IEEE Transactions on Network Science and Engineering最新文献_第7页

A Secure Authentication Scheme for Satellite-Terrestrial Networks 卫星-地面网络安全认证方案

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-19 DOI: 10.1109/TNSE.2024.3445712

Shanyao Ren;Jianwei Liu;Ruihang Ji;Shuzhi Sam Ge;Dongyu Li

{"title":"A Secure Authentication Scheme for Satellite-Terrestrial Networks","authors":"Shanyao Ren;Jianwei Liu;Ruihang Ji;Shuzhi Sam Ge;Dongyu Li","doi":"10.1109/TNSE.2024.3445712","DOIUrl":"10.1109/TNSE.2024.3445712","url":null,"abstract":"The low earth orbit (LEO) satellite constellations, with their advantages of low latency and high maneuverability, are widely applied in various systems and are highly suitable for communication systems. However, there are still numerous security vulnerabilities present in the communication between LEO satellites and the ground. In the recent literature, there are elegant studies conducted on authentication protocols for LEO satellite networks. Inspired by the previous works, we present a secure authentication and key management protocol for the LEO-terrestrial networks by elliptic curve cryptography (ECC). Our scheme optimizes the shortcomings of previous work, significantly alleviates the computational burden on both users and servers sides, and provides a novel solution for session key updates. The verification of the security for the interaction involves the application of both informal and formal analysis approaches. Through the comprehensive analysis and simulation, it is demonstrated that the proposed work satisfies the perfect forward security, resists all known attacks, and reduces the execution time and communication cost. Additionally, our work exhibits versatility in its applicability to a diverse range of industrial scenarios with its practical significance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6470-6482"},"PeriodicalIF":6.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191797","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

Fuzzy Distance Jaya Algorithm Based Node Localization in Anisotropic Wireless Sensor Networks 各向异性无线传感器网络中基于模糊距离贾亚算法的节点定位

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-16 DOI: 10.1109/TNSE.2024.3444589

Shilpi;Arvind Kumar

{"title":"Fuzzy Distance Jaya Algorithm Based Node Localization in Anisotropic Wireless Sensor Networks","authors":"Shilpi;Arvind Kumar","doi":"10.1109/TNSE.2024.3444589","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3444589","url":null,"abstract":"Many applications of Wireless Sensor Networks (WSNs) depend on location information. Every WSN has anchor nodes or known location-based nodes and target or unknown nodes. Due to several anisotropic factors, solving the node localization problem in Anisotropic WSNs (AWSNs) is more challenging. This work solves the node localization issue in AWSNs using soft-computing approaches. Distance is estimated using a fuzzy logic model to avoid irregularities in anchor nodes' Received Signal Strength Indicator (RSSI) value. The Mamdani Fuzzy Inference System (FIS) employs a triangular membership function to optimize the distance between the anchor and target nodes. The simplicity of the Jaya algorithm inspires us to use it to find the target node location coordinates in AWSNs. The performance of the proposed algorithm is measured in terms of localization error and computation time through simulation analysis on MATLAB software with the fuzzy logic toolbox. The localization error is calculated for different node densities, anchor nodes, and Degree of Irregularity (\u0000<inline-formula><tex-math>$doi$</tex-math></inline-formula>\u0000) values. The proposed algorithm compares the performance metrics with existing localization algorithms for AWSNs and provides better location estimation.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6345-6355"},"PeriodicalIF":6.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679246","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

GTCC: A Game Theoretic Approach for Efficient Congestion Control in Datacenter Networks GTCC：数据中心网络高效拥塞控制的博弈论方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-16 DOI: 10.1109/TNSE.2024.3443099

Likai Liu;Fu Xiao;Lei Han;Weibei Fan;Xin He

{"title":"GTCC: A Game Theoretic Approach for Efficient Congestion Control in Datacenter Networks","authors":"Likai Liu;Fu Xiao;Lei Han;Weibei Fan;Xin He","doi":"10.1109/TNSE.2024.3443099","DOIUrl":"10.1109/TNSE.2024.3443099","url":null,"abstract":"Utilization of Remote Direct Memory Access (RDMA) can offer higher bandwidth, lower latency, and reduced CPU overhead compared to traditional TCP. However, existing feedback-based RDMA congestion control schemes are not effective in addressing the problem of sudden queue accumulation and insufficient bandwidth utilization caused by frequent traffic bursts. In this paper, we propose GTCC, a game theoretic approach for efficient congestion control in RDMA data center networks. This approach enables the transmission rates between distributed senders to approach approximate coordination, thereby reducing the likelihood of network congestion. Firstly, we design a mechanism based on a non-cooperative game model and apply it to data center congestion control. Secondly, considering the limitations of simply introducing a non-cooperative game model, we optimize the game-theoretic approach to better suit data center characteristics. Finally, with the optimized game-theoretic approach, we implement the GTCC congestion control mechanism, improving network metrics in a simple, efficient, and viable manner. We evaluate GTCC using large-scale NS3 simulations. Compared to the standalone deployment of HPCC, GTCC integrated with HPCC shortens Flow Completion Time (FCT) for short flows, with the tail FCT reduced by up to approximately 0.7% to 8.6% in our experiments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6328-6344"},"PeriodicalIF":6.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191799","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

Adaptive QoE-Aware SFC Orchestration in UAV Networks: A Deep Reinforcement Learning Approach 无人机网络中的自适应 QoE 感知 SFC 协调：深度强化学习方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-16 DOI: 10.1109/TNSE.2024.3442857

Yao Wu;Ziye Jia;Qihui Wu;Zhuo Lu

{"title":"Adaptive QoE-Aware SFC Orchestration in UAV Networks: A Deep Reinforcement Learning Approach","authors":"Yao Wu;Ziye Jia;Qihui Wu;Zhuo Lu","doi":"10.1109/TNSE.2024.3442857","DOIUrl":"10.1109/TNSE.2024.3442857","url":null,"abstract":"In the low altitude intelligent network (LAIN), unmanned aerial vehicles (UAVs) are extensively utilized to provide flexible communication and data transmission services. Besides, based on the network function virtualization technology, the service function chain (SFC) orchestration is an effective solution for optimizing communication performance and network adaptability in UAV networks. Hence, this paper investigates an adaptive SFC orchestration scheme for UAV networks in LAIN by defining and managing service pathways. Firstly, to quantify the quality of experience (QoE) of users, we employ the fuzzy analytic hierarchy process to construct a mathematical model to elucidate the relationship between the quality of service and QoE. Subsequently, we introduce the markov decision process model to capture the dynamic network state transitions, and then devise an algorithm of dueling double deep Q-network with regularization for adaptive online SFC deployment. Finally, we investigate the adaptability of deep reinforcement learning algorithms to resource constraints within the UAV network scenarios. Numerical results indicate that compared with the baseline algorithms, the proposed algorithm can enhance training stability, ensure the QoE of users, and optimize key indicators such as energy consumption and task completion.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6052-6065"},"PeriodicalIF":6.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191798","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

HAT: Task Offloading and Resource Allocation in RIS-Assisted Collaborative Edge Computing HAT：RIS 辅助协作边缘计算中的任务卸载和资源分配

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-15 DOI: 10.1109/TNSE.2024.3432893

Lin Tan;Songtao Guo;Pengzhan Zhou;Zhufang Kuang;Xianlong Jiao

{"title":"HAT: Task Offloading and Resource Allocation in RIS-Assisted Collaborative Edge Computing","authors":"Lin Tan;Songtao Guo;Pengzhan Zhou;Zhufang Kuang;Xianlong Jiao","doi":"10.1109/TNSE.2024.3432893","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3432893","url":null,"abstract":"The problem of joint offloading decisions, resource allocation, and Reconfigurable Intelligent Surface (RIS) beamforming matrices for RIS-Assisted Edge Computing is a challenging issue. In this paper, user tasks can be either executed locally, or offloaded to a collaborative device or edge server with the assistance of the RIS, where RIS elements are grouped and assigned to all users to enable parallel services. The objective is formulated as a mixed integer nonlinear programming (MINLP) problem, where collaborative offloading decisions, RIS beamforming matrices, transmission power allocation, and computation resource allocation are jointly optimized to minimize the energy consumption. To address this problem, we propose a discrete-continuous Hybrid Action adapted Twin Delayed Deep Deterministic policy gradient (TD3) algorithm based on Deep Reinforcement Learning, named HAT. HAT constructs a latent representation space for the original discrete-continuous hybrid actions, fully considering the relations among highly coupled hybrid optimization variables. Experimental results demonstrate that HAT achieves significant performance gains over existing work (e.g., MELO, DDPG, PADDPG) and other benchmark schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"4665-4678"},"PeriodicalIF":6.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013207","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 Gradient Method for Neural Network-Based Constrained $k$-Winners-Take-All 基于神经网络的分布式梯度法（Distributed Gradient Method for Neural Network-based Constrained $k$-win-win-take-all

