IEEE Transactions on Network and Service Management最新文献_第3页

SGA-Seq: Station-Aware Graph Attention Sequence Network for Cellular Traffic Prediction SGA-Seq：用于蜂窝交通预测的站点感知图注意序列网络

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-13 DOI: 10.1109/TNSM.2026.3664401

Shiyu Yang;Qunyong Wu;Zhanchao Huang;Zihao Zhuo

{"title":"SGA-Seq: Station-Aware Graph Attention Sequence Network for Cellular Traffic Prediction","authors":"Shiyu Yang;Qunyong Wu;Zhanchao Huang;Zihao Zhuo","doi":"10.1109/TNSM.2026.3664401","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664401","url":null,"abstract":"Cellular traffic prediction is crucial for optimizing network resources and enhancing service quality. Despite progress in existing traffic prediction methods, challenges remain in capturing periodic features, spatial heterogeneity, and abnormal signals. To address these challenges, we propose a Station-aware Graph Attention Sequence Network (SGA-Seq). The core idea is to achieve accurate cellular traffic prediction by adaptively modeling station-specific spatiotemporal patterns and effectively handling complex traffic dynamics. First, we introduce a learnable temporal embedding mechanism to capture temporal features across multiple scales. Second, we design a station-aware graph attention network to model complex spatial relationships across stations. Additionally, by progressively separating regular and abnormal signals layer by layer, we enhance the model’s robustness. Experimental results demonstrate that SGA-Seq outperforms existing methods on five diverse mobile network datasets spanning different scales, including cellular traffic, mobility flow, and communication datasets. Notably, on the V-GCT dataset, our method achieves an 8.04% improvement in Root Mean Squared Error compared to the Spatiotemporal-aware Trend-Seasonality Decomposition Network. The code of SGA-Seq is available at <uri>https://github.com/OvOYu/SGA-Seq</uri>","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2652-2665"},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299641","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

On the Scalability of Access and Mobility Management Function: The Localization Management Function Use Case 访问和移动管理功能的可扩展性：本地化管理功能用例

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-13 DOI: 10.1109/TNSM.2026.3664546

Domenico Scotece;Giuseppe Santaromita;Claudio Fiandrino;Luca Foschini;Domenico Giustiniano

{"title":"On the Scalability of Access and Mobility Management Function: The Localization Management Function Use Case","authors":"Domenico Scotece;Giuseppe Santaromita;Claudio Fiandrino;Luca Foschini;Domenico Giustiniano","doi":"10.1109/TNSM.2026.3664546","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664546","url":null,"abstract":"The adoption of Service-Based Architecture (SBA) in 5G Core Networks (5GC) has significantly transformed the design and operation of the control plane, enabling greater flexibility and agility for cloud-native deployments. While the infrastructure has initially evolved by implementing key functions, there remains significant potential for additional services, such as localization, paving the way for the integration of the Location Management Function (LMF). However, the extensive functional decomposition within SBA leads to consequences, such as the increase of control plane operations. Specifically, we observe that the additional signaling traffic introduced by the presence of the LMF overwhelms the Access and Mobility Management Function (AMF) which is responsible for authentication and mobility. In fact, in mobile positioning, each connected mobile device requires a significant amount of control traffic to support location algorithms in the 5GC. To address this scalability challenge, we analyze the impact of three well-known optimization techniques on location procedures to reduce control message traffic in the specific context of the 5GC, namely a caching system, a request aggregation system, and a service scalability system. Our solutions are evaluated in an OpenAirInterface (OAI) emulated environment with real hardware. After the analysis in the emulated environment, we select the caching system–due to its feasibility–for being analyzed in a real 5G testbed. Our results demonstrate a significant reduction in the additional overhead introduced by the LMF, improving scalability by minimizing the impact on AMF processing time up to a 50% reduction.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2624-2635"},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396356","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SCFusionLocator: A Statement-Level Smart Contract Vulnerability Localization Framework Based on Code Slicing and Multi-Modal Feature Fusion 基于代码切片和多模态特征融合的语句级智能合约漏洞定位框架

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-13 DOI: 10.1109/TNSM.2026.3664599

