IEEE Transactions on Network Science and Engineering最新文献

Guest Editorial: Introduction to the Special Section on Research on Power Technology, Economy and Policy Towards Net-Zero Emissions 特邀社论：迈向净零排放的电力技术、经济和政策研究》特别章节导言

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-20 DOI: 10.1109/TNSE.2024.3478396

Junhua Zhao;Jing Qiu;Fushuan Wen;Junbo Zhao;Ciwei Gao;Yue Zhou

引用次数: 0

Guest Editorial: Introduction to the Special Section on Aerial Computing Networks in 6G 特邀编辑：6G 空中计算网络专题介绍

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-20 DOI: 10.1109/TNSE.2024.3483408

Yang Yang;Chen Chen;Rose Qingyang Hu;Schahram Dustdar;Qingqi Pei

引用次数: 0

Temporal Link Prediction via Auxiliary Graph Transformer 通过辅助图变换器进行时态链接预测

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.2024.3485093

Tao Tan;Xianbin Cao;Fansheng Song;Shenwen Chen;Wenbo Du;Yumeng Li

{"title":"Temporal Link Prediction via Auxiliary Graph Transformer","authors":"Tao Tan;Xianbin Cao;Fansheng Song;Shenwen Chen;Wenbo Du;Yumeng Li","doi":"10.1109/TNSE.2024.3485093","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3485093","url":null,"abstract":"Temporal link prediction is fundamental for analyzing and predicting the behavior of real evolving complex systems. Recently, advances in graph learning for temporal network snapshots present a promising approach for predicting the evolving topology. However, previous methods only considered temporal-structural encoding of the entire network, which leads to the overshadowing of crucial evolutionary characteristics by massive invariant network structural information. In this paper, we delve into the evolving topology and propose an auxiliary learning framework to capture not only the overall network evolution patterns but also the time-varying regularity of the evolved edges. Specifically, we utilize a graph transformer to infer temporal networks, incorporating a temporal cross-attention mechanism to refine the dynamic graph representation. Simultaneously, a dynamic difference transformer is designed to infer the evolved edges, serving as an auxiliary task and being aggregated with graph representation to generate the final predicted result. Extensive experiments are conducted on eight real-world temporal networks from various scenarios. The results indicate that our auxiliary learning framework outperforms the baselines, demonstrating the superiority of the proposed method in extracting evolution patterns.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5954-5968"},"PeriodicalIF":6.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694647","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

Mixup in Latent Geometry for Graph Classification 图形分类中的潜在几何混合

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-21 DOI: 10.1109/TNSE.2024.3482188

Zijia Liu;Xiaolei Ru;Jack Murdoch Moore;Xin-Ya Zhang;Gang Yan

{"title":"Mixup in Latent Geometry for Graph Classification","authors":"Zijia Liu;Xiaolei Ru;Jack Murdoch Moore;Xin-Ya Zhang;Gang Yan","doi":"10.1109/TNSE.2024.3482188","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3482188","url":null,"abstract":"Mixup is a data augmentation method which can interpolate between existing data to create new samples. By enlarging the training distribution, it reduces the risk of over-fitting and improves generalization. Mixup is relatively straightforward to apply to image samples because pixels with equivalent coordinates in different images can be associated. However, alignment of distinct graphs with different sizes is non-trivial, thereby hindering the application of Mixup to graph data. Here we develop a novel algorithm to address this issue by exploiting the latent hyperbolic geometry which has been shown to underlie many real-world graphs. By considering global graph structure similarity and several fundamental structural features of graph models, we demonstrate that our mixup scheme leads to synthetic graphs whose structural features approximate the linear interpolation of parent graphs, a property important for avoiding the generation of mislabeled synthetic data. We apply the proposed algorithm to classify empirical graphs, and the results show that it improves classification performance on all six benchmark datasets and significantly enhances the generalization ability and robustness of graph neural networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5943-5953"},"PeriodicalIF":6.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10723746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694668","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

Load Balancing With Traffic Splitting for QoS Enhancement in 5G HetNets 在 5G HetNets 中利用流量分流实现负载平衡以增强 QoS

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-21 DOI: 10.1109/TNSE.2024.3482365

