IEEE Transactions on Network Science and Engineering最新文献_第9页

Joint Resource Allocation and 3D-Position Optimization for UAV-Assisted MEC Network With NOMA 基于NOMA的无人机辅助MEC网络联合资源分配与三维位置优化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-14 DOI: 10.1109/TNSE.2025.3529200

Xiangbin Yu;Xinyi Zhang;Yun Rui;Xiaoyu Dang;Guoqing Jia;Mohsen Guizani

{"title":"Joint Resource Allocation and 3D-Position Optimization for UAV-Assisted MEC Network With NOMA","authors":"Xiangbin Yu;Xinyi Zhang;Yun Rui;Xiaoyu Dang;Guoqing Jia;Mohsen Guizani","doi":"10.1109/TNSE.2025.3529200","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3529200","url":null,"abstract":"In this article, the computation efficiency (CE) optimization of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) is addressed in the presence of imperfect successive interference cancelation. Specifically, joint design schemes of resource allocation (RA) and three-dimensional (3D) position are developed to improve the CE while ensuring the fairness of groundusers. In particular, we apply the max-min fairness criterion and optimize the beamforming (BF), power allocation (PA), local CPU frequency and UAV position jointly via two-step optimization method. Namely, we first optimize the 3D position by using an efficient iteration algorithm based on the alternating optimization and concave-convex procedure methods. Then, the joint design of BF, PA and CPU frequency is solved by an efficient iteration algorithm based on the block coordinate descent, sub-gradient methods and convex optimization tool. Additionally, a lower-complexity suboptimal PA scheme with closed-form expression for each iteration is developed. Simulation results indicate that the proposed two design schemes of joint RA and position are effective.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"1440-1456"},"PeriodicalIF":6.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870961","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

Strategic Storage Investment and Operation Under Uncertainty: Behavioral Economics Analysis 不确定性条件下的战略性仓储投资与运营：行为经济学分析

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-13 DOI: 10.1109/TNSE.2025.3528554

Qisheng Huang;Jin Xu;Peng Sun;Bo Liu;Ting Wu;Costas Courcoubetis

{"title":"Strategic Storage Investment and Operation Under Uncertainty: Behavioral Economics Analysis","authors":"Qisheng Huang;Jin Xu;Peng Sun;Bo Liu;Ting Wu;Costas Courcoubetis","doi":"10.1109/TNSE.2025.3528554","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3528554","url":null,"abstract":"In this paper, we propose a two-stage Stackelberg game to investigate the strategic storage investment and operation interaction between the storage aggregator and consumers under demand uncertainty. In the first stage, the storage aggregator makes the storage investment and pricing decisions to maximize its profit. After observing the storage aggregator's decisions, each consumer makes its own storage operation decisions to minimize its electricity bill. Different from previous studies that mainly assumed a risk-neutral consumer based on the expected utility theory (EUT), we propose a prospect theory (PT) model to capture consumers' risk preferences. To solve the PT-based non-convex problem, we exploit the unimodal structure of the objective function and characterize the equilibrium solutions. Theoretical and numerical results show that the consumers' risk preferences have significant impacts on the equilibrium solutions: 1) a PT-consumer with a low reference point is more willing to use energy storage to reduce risk compared with the EUT benchmark; 2) a PT consumer is more willing to use the energy storage when the probability of high demand is small, due to the probability distortion; 3) the consumers with a lower level of risk preference are easier to be affected by the increase of storage investment cost.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1329-1342"},"PeriodicalIF":6.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471813","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

AI-Based E2E Resilient and Proactive Resource Management in Slice-Enabled 6G Networks 基于人工智能的片式 6G 网络 E2E 弹性和主动资源管理

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-10 DOI: 10.1109/TNSE.2025.3528190

