>l\u0000evel p\u0000<bold>o\u0000lynomial \u0000<bold>o\u0000pti\u0000<bold>m\u0000ization (Bloom) framework. Bloom not only provides a powerful mathematical modeling framework, underpinned by theoretical support, for causal structure discovery from both interventional and observational data, but also aspires to an efficient causal discovery algorithm with convergence and optimality guarantees. We further extend Bloom to a distributed setting to reduce the communication overhead and mitigate data privacy risks. It is seen through experiments on both synthetic and real-world datasets that Bloom markedly surpasses other leading learning algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"156-172"},"PeriodicalIF":6.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network 移动边缘计算网络中多aav巡逻检测系统的节能均衡任务分配策略

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-01 DOI: 10.1109/TNSE.2024.3488839

Kuan Jia;Dingcheng Yang;Yapeng Wang;Tianyun Shui;Chenji Liu

{"title":"Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network","authors":"Kuan Jia;Dingcheng Yang;Yapeng Wang;Tianyun Shui;Chenji Liu","doi":"10.1109/TNSE.2024.3488839","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3488839","url":null,"abstract":"This paper considers a patrol inspection scenario where multiple autonomous aerial vehicles (AAVs) are adopted to traverse multiple predetermined cruise points for data collection. The AAVs are connected to cellular networks and they would offload the collected data to the ground base stations (GBSs) for data processing within the constrained duration. This paper proposes a balanced task assignment strategy among patrol AAVs and an energy-efficient trajectory design method. Through jointly optimizing the cruise point assignment, communication scheduling, computational allocation, and AAV trajectory, a novel solution can be obtained to balance the multiple AAVs' task completion time and minimize the total energy consumption. Firstly, we propose a novel clustering method that considers geometry topology, communication rate, and offload volume; it can determine each AAV's cruise points and balance the AAVs' patrol task. Secondly, a hybrid Time-Energy traveling salesman problem is formulated to analyze the cruise point traversal sequence, and the energy-efficient AAV trajectory can be designed by adopting the successive convex approximation (SCA) technique and block coordinate descent (BCD) scheme. The numerical results demonstrate that the proposed balanced task assignment strategy can efficiently balance the multiple AAVs' tasks. Moreover, the min-max task completion time and total energy consumption performance of the proposed solution outperform that of the current conventional approach.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"210-222"},"PeriodicalIF":6.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incorporating Mobility Prediction in Handover Procedure for Frequent-Handover Mitigation in Small-Cell Networks 基于移动性预测的小蜂窝网络频率切换控制

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-31 DOI: 10.1109/TNSE.2024.3487415

Syed Maaz Shahid;Jee-Hyeon Na;Sungoh Kwon

{"title":"Incorporating Mobility Prediction in Handover Procedure for Frequent-Handover Mitigation in Small-Cell Networks","authors":"Syed Maaz Shahid;Jee-Hyeon Na;Sungoh Kwon","doi":"10.1109/TNSE.2024.3487415","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3487415","url":null,"abstract":"Small cells are deployed in high-density environments to provide additional capacity and improve network coverage, supporting high-speed, high-quality mobile broadband services. However, the deployment of small cells increases the impact of user mobility on handover performance. Trends in the different movements of users at the edge of small cells lead to an excessive number of unnecessary handovers. Since user mobility is not purely random, and the overlapping coverage areas of small cells are very limited, handover management in small cells is direction-dependent. This paper proposes a handover algorithm incorporating user mobility information into the handover procedure to mitigate frequent handovers in a small-cell network. The proposed algorithm observes the pattern in the reference signal received power (RSRP) of a candidate target cell during the time to trigger to detect the change in the users' movements. Based on the RSRP pattern, the algorithm makes an optimal handover decision by selecting a target cell in the user's path. The proposed algorithm does not require information on users' previous movements because A3 event-based measurement reporting tracks user mobility. Via simulations, we show that the proposed algorithm reduces the number of handovers without sacrificing the network throughput in different network environments and performs satisfactorily in high-shadowing environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"186-197"},"PeriodicalIF":6.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incentive Mechanism Design for Multi-Round Federated Learning With a Single Budget 单一预算下多轮联邦学习的激励机制设计

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-30 DOI: 10.1109/TNSE.2024.3488719

Zhihao Ren;Xinglin Zhang;Wing W. Y. Ng;Junna Zhang

{"title":"Incentive Mechanism Design for Multi-Round Federated Learning With a Single Budget","authors":"Zhihao Ren;Xinglin Zhang;Wing W. Y. Ng;Junna Zhang","doi":"10.1109/TNSE.2024.3488719","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3488719","url":null,"abstract":"Federated learning (FL) is a popular distributed learning paradigm. In practical applications, FL faces two major challenges: (1) Participants inevitably incur computational and communication costs during training, which may discourage their participation; (2) The local data of participants is usually non-IID, which significantly affects the global model's performance. To address these challenges, in this paper, we model the FL incentive processas a budget-constrained cumulative quality maximization problem (BCQM). Unlike most existing works that focus on a single round of FL, BCQM fully encompasses the entire multi-round FL process with a single budget. Then, we propose a comprehensive incentive mechanism named \u0000<underline>R\u0000everse \u0000<underline>A\u0000uction for \u0000<underline>B\u0000udget-constrained n\u0000<underline>O\u0000n-IID fede\u0000<underline>R\u0000ated lear\u0000<underline>N\u0000ing (RABORN) to solve BCQM. RABORN covers the entire FL process while ensuring several desirable properties. We also prove RABORN's theoretical performance. Moreover, compared to baselines on real-world datasets, RABORN exhibits significant advantages. Specifically, on MNIST, Fashion-MNIST, and CIFAR-10, RABORN achieves final accuracies that are respectively 2.94%, 5.94%, and 21.75% higher than baselines. Correspondingly, when the final model accuracies on MNIST, Fashion-MNIST, and CIFAR-10 converge to 80%, 70%, and 40%, RABORN reduces communication rounds by over 33%, 45%, and 74% compared to baselines, while increasing the remaining budget by over 30%, 19%, and 130%, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"198-209"},"PeriodicalIF":6.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fully Interactive Graph-Based Trajectory Prediction via Topological Scenario Representation 基于拓扑场景表示的完全交互式图的轨迹预测

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-28 DOI: 10.1109/TNSE.2024.3486539

Xinran Li;Xiuxian Li;Li Li;Jie Chen

{"title":"Fully Interactive Graph-Based Trajectory Prediction via Topological Scenario Representation","authors":"Xinran Li;Xiuxian Li;Li Li;Jie Chen","doi":"10.1109/TNSE.2024.3486539","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3486539","url":null,"abstract":"The accurate trajectory prediction for vehicles and other traffic agents is essential for the safety and efficiency of transportation environment construction. However, the trajectory prediction task can be affected by many factors such as road constraints, vehicle intentions, interactions with nearby agents and so forth, which makes the prediction challenging and time-consuming. To address the complex traffic conditions and heterogeneous impact factors, this study proposes a fully interactive graph-based trajectory prediction method with the topological scenario representation. Specifically, the traffic scenario is firstly constructed as a topological graph to maintain the spatial relationship among agents and map. The temporal features of traffic states are then obtained via a Gated Recurrent Unit processor. After that, two types of interaction graph are generated based on the topological scenario and a directed edge-enhanced graph network is adopted for the extraction of both inter-agent and agent-map interactive features. Finally, a Graph Convolutional Network block is employed to encode the whole scenario context information. A Long Short-Term Memory decoder is used for future trajectory generation based on the above spatial-temporal interactive features. The proposed model is trained and validated on Argoverse2 dataset, and the results demonstrate the effectiveness of our approach.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"122-133"},"PeriodicalIF":6.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLB-LP: Controller Load Balancing Based on Load Prediction Using Deep Learning for Software-Defined IoT Networks CLB-LP：基于负载预测的基于深度学习的软件定义物联网控制器负载均衡

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-28 DOI: 10.1109/TNSE.2024.3487355

Quanze Liu;Yong Liu;Qian Meng;Tianyi Yu

{"title":"CLB-LP: Controller Load Balancing Based on Load Prediction Using Deep Learning for Software-Defined IoT Networks","authors":"Quanze Liu;Yong Liu;Qian Meng;Tianyi Yu","doi":"10.1109/TNSE.2024.3487355","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3487355","url":null,"abstract":"By integrating Software-Defined Networking (SDN), Software-Defined Internet of Things (SD-IoT) simplifies network configuration while enhancing controllability. The expansion of the IoT scale has led to the emergence of the multiple controller architecture. However, it introduces the challenge of controller load imbalances. Existing schemes primarily focus on dynamic switch migration. Nonetheless, conventional strategies use real-time network information for load measurement and selection of candidate switches, which reduces load balancing performance due to inaccurate load measurement. Moreover, existing approaches struggle to balance load balancing rate and migration cost when selecting the target controllers. Therefore, we propose the controller load balancing based on load prediction (CLB-LP) scheme, which uses historical load data to predict future load, thereby avoiding unnecessary switch migrations. Additionally, we introduce a switch selection algorithm that combines load prediction and migration probability to select candidate switches, effectively improving load balancing performance. Furthermore, we present a target controller selection algorithm based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which improves the load balancing rate while reducing migration cost. Finally, we evaluate the effectiveness of CLB-LP, and compared to existing schemes, its load balancing rate and response time are 29.4% higher and 28.5% lower, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"173-185"},"PeriodicalIF":6.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Urban Traffic Incident Prediction With Vertical Federated Learning: A Feature Selection Based Approach 利用垂直联邦学习优化城市交通事件预测：基于特征选择的方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-28 DOI: 10.1109/TNSE.2024.3487268

Basharat Hussain;Muhammad Khalil Afzal

{"title":"Optimizing Urban Traffic Incident Prediction With Vertical Federated Learning: A Feature Selection Based Approach","authors":"Basharat Hussain;Muhammad Khalil Afzal","doi":"10.1109/TNSE.2024.3487268","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3487268","url":null,"abstract":"Federated learning constitutes a collaborative and shared machine learning paradigm facilitating the joint development of a global model, distinctively addressing privacy concerns while integrating data from various sources. Urban traffic incident prediction (UTIP) tasks inherently require cross-departmental data collaboration, underscoring the significance of federated learning. Specifically, vertical federated learning (VFL) enables multiple participants, each possessing non-overlapping feature subsets, to collectively train predictive models. Recently, researchers have focused on specific VFL issues, such as feature selection and privacy. This study provides a methodology for developing a VFL model utilizing a significant feature selection strategy. The proposed framework is called feature selection-based VFL traffic incident prediction (FSVFL-TIP), and specifically intends to improve incident prediction accuracy. The effectiveness of our suggested model is studied and compared to the baseline VFL model, revealing that our approach outperforms the baseline by 5.7% to 11.6% in test accuracy on two publicly available traffic datasets. Finally, this study explores the improvement in accuracy under various VFL split configurations. The results indicate that VFL is a preferable solution for improved accuracy and communication efficiency while using high-performing feature selection strategies.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"145-155"},"PeriodicalIF":6.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Consensus-Based Filtering Against False Data Injection Attacks 基于共识的分布式虚假数据注入过滤

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-25 DOI: 10.1109/TNSE.2024.3486451

Yuhang Yang;Xiangzhou Gao;Shenmin Song;Zhiqiang Li

{"title":"Distributed Consensus-Based Filtering Against False Data Injection Attacks","authors":"Yuhang Yang;Xiangzhou Gao;Shenmin Song;Zhiqiang Li","doi":"10.1109/TNSE.2024.3486451","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3486451","url":null,"abstract":"Existing distributed state estimation algorithms usually show satisfactory performance when dealing with data bias caused by network-induced phenomena. However, the security characteristics of these algorithms are often significantly affected by more complex and severe network attacks. Specifically, due to the lack of dynamic adaptability and abnormal data detection ability of the estimator, the estimator may deteriorate significantly or even diverge, which poses a serious threat to the stability and reliability of the system. To remedy this issue, we propose a distributed estimation algorithm based on the classical Kalman consensus filter framework. The accuracy of the estimator is significantly improved by utilizing the innovation of neighbor nodes. Furthermore, we construct an adaptive weight allocation mechanism based on the principle of minimizing the estimation error variance according to the possible accuracy differences between different estimators. This mechanism can evaluate the data accuracy of each node, and dynamically adjust its weight accordingly. Subsenquently, an event-triggered detector with random thresholds is designed to enhance the anti-attack ability of the estimator. The detector can monitor the data flow in the network in real time, and identify the potential abnormal or attack behavior by setting dynamic thresholds. Once abnormal data is detected, the detector can immediately trigger corresponding countermeasures to block the propagation path of erroneous data and protect the safe and stable operation of the system. Simulation results are employed to validate the effectiveness of the proposed method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"110-121"},"PeriodicalIF":6.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PTAS: PIFO-Based Time-Aware Shaper for Massive Concurrent Flows in Time-Sensitive Networks PTAS：基于pifo的时间感知整形器，用于时间敏感网络中的海量并发流

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-24 DOI: 10.1109/TNSE.2024.3486038

Jie Ren;Dong Yang;Weiting Zhang;Kai Gong;Weiliang Chen;Wen Wu;Hongke Zhang

{"title":"PTAS: PIFO-Based Time-Aware Shaper for Massive Concurrent Flows in Time-Sensitive Networks","authors":"Jie Ren;Dong Yang;Weiting Zhang;Kai Gong;Weiliang Chen;Wen Wu;Hongke Zhang","doi":"10.1109/TNSE.2024.3486038","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3486038","url":null,"abstract":"Time-sensitive networking (TSN) specifies a time-aware shaper (TAS) to enhance the real-time support of Ethernet for time-triggered traffic. In TAS, flow/frame isolation is necessary for obtaining a deterministic queuing order, which is the key to achieving determinism. However, this isolation is difficult to fulfill when accessing a large number of concurrent flows generated by industrial devices without TSN capabilities due to the uncontrollable sending times and a limited number of egress queues in TAS. In this paper, a novel TAS framework is designed for massive concurrent flows, named push-in-first-out (PIFO) based time-aware shaper (PTAS). Specifically, the PTAS takes advantage of a PIFO queue to buffer and sort all time-triggered frames based on expected sending time (EST), while an ephemeral memory EST allocation algorithm is proposed to obtain ESTs that can ensure a deterministic queuing order. Through frame sorting rather than conventional isolation, the PTAS efficiently provides a deterministic transmission guarantee for massive concurrent flows. Furthermore, the deterministic flow scheduling constraints for the PTAS are derived, based on which the PTAS is proven to have better schedulability than conventional TAS. In addition to simulation, the PTAS is also implemented on a TSN testbed for evaluation. Extensive experimental results demonstrate that the PTAS can reduce up to 37.0% maximum end-to-end latency, 98.8% jitter, and save 40.1% queue resources in high-concurrency scenarios while scheduling up to 15.5% more flows as compared with conventional TAS.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"83-95"},"PeriodicalIF":6.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0