Abdul Manan;Syed Maaz Shahid;SungKyung Kim;Sungoh Kwon

{"title":"Load Balancing With Traffic Splitting for QoS Enhancement in 5G HetNets","authors":"Abdul Manan;Syed Maaz Shahid;SungKyung Kim;Sungoh Kwon","doi":"10.1109/TNSE.2024.3482365","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3482365","url":null,"abstract":"In heterogeneous networks (HetNets), high user density and random small cell deployment often result in uneven User Equipment (UE) distributions among cells. This can lead to excessive resource usage in some cells and a degradation of Quality of Service (QoS) for users, even while resources in other cells remain underutilized. To address this challenge, we propose a load-balancing algorithm for 5G HetNets that employs traffic splitting for dual connectivity (DC) users. By enabling traffic splitting, DC allows UEs to receive data from both macro and small cells, thereby enhancing network performance in terms of load balancing and QoS improvement. To prevent cell overloading, we formulate the problem of minimizing load variance across 5G HetNet cells using traffic splitting. We derive a theoretical expression to determine the optimal split ratio by considering the cell load conditions. The proposed algorithm dynamically adjusts the data traffic split for DC users based on the optimal split ratio and, if necessary, offloads edge users from overloaded macro cells to underloaded macro cells to achieve uniform network load distribution. Simulation results demonstrate that the proposed algorithm achieves more even load distribution than other load balancing algorithms and increases network throughput and the number of QoS-satisfied users.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6272-6284"},"PeriodicalIF":6.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679301","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

Hypergraph-Based Model for Modeling Multi-Agent Q-Learning Dynamics in Public Goods Games 基于超图的公共物品游戏中多代理 Q 学习动态建模模型

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-17 DOI: 10.1109/TNSE.2024.3473941

Juan Shi;Chen Liu;Jinzhuo Liu

{"title":"Hypergraph-Based Model for Modeling Multi-Agent Q-Learning Dynamics in Public Goods Games","authors":"Juan Shi;Chen Liu;Jinzhuo Liu","doi":"10.1109/TNSE.2024.3473941","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3473941","url":null,"abstract":"Modeling the learning dynamic of multi-agent systems has long been a crucial issue for understanding the emergence of collective behavior. In public goods games, agents interact in multiple larger groups. While previous studies have primarily focused on infinite populations that only allow pairwise interactions, we aim to investigate the learning dynamics of agents in a public goods game with higher-order interactions. With a novel use of hypergraphs for encoding higher-order interactions, we develop a formal model (a Fokker-Planck equation) to describe the temporal evolution of the distribution function of Q-values. Noting that early research focused on replicator models to predict system dynamics failed to accurately capture the impact of hyperdegree in hypergraphs, our model effectively maps its influence. Through experiments, we demonstrate that our theoretical findings are consistent with the agent-based simulation results. We demonstrated that as the number of groups an agent is involved in reaches a certain scale, the learning dynamics of the system evolve to resemble those of a well-mixed population. Furthermore, we demonstrate that our model offers insights into algorithmic parameters, such as the Boltzmann temperature, facilitating parameter tuning.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6169-6179"},"PeriodicalIF":6.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679329","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

V2IViewer: Towards Efficient Collaborative Perception via Point Cloud Data Fusion and Vehicle-to-Infrastructure Communications V2IViewer：通过点云数据融合和车对基础设施通信实现高效协同感知

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-16 DOI: 10.1109/TNSE.2024.3479770

Sheng Yi;Hao Zhang;Kai Liu

{"title":"V2IViewer: Towards Efficient Collaborative Perception via Point Cloud Data Fusion and Vehicle-to-Infrastructure Communications","authors":"Sheng Yi;Hao Zhang;Kai Liu","doi":"10.1109/TNSE.2024.3479770","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3479770","url":null,"abstract":"Collaborative perception (CP) with vehicle-to-infrastructure (V2I) communications is a critical scenario in high-level autonomous driving. This paper presents a novel framework called V2IViewer to facilitate collaborative perception, which consists of three modules: object detection and tracking, data transmission, and object alignment. On this basis, we design a heterogeneous multi-agent middle layer (HMML) as the backbone to extract feature representations, and utilize a Kalman filter (KF) with the Hungarian algorithm for object tracking. For transmitting object information from infrastructure to ego-vehicle, Protobuf is utilized for data serialization using binary encoding, which reduces communication overheads. For object alignment from multiple agents, a Spatiotemporal Asynchronous Fusion (SAF) method is proposed, which uses a Multilayer Perceptron (MLP) for generating post-synchronization object sequences. These sequences are then utilized for fusion to enhance the accuracy of the integration. Experimental validation on DAIR-V2X-C, V2X-Seq, and V2XSet datasets shows that V2IViewer enhances long-range object detection accuracy by an average of 12.9% over state-of-the-art collaborative methods. Moreover, V2IViewer demonstrates an average improvement in accuracy of 3.3% across various noise conditions compared to existing models. Finally, the system prototype is implemented and the performance has been validated in realistic environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6219-6230"},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679300","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

GFlow: GNN-Based Optimal Flow Scheduling for Multipath Transmission With Link Overlapping GFlow：基于 GNN 的链路重叠多径传输优化流量调度

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-16 DOI: 10.1109/TNSE.2024.3481413

Du Chen;Weiting Zhang;Deyun Gao;Dong Yang;Hongke Zhang

{"title":"GFlow: GNN-Based Optimal Flow Scheduling for Multipath Transmission With Link Overlapping","authors":"Du Chen;Weiting Zhang;Deyun Gao;Dong Yang;Hongke Zhang","doi":"10.1109/TNSE.2024.3481413","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3481413","url":null,"abstract":"Multipath TCP (MPTCP) is considered as a solution capable of addressing the growing demand for bandwidth. However, the existing MPTCP mechanisms make flow scheduling based on coarse-grained end-to-end network states, which prevents MPTCP from better aggregating the bandwidth of multiple paths. Besides, link overlapping may occur between different MPTCP connections, which results in multiple subflows competing for bandwidth of the shared link. In this paper, we propose GFlow, a Graph Neural Network (GNN) based Deep Reinforcement Learning (DRL) algorithm, to make optimal flow scheduling for multipath transmission with link overlapping. Specifically, we formulate the flow scheduling problem as a problem of maximizing overall throughput by taking both bottleneck bandwidth and shared bandwidth into consideration. To support accurate network state perception, GFlow utilizes In-band Network Telemetry (INT) to collect real-time and fine-grained network states. Taking these states as input, the DRL agent with GNN integrated fully learns the relationships among links, paths (subflows), and MPTCP connections. In this way, GFlow is able to make optimal flow scheduling decisions according to the network states. We build a P4-based multipath transmission system and carry out extensive experiments to evaluate the performance of GFlow. The results show that GFlow outperforms the baseline multipath transmission mechanism in both homogeneous scenario and heterogeneous scenario, improving the average overallthroughput while reducing the average round trip time (RTT).","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6244-6258"},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679385","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

Joint Data Allocation and LSTM-Based Server Selection With Parallelized Federated Learning in LEO Satellite IoT Networks 低地轨道卫星物联网网络中基于并行化联合学习的联合数据分配和 LSTM 服务器选择

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-16 DOI: 10.1109/TNSE.2024.3481630

Pengxiang Qin;Dongyang Xu;Lei Liu;Mianxiong Dong;Shahid Mumtaz;Mohsen Guizani

{"title":"Joint Data Allocation and LSTM-Based Server Selection With Parallelized Federated Learning in LEO Satellite IoT Networks","authors":"Pengxiang Qin;Dongyang Xu;Lei Liu;Mianxiong Dong;Shahid Mumtaz;Mohsen Guizani","doi":"10.1109/TNSE.2024.3481630","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3481630","url":null,"abstract":"Low earth orbit (LEO) satellite networks have emerged as a promising field for distributed Internet of Things (IoT) devices, particularly in latency-tolerant applications. Federated learning (FL) is implemented in LEO satellite IoT networks to preserve data privacy and facilitate machine learning (ML). However, the user who spends the longest time significantly hampers FL efficiency and degrades the Quality-of-Service (QoS), potentially leading to irreparable damage. To address this challenge, we propose a joint data allocation and server selection strategy based on long short-term memory (LSTM) with parallelized FL in LEO satellite IoT networks. Herein, data-parallel learning is utilized, allowing multiple users to collaboratively train ML networks to minimize latency. Moreover, server selection takes into account signal propagation delays as well as traffic loads forecasted by an LSTM network, thereby improving the efficiency even further. Specifically, the strategies are formulated as optimization problems and tackled using a line search sequential quadratic programming (SQP) method and a multiple-objective particle swarm optimization (MOPSO) algorithm. Simulation results show the effectiveness of the proposed strategy in reducing total latency and enhancing the efficiency of FL in LEO satellite IoT networks compared to the alternatives.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6259-6271"},"PeriodicalIF":6.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679303","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

NHCE: A Neural High-Order Causal Entropy Algorithm for Disentangling Coupling Dynamics NHCE：析构耦合动力学的神经高阶因果熵算法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-10-15 DOI: 10.1109/TNSE.2024.3480710

Yanyan He;Mingyu Kang;Duxin Chen;Wenwu Yu

引用次数: 0