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-15 DOI: 10.1109/TNSE.2024.3443864

Xiasheng Shi;Yanxu Su;Chaoxu Mu;Changyin Sun

{"title":"Distributed Gradient Method for Neural Network-Based Constrained $k$-Winners-Take-All","authors":"Xiasheng Shi;Yanxu Su;Chaoxu Mu;Changyin Sun","doi":"10.1109/TNSE.2024.3443864","DOIUrl":"10.1109/TNSE.2024.3443864","url":null,"abstract":"Thispaper studies the neural network-based distributed constrained \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-winners-take-all (\u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000WTA) problem, which aims to select \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000 largest inputs from amount of inputs under two types of global coupled constraints. Namely, equality and inequality constrained \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000WTA problems. By selecting the proper parameter, the two constrained \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000WTA problems can be transformed into two continuous constrained quadratic programming problems. Subsequently, we propose a derivative feedback-based modified primal-dual fully distributed algorithm for the \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000WTA problem with a global coupled equality constraint by utilizing Karush-Kuhn-Tucker (KKT) conditions and the gradient flow method. In addition, the developed derivative feedback-based distributed neurodynamic method is initialization-free. Furthermore, the above method is revised via a maximal projection operator for the \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000WTA problem with a global coupled inequality constraint. The two methods are rigorously proved to asymptotically solve the distributed constrained \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000WTA models in accordance with LaSalle's invariance principle. The performance of our designed methods is tested via four simulation examples.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5760-5772"},"PeriodicalIF":6.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191801","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

Guest Editorial Introduction to the Special Section on Advanced Networking Technologies for Web 3.0 客座编辑：Web 3.0 高级网络技术专题介绍

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-15 DOI: 10.1109/TNSE.2024.3422588

Henry Hong-Ning Dai;Daniel Xiapu Luo;Zibin Zheng;Yan Zhang;Qin Hu

引用次数: 0

Hybrid Quantum–Classical Benders' Decomposition for Federated Learning Scheduling in Distributed Networks 用于分布式网络中联合学习调度的混合量子-经典班德斯分解法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-14 DOI: 10.1109/TNSE.2024.3440930

Xinliang Wei;Lei Fan;Yuanxiong Guo;Yanmin Gong;Zhu Han;Yu Wang

{"title":"Hybrid Quantum–Classical Benders' Decomposition for Federated Learning Scheduling in Distributed Networks","authors":"Xinliang Wei;Lei Fan;Yuanxiong Guo;Yanmin Gong;Zhu Han;Yu Wang","doi":"10.1109/TNSE.2024.3440930","DOIUrl":"10.1109/TNSE.2024.3440930","url":null,"abstract":"Scheduling multiple federated learning (FL) models within a distributed network, especially in large-scale scenarios, poses significant challenges since it involves solving NP-hard mixed-integer nonlinear programming (MINLP) problems. However, it's imperative to optimize participant selection and learning rate determination for these FL models to avoid excessive training costs and prevent resource contention. While some existing methods focus solely on optimizing a single global FL model, others struggle to achieve optimal solutions as the problem grows more complex. In this paper, exploiting the potential of quantum computing, we introduce the Hybrid Quantum-Classical Benders' Decomposition (HQCBD) algorithm to effectively tackle the joint MINLP optimization problem for multi-model FL training. HQCBD combines quantum and classical computing to solve the joint participant selection and learning scheduling problem. It decomposes the optimization problem into a master problem with binary variables and small subproblems with continuous variables, then leverages the strengths of both quantum and classical computing to solve them respectively and iteratively. Furthermore, we propose the Hybrid Quantum-Classical Multiple-cuts Benders' Decomposition (MBD) algorithm, which utilizes the inherent capabilities of quantum algorithms to produce multiple cuts in each round, to speed up the proposed HQCBD algorithm. Extensive simulation on the commercial quantum annealing machine demonstrates the effectiveness and robustness of the proposed methods (both HQCBD and MBD), with improvements of up to 70.3% in iterations and 81% in computation time over the classical Benders' decomposition algorithm on classical CPUs, even at modest scales.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6038-6051"},"PeriodicalIF":6.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191800","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

Modeling Dual-Layer Interdependent Command and Control Networks for Integrated Reconnaissance-Strike and OODA-Loop Capabilities 为综合侦察-打击和 OODA 循环能力的双层相互依存指挥与控制网络建模

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-13 DOI: 10.1109/TNSE.2024.3443191

Bo Chen;Guimei Pang;Zhengtao Xiang;Xiue Gao;Yufeng Chen;Shifeng Chen

{"title":"Modeling Dual-Layer Interdependent Command and Control Networks for Integrated Reconnaissance-Strike and OODA-Loop Capabilities","authors":"Bo Chen;Guimei Pang;Zhengtao Xiang;Xiue Gao;Yufeng Chen;Shifeng Chen","doi":"10.1109/TNSE.2024.3443191","DOIUrl":"10.1109/TNSE.2024.3443191","url":null,"abstract":"In the context of information warfare, command and control (C2) networks are exhibiting increasingly prominent multi-network dependency characteristics, leading to a growing interest in the study of interdependent C2 network models. We propose a modeling approach for interdependent C2 networks based on integrated reconnaissance–strike and OODA loop, addressing the limitations of existing edge connection strategies in effectively capturing the interdependent coupling relationships within the network. First, the interdependent relationships within the network are described, and a dual-layer structural model of interdependent C2 networks is abstracted. Second, based on the local efficiency of nodes, an edge connection strategy for sensing and firepower nodes under integrated reconnaissance–strike is proposed. Third, interdependence strength and link balance are defined, and an inter-layer coupling edge connection strategy based on interdependence strength and link balance is proposed. Finally, algorithmic simulations are designed to analyze the network properties of the model and network performance under different edge connection strategies. Simulation results demonstrate that the proposed modeling method effectively captures the interdependent characteristics of C2 networks while exhibiting enhanced network resilience against destruction.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5744-5759"},"PeriodicalIF":6.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191802","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

Delay-Aware Optimization of Fine-Grained Microservice Deployment and Routing in Edge via Reinforcement Learning 通过强化学习对边缘细粒度微服务部署和路由进行延迟感知优化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-08 DOI: 10.1109/TNSE.2024.3436616

Kai Peng;Jintao He;Jialu Guo;Yuan Liu;Jianwen He;Wei Liu;Menglan Hu

{"title":"Delay-Aware Optimization of Fine-Grained Microservice Deployment and Routing in Edge via Reinforcement Learning","authors":"Kai Peng;Jintao He;Jialu Guo;Yuan Liu;Jianwen He;Wei Liu;Menglan Hu","doi":"10.1109/TNSE.2024.3436616","DOIUrl":"10.1109/TNSE.2024.3436616","url":null,"abstract":"Microservices have exerted a profound impact on the development of internet applications. Meanwhile, the growing number of mobile terminal user requests has made the communication between microservices extremely complex, significantly impacting the quality of user service experience in mobile edge computing. Therefore, the joint optimization of microservice deployment and request routing is necessary to alleviate server pressure and enhance overall performance of large-scaled MEC applications. However, most existing work studies the microservice deployment and request routing as two isolated problems and neglects the dependencies between microservices. This paper focuses on the data dependency relationship of request and multi-instance processing problem, and then formulate the joint problem of microservice deployment and request routing as an integer nonlinear program and queuing optimization model under complex constraints. To address this problem, we propose a fine-grained reinforcement learning-based algorithm named Reward Memory Shaping Deep Deterministic Policy Gradient (RMS \u0000<inline-formula><tex-math>$_$</tex-math></inline-formula>\u0000 DDPG). Furthermore, we introduce the Long Short-Term Memory (LSTM) block into the actor network and critical network to make actions memorable. Finally, our experiments demonstrate that our algorithm is more superior in terms of delay target, load balancing and algorithm robustness compared with four baseline algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6024-6037"},"PeriodicalIF":6.7,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934722","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