Jing Huang;Yabo Wang;Honggui Han

{"title":"SCFusionLocator: A Statement-Level Smart Contract Vulnerability Localization Framework Based on Code Slicing and Multi-Modal Feature Fusion","authors":"Jing Huang;Yabo Wang;Honggui Han","doi":"10.1109/TNSM.2026.3664599","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664599","url":null,"abstract":"Smart contract vulnerabilities have led to over <inline-formula> <tex-math>${$}$ </tex-math></inline-formula>20 billion in losses, but existing methods suffer from coarse-grained detection, two-stage “detection-then-localization” pipelines, and insufficient feature extraction. This paper proposes SCFusionLocator, a statement-level vulnerability localization framework for smart contracts. It adopts a novel code-slicing mechanism (via function call graphs and data-flow graphs) to decompose contracts into single-function subcontracts and filter low-saliency statements, paired with source code normalization to reduce noise. A dual-branch architecture captures complementary features: the code-sequence branch uses GraphCodeBERT (with data-flow-aware masking) for semantic extraction, while the graph branch fuses call/control-flow/data-flow graphs into a heterogeneous graph and applies HGAT for structural modeling. SCFusionLocator enables end-to-end statement-level localization by framing tasks as statement classification.We release BJUT_SC02, a large dataset of over 240,000 contracts with line-level labels for 58 vulnerability types. Experiments on BJUT_SC02, SCD, and MANDO datasets show SCFusionLocator outperforms 8 conventional tools and nearly 20 ML baselines, achieving over 90% average F1 at the statement level, with better generalization to similar unknown vulnerabilities, and remains competitive in contract-level detection.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2832-2851"},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362291","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

Revisit Fast Event Matching–Routing for High-Volume Subscriptions 回顾大容量订阅的快速事件匹配路由

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-13 DOI: 10.1109/TNSM.2026.3664517

Qichen Luo;Zhiyun Zhou;Ruisheng Shi;Lina Lan;Qingling Feng;Qifeng Luo;Di Ao

{"title":"Revisit Fast Event Matching–Routing for High-Volume Subscriptions","authors":"Qichen Luo;Zhiyun Zhou;Ruisheng Shi;Lina Lan;Qingling Feng;Qifeng Luo;Di Ao","doi":"10.1109/TNSM.2026.3664517","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664517","url":null,"abstract":"Although many scalable event matching algorithms have been proposed to achieve scalability for publish/subscribe services, the content-based pub/sub system still suffer from performance deterioration when the system has large numbers of subscriptions, and cannot support the requirements of real-time pub/sub data services. In this paper, we model the event matching problem as an existence problem which only care about whether there is at least one matching subscription in the given subscription set, differing from existing works that try to speed up the time-consuming search operation to find all matching subscriptions. To solve this existence problem efficiently, we propose DLS (Discrete Label Set), a novel subscription and event representation model. Based on the DLS model, we propose an event matching algorithm with <inline-formula> <tex-math>$O(N_{d})$ </tex-math></inline-formula> time complexity to support real-time event matching for a large volume of subscriptions and high event arrival speed, where <inline-formula> <tex-math>$N_{d}$ </tex-math></inline-formula> is the node degree in overlay network. Experimental results show that the event matching performance can be improved by several orders of magnitude compared with traditional algorithms.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2807-2817"},"PeriodicalIF":5.4,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362303","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

Enhancing Anomaly Alert Prioritization Through Calibrated Standard Deviation Uncertainty Estimation With an Ensemble of Auto-Encoders 通过自编码器集成校准标准差不确定性估计增强异常警报优先级

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-12 DOI: 10.1109/TNSM.2026.3664298

Jordan F. Masakuna;D'Jeff K. Nkashama;Arian Soltani;Marc Frappier;Pierre-Martin Tardif;Froduald Kabanza

{"title":"Enhancing Anomaly Alert Prioritization Through Calibrated Standard Deviation Uncertainty Estimation With an Ensemble of Auto-Encoders","authors":"Jordan F. Masakuna;D'Jeff K. Nkashama;Arian Soltani;Marc Frappier;Pierre-Martin Tardif;Froduald Kabanza","doi":"10.1109/TNSM.2026.3664298","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3664298","url":null,"abstract":"Deep auto-encoders (AEs) are widely employed deep learning methods in the field of anomaly detection across diverse domains (e.g., cybersecurity analysts managing large volumes of alerts, or medical practitioners monitoring irregular patient signals). In such contexts, practitioners often face challenges of scale and limited processing resources. To cope, strategies such as false positive reduction, human-in-the-loop review, and alert prioritization are commonly adopted. This paper explores the integration of uncertainty quantification (UQ) methods into alert prioritization for anomaly detection using ensembles of AEs. UQ models highlight doubtful classification decisions, enabling analysts to address the most certain alerts first, since higher certainty typically correlates with greater accuracy. Our study reveals a nuanced issue where applying UQ to ensembles of AEs can produce skewed distributions of large reconstruction errors (errors exceeding a pre-defined threshold), which may falsely suggest high uncertainty when standard deviation is used as the metric. Conventionally, a high standard deviation indicates high uncertainty. However, contrary to intuition, large reconstruction errors often reflect AE is strongly confident that an input is anomalous—not uncertainty about it. Moreover, ensembles of AEs generate reconstruction errors with varying ranges, complicating interpretation. To address this, we propose an extension that calibrates the standard deviation distribution of uncertainties, mitigating erroneous prioritization. Evaluation on 10 benchmark datasets demonstrates that our calibration approach improves the effectiveness of UQ methods in prioritizing alerts, while maintaining favorable trade-offs across other key performance metrics.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2481-2493"},"PeriodicalIF":5.4,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223537","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

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks 社区和异构网络中有效分配的强化学习

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-10 DOI: 10.1109/TNSM.2026.3663316

Zhiwei Yu;Chengze Du;Heng Xu;Ying Zhou;Bo Liu;Jialong Li

{"title":"REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks","authors":"Zhiwei Yu;Chengze Du;Heng Xu;Ying Zhou;Bo Liu;Jialong Li","doi":"10.1109/TNSM.2026.3663316","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3663316","url":null,"abstract":"Community GPU(Graphics Processing Unit) platforms are emerging as a cost-effective and democratized alternative to centralized GPU clusters for AI(Artificial Intelligence) workloads, aggregating idle consumer GPUs from globally distributed and heterogeneous environments. However, their extreme hardware/software diversity, volatile availability, and variable network conditions render traditional schedulers ineffective, leading to suboptimal task completion. In this work, we present REACH (Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks), a Transformer-based reinforcement learning framework that redefines task scheduling as a sequence scoring problem to balance performance, reliability, cost, and network efficiency. By modeling both global GPU states and task requirements, REACH learns to adaptively co-locate computation with data, prioritize critical jobs, and mitigate the impact of unreliable resources. Extensive simulation results show that REACH improves task completion rates by up to 17%, more than doubles the success rate for high-priority tasks, and reduces bandwidth penalties by over 80% compared to state-of-the-art baselines. Stress tests further demonstrate its robustness to GPU churn and network congestion, while scalability experiments confirm its effectiveness in large-scale, high-contention scenarios.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2528-2542"},"PeriodicalIF":5.4,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223534","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

Decentralized Coalition Formation of Infrastructure Providers for Resource Provisioning in Coverage Constrained Virtualized Mobile Networks 覆盖受限虚拟化移动网络中资源供应基础设施提供商的分散联盟形成

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-10 DOI: 10.1109/TNSM.2026.3663437

Muhammad Fahimullah;Michel Kieffer;Sylvaine Kerboeuf;Shohreh Ahvar;Maria Trocan

{"title":"Decentralized Coalition Formation of Infrastructure Providers for Resource Provisioning in Coverage Constrained Virtualized Mobile Networks","authors":"Muhammad Fahimullah;Michel Kieffer;Sylvaine Kerboeuf;Shohreh Ahvar;Maria Trocan","doi":"10.1109/TNSM.2026.3663437","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3663437","url":null,"abstract":"The concept of wireless virtualized networks enables Mobile Virtual Network Operators (MVNOs) to utilize resources made available by multiple Infrastructure Providers (InPs) to set up a service. Nevertheless, existing centralized resource provisioning approaches fail to address such a scenario due to conflicting objectives among InPs and their reluctance to share private information. This paper addresses the problem of resource provisioning from several InPs for services with geographic coverage constraints. When complete information is available, an Integer Linear Program (ILP) formulation is provided, along with a greedy solution. An alternative coalition formation approach is then proposed to build coalitions of InPs that satisfy the constraints imposed by an MVNO, while requiring only limited information sharing. The proposed solution adopts a hedonic game-theoretic approach to coalition formation. For each InP, the decision to join or leave a coalition is made in a decentralized manner, relying on the satisfaction of service requirements and on individual profit. Simulation results demonstrate the applicability and performance of the proposed solution.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2899-2915"},"PeriodicalIF":5.4,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362419","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

Shannon Entropy for Load-Balanced Cellular Network Planning: Data-Driven Voronoi Optimization of Base-Station Locations 负载均衡蜂窝网络规划的香农熵：基站位置数据驱动的Voronoi优化

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-09 DOI: 10.1109/TNSM.2026.3663045

Mohammad Amir Dastgheib;Hamzeh Beyranvand;Jawad A. Salehi

{"title":"Shannon Entropy for Load-Balanced Cellular Network Planning: Data-Driven Voronoi Optimization of Base-Station Locations","authors":"Mohammad Amir Dastgheib;Hamzeh Beyranvand;Jawad A. Salehi","doi":"10.1109/TNSM.2026.3663045","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3663045","url":null,"abstract":"In this paper, we introduce a stochastic shape optimization technique for base-station placement in cellular wireless communication networks. We formulate the data-driven facility location problem in a gradient-based framework and propose an algorithm that computes stochastic gradients efficiently via nearest-neighbor evaluations on Voronoi diagrams. This enables the use of Shannon-entropy objectives that promote balanced coverage and yield more than two orders of magnitude reduction in per-iteration runtime compared to a conventional integral-based optimization that assumes full knowledge of the underlying density, making the proposed approach practical for real deployments. We highlight the requirements of facility location balancing problems with the introduction of the Adjusted Entropy Ratio and show a significant improvement in load balancing compared to the baseline algorithms, particularly in scenarios where baseline algorithms fall short in subdividing crowded areas for more equitable coverage. A downlink telecom evaluation with realistic propagation and interference models further shows that the proposed method configuration substantially improves user-rate fairness and load balance. Our results also show that Self-Organizing Maps (SOMs) provide an effective initialization by capturing the structure of the users’ location data.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2494-2506"},"PeriodicalIF":5.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223669","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

Hybrid CNN-LSTM Model for DDoS Detection and Mitigation in Software-Defined Networks 软件定义网络中DDoS检测与缓解的CNN-LSTM混合模型

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-09 DOI: 10.1109/TNSM.2026.3662819

Abdinasir Hirsi;Mohammed A. Alhartomi;Lukman Audah;Mustafa Maad Hamdi;Adeb Salah;Godwin Okon Ansa;Salman Ahmed;Abdullahi Farah

{"title":"Hybrid CNN-LSTM Model for DDoS Detection and Mitigation in Software-Defined Networks","authors":"Abdinasir Hirsi;Mohammed A. Alhartomi;Lukman Audah;Mustafa Maad Hamdi;Adeb Salah;Godwin Okon Ansa;Salman Ahmed;Abdullahi Farah","doi":"10.1109/TNSM.2026.3662819","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3662819","url":null,"abstract":"Software-Defined Networking (SDN) enhances programmability and control but remains highly vulnerable to distributed denial-of-service (DDoS) attacks. Existing solutions often adapt conventional methods without leveraging SDN’s native features or addressing real-time mitigation. This study introduces a novel hybrid deep learning framework for DDoS detection and mitigation in SDN, significantly advancing the state of the art. We develop a custom dataset in a Mininet–Ryu testbed that reflects realistic SDN traffic conditions, and employ a multistage feature selection pipeline to reduce redundancy and highlight the most discriminative flow attributes. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is then applied, capturing both spatial and temporal traffic patterns. The proposed system achieves 99.5% accuracy and a 97.7% F1-score, demonstrating a significant improvement over baseline ML and DL approaches. In addition, a lightweight and scalable mitigation module embedded in the SDN controller dynamically drops or reroutes malicious flows, enabling real-time, low-latency responsiveness. Experimental results across diverse topologies confirm the framework’s scalability and applicability in real-world SDN environments.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2507-2527"},"PeriodicalIF":5.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223532","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

A Semantic-Aware TSN Framework for Minimizing Age of Informative Data in Real-Time Industrial Monitoring Systems 实时工业监控系统中最小化信息数据年龄的语义感知TSN框架

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-02-06 DOI: 10.1109/TNSM.2026.3661050

Beom-Su Kim

{"title":"A Semantic-Aware TSN Framework for Minimizing Age of Informative Data in Real-Time Industrial Monitoring Systems","authors":"Beom-Su Kim","doi":"10.1109/TNSM.2026.3661050","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3661050","url":null,"abstract":"Real-time industrial monitoring systems rely on the timely delivery of semantically important data, such as anomaly-indicating sensor readings, to enable accurate and responsive decision-making. Credit-Based Shaping (CBS), a key mechanism in Time-Sensitive Networking (TSN), is well-suited for such systems due to its ability to dynamically manage aperiodic and bursty traffic without rigid transmission schedules. However, CBS was originally designed for Audio Video Bridging (AVB) traffic, which is periodic and less delay-sensitive, and thus lacks mechanisms to prioritize packets based on their semantic importance or freshness. As a result, critical updates may be delayed by the transmission of redundant or outdated packets. Motivated by these limitations, this paper presents an enhanced CBS mechanism, aiming to ensure the timeliness of semantically informative data in real-time industrial monitoring systems. Specifically, we encapsulate this enhancement within a semantic-aware TSN framework, which integrates three tightly coupled techniques: 1) semantic-based packet prioritization, 2) age-aware traffic shaping, and 3) age-aware packet forwarding and filtering. These mechanisms work in synergy to detect and expedite the transmission of high-priority, semantically meaningful packets, while suppressing redundant updates. Simulation results demonstrate that the proposed approach significantly improves anomaly detection responsiveness while maintaining efficient bandwidth utilization.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"2350-2366"},"PeriodicalIF":5.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175656","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