Ali Nouruzi;Nader Mokari;Paeiz Azmi;Eduard A. Jorswieck;Melike Erol-Kantarci

{"title":"AI-Based E2E Resilient and Proactive Resource Management in Slice-Enabled 6G Networks","authors":"Ali Nouruzi;Nader Mokari;Paeiz Azmi;Eduard A. Jorswieck;Melike Erol-Kantarci","doi":"10.1109/TNSE.2025.3528190","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3528190","url":null,"abstract":"Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in network slicing (NetS) technology. This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS. Since guaranteeing quality of service (QoS) in NetS has many challenges, we present an intelligent method that has two characteristics: resilience and proactivity. Guaranteeing successful slice provision is costly, we formulate a comprehensive model of the imposed costs. To minimize the cost function, we introduce a new optimization problem with radio, processing, and transmission resource constraints. In addition, we introduce two new constraints that guarantee the proactivity and resilience capabilities of the network based on the probability of successful slice provisioning (PSSP). Since the proposed optimization problem is non-convex, online and belongs to the NP-hard category, we adopt a deep reinforcement learning (DRL) based method to solve it. In particular, the soft actor critic (SAC) method is utilized due to its robustness in uncertain environment that the obtained results reveal that the applied method can improve the percentage of successful slice provisioned (PrSSP). In addition, the resiliency time is reduced comparatively. Finally, as the main achievement, the resilient scenario improves PrSSP compared to the non-resilient scenario.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1311-1328"},"PeriodicalIF":6.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471812","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

Meta-Learning Enhanced Physics-Informed Graph Attention Convolutional Network for Distribution Power System State Estimation

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-10 DOI: 10.1109/TNSE.2025.3525625

Huayi Wu;Zhao Xu;Minghao Wang;Xue Lyu

{"title":"Meta-Learning Enhanced Physics-Informed Graph Attention Convolutional Network for Distribution Power System State Estimation","authors":"Huayi Wu;Zhao Xu;Minghao Wang;Xue Lyu","doi":"10.1109/TNSE.2025.3525625","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3525625","url":null,"abstract":"Promptly perceiving distribution system states is challenged by frequent topology changes and uncertain power injections. To address these issues, a Meta-learning enhanced physics-informed graph attention convolutional network (Meta-PIGACN) model is proposed to handle topological variability in distribution system state estimation (DSSE). Specifically, physics information is integrated into the graph convolutional network, enabling a physics-informed edge-weighting process that incorporates physical information to control the aggregation of neighboring nodes. Besides, the graph attention mechanism automatically adjusts the importance of different neighboring nodes, allowing the capture and preservation of inherent system features across varying topologies, thereby improving state estimation accuracy. Furthermore, meta-learning is proposed to acquire empirical knowledge across multiple topologies so that the model can rapidly adapt to new configurations through iterative gradient descent updates even in large-scale systems. The simulation results based on the 33/118/1746-node distribution systems show the high accuracy and efficiency of the proposed model.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1186-1197"},"PeriodicalIF":6.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465581","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

Networks for Flight Delay Analysis: A Scoping Review and Research Agenda 航班延误分析网络：范围审查和研究议程

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-10 DOI: 10.1109/TNSE.2025.3526850

Sebastian Wandelt;Xinyue Chen;Xiaoqian Sun

引用次数: 0

Graph Deep Reinforcement Learning for Multi-Cycle Queuing and Scheduling in Deterministic Networking

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-10 DOI: 10.1109/TNSE.2025.3528043

Yelin Huang;Weiqiang Xu;Yueyue Dai;Sabita Maharjan;Yan Zhang

{"title":"Graph Deep Reinforcement Learning for Multi-Cycle Queuing and Scheduling in Deterministic Networking","authors":"Yelin Huang;Weiqiang Xu;Yueyue Dai;Sabita Maharjan;Yan Zhang","doi":"10.1109/TNSE.2025.3528043","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3528043","url":null,"abstract":"Deterministic networking (DetNet) offers guaranteed transmission services for critical real-time applications, such as industrial automation and intelligent transport systems. It is challenging to fully utilise link resources of different rates to achieve deterministic scheduling in real-world network scenarios with heterogeneous link rates (e.g., hierarchical networks). The cycle specified queuing and forwarding (CSQF) is a typical approach to provide deterministic end-to-end delay in DetNet. However, to achieve deterministic scheduling, the CSQF mechanism sets the same cycle length for both high-speed links and low speed links, resulting in a significant waste of high-speed link resources. To address this issue, we propose a multi-cycle CSQF (MCCSQF) mechanism for multi-link rate networks to reduce queuing delay during high-speed link scheduling and consequently leading to a lower end-to-end flow latency. Furthermore, to fully exploit the exploration and decision-making capabilities of deep reinforcement learning (DRL) in complex environments, we design a DRL framework to achieve deterministic flow routing and low-latency scheduling in MCCSQF. However, DRL algorithms are not capable of fully utilizing network topology information for decision making. We, therefore introduce a graph DRL (GDRL) algorithm- incorporating graph convolution into DRL to extract topological spatial features of network links. Our numerical evaluation results from various network scenarios with different topologies and multiple link rates demonstrate that our proposed GDRL outperforms DRL in flow scheduling while MCCSQF effectively reduces the end-to-end delay compared to CSQF.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1297-1310"},"PeriodicalIF":6.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471796","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

Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-09 DOI: 10.1109/TNSE.2025.3526206

Xinliang Dai;Junyi Zhai;Yuning Jiang;Yi Guo;Colin N. Jones;Veit Hagenmeyer

{"title":"Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems","authors":"Xinliang Dai;Junyi Zhai;Yuning Jiang;Yi Guo;Colin N. Jones;Veit Hagenmeyer","doi":"10.1109/TNSE.2025.3526206","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3526206","url":null,"abstract":"This paper introduces a distributed operational solution for integrated transmission-distribution (ITD) system management. A fundamental challenge in designing distributed approaches for AC optimal power flow (OPF) problems in ITD systems is the nonconvexity and nonlinearity of the optimization problems for both transmission and distribution systems. To tackle the challenges, our research introduces an enhanced version of the Augmented Lagrangian based Alternating Direction Inexact Newton method (<sc>aladin</small>), which incorporates a second-order correction strategy and convexification. The former improves the algorithm's ability to follow curved trajectories effectively with minimal additional computational demand, while the latter simplifies the decoupled subproblems without introducing the combinatory complexity typically associated with additional inequality constraints. The theoretical studies demonstrate that the proposed distributed algorithm operates the ITD systems with a local quadratic convergence guarantee. Extensive simulations on various ITD configurations highlight the superior performance of our distributed approach in terms of convergence speed, computational efficiency, scalability, and adaptability.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1210-1223"},"PeriodicalIF":6.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471696","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

Intra-QLAN Connectivity via Graph States: Beyond the Physical Topology

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-09 DOI: 10.1109/TNSE.2024.3520856

Francesco Mazza;Marcello Caleffi;Angela Sara Cacciapuoti

{"title":"Intra-QLAN Connectivity via Graph States: Beyond the Physical Topology","authors":"Francesco Mazza;Marcello Caleffi;Angela Sara Cacciapuoti","doi":"10.1109/TNSE.2024.3520856","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3520856","url":null,"abstract":"In the near to mid future, Quantum Local Area Networks (QLANs) – the fundamental building block of the Quantum Internet – will unlike exhibit physical topologies characterized by densely physical connections among the nodes. On the contrary, it is pragmatic to consider QLANs based on simpler, scarcely-connected physical topologies, such as star topologies. This constraint, if not properly tackled, will significantly impact the QLAN performance in terms of communication delay and/or overhead. Thankfully, it is possible to create on-demand links between QLAN nodes, without physically deploying them, by properly manipulating a shared multipartite entangled state, namely, a graph state. Thus, it is possible to build an overlay topology, referred to as <italic>artificial topology</i>, upon the physical one, by only performing Local Operations and Classical Communication (LOCC). In this paper, we address the fundamental issue of engineering the artificial topology of a QLAN to bypass the limitations induced by the physical topology. The designed framework relays only on local operations, without exchanging signaling among the client nodes of the QLAN, which, in turn, would introduce further delays in a scenario very sensitive to the decoherence. Finally, by exploiting the artificial topology, it is proved that the troubleshooting is simplified, by overcoming the single point of failure, typical of classical LAN star topologies.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"870-887"},"PeriodicalIF":6.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10835135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465654","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

IPv6Landmarker: Enhancing IPv6 Street-Level Geolocation Through Network Landmark Mining and Targeted Updates IPv6Landmarker：通过网络地标挖掘和定向更新增强 IPv6 街道级地理定位功能

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-08 DOI: 10.1109/TNSE.2025.3527563

Ruosi Cheng;Shichang Ding;Liancheng Zhang;Ruixiang Li;Shaoyong Du;Xiangyang Luo

{"title":"IPv6Landmarker: Enhancing IPv6 Street-Level Geolocation Through Network Landmark Mining and Targeted Updates","authors":"Ruosi Cheng;Shichang Ding;Liancheng Zhang;Ruixiang Li;Shaoyong Du;Xiangyang Luo","doi":"10.1109/TNSE.2025.3527563","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3527563","url":null,"abstract":"IP geolocation accuracy heavily relies on the availability of numerous high-quality network landmarks. However, IPv6 geolocation faces challenges due to its vast address space and rotating prefixes. Existing landmark mining methods struggle to meet the stringent demands of IPv6 street-level geolocation. We introduce IPv6Landmarker, a novel approach that enhances IPv6 geolocation precision through landmark mining and targeted updates. By associating WAN IPv6 addresses with WiFi BSSIDs in wireless routers, we employ a multi-association coordinate filtering algorithm to select reliable IPv6 street-level landmarks. We also implement targeted updates based on IPv6 prefix rotation patterns. Using real-world data, we demonstrate significant improvements, including a range increase of 16.75% to 46.68% in candidate landmarks acquired globally and of 10.06% to 126.39% in landmarks acquired specifically within target cities. In particular, there is a range of 16.67% to 66.67% enhancement in the geolocation success of ground truth landmarks, coupled with a range of 6.09% to 40.34% reduction in geolocation error. Additionally, it shows a remarkable 82.36% improvement in landmark set stability.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1280-1296"},"PeriodicalIF":6.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471795","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

Local Optimization Policy for Link Prediction via Reinforcement Learning 通过强化学习进行链路预测的局部优化策略

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-07 DOI: 10.1109/TNSE.2025.3526340

Mingshuo Nie;Dongming Chen;Dongqi Wang;Huilin Chen

{"title":"Local Optimization Policy for Link Prediction via Reinforcement Learning","authors":"Mingshuo Nie;Dongming Chen;Dongqi Wang;Huilin Chen","doi":"10.1109/TNSE.2025.3526340","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3526340","url":null,"abstract":"Link prediction effectively recovers missing and undiscovered link structures in a graph, enhancing researchers' ability to comprehend the generation mechanisms and evolutionary processes of the graph. Graph Neural Networks (GNNs) address link prediction tasks by aggregating complex structures and features within a specified scope. However, determining the optimal aggregation scopes for nodes in different graph-structured data poses challenges in terms of complexity and time consumption. Handcrafted or expert-based aggregation scopes require significant computational resources and involve high complexity. To address these challenges, in this paper, we propose exploring diverse information aggregation scopes for individual nodes to enhance the performance of GNNs. We introduce the Local Optimization Policy (LOP) to jointly learn the creation of the GNNs and the link prediction task. LOP adaptively learns the aggregation scope of each node through deep reinforcement learning and utilizes the learned aggregation scopes to construct the GNNs. Furthermore, we introduce the virtual node and edge features to enhance the performance of link prediction. Experimental results on multiple datasets demonstrate the superior performance of LOP compared to baselines, providing evidence for the feasibility, effectiveness, and reliability of combining GNNs and deep reinforcement learning.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"1224-1236"},"PeriodicalIF":6.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471815","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