IEEE Transactions on Network Science and Engineering最新文献_第8页

Nonlinear Perturbation-Based Non-Convex Optimization Over Time-Varying Networks 基于非线性扰动的时变网络非凸优化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-07 DOI: 10.1109/TNSE.2024.3439744

Mohammadreza Doostmohammadian;Zulfiya R. Gabidullina;Hamid R. Rabiee

{"title":"Nonlinear Perturbation-Based Non-Convex Optimization Over Time-Varying Networks","authors":"Mohammadreza Doostmohammadian;Zulfiya R. Gabidullina;Hamid R. Rabiee","doi":"10.1109/TNSE.2024.3439744","DOIUrl":"10.1109/TNSE.2024.3439744","url":null,"abstract":"Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization problems described over a network of nodes and proposes a computationally efficient algorithm that solves distributed convex problems and optimally finds the solution to locally non-convex objective functions. In contrast to batch gradient optimization in some literature, our algorithm is on a single-time scale with no extra inner consensus loop. It evaluates one gradient entry per node per time. Further, the algorithm addresses link-level nonlinearity representing, for example, logarithmic quantization of the exchanged data or clipping of the exchanged data bits. Leveraging perturbation-based theory and algebraic Laplacian network analysis proves optimal convergence and dynamics stability over time-varying and switching networks. The time-varying network setup might be due to packet drops or link failures. Despite the nonlinear nature of the dynamics, we prove exact convergence in the face of odd sign-preserving sector-bound nonlinear data transmission over the links. Illustrative numerical simulations further highlight our contributions.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6461-6469"},"PeriodicalIF":6.7,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934723","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

UHA: An Intelligent Uncertainty Map Based Hierarchical Attention Network System for Building Segmentation UHA：基于不确定性图谱的建筑物分段智能注意网络系统

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-06 DOI: 10.1109/TNSE.2024.3438846

Liezhuo Zhang;Xianwei Lv;Chen Yu;Jiang Xiao;Kai Liu;Hai Jin

{"title":"UHA: An Intelligent Uncertainty Map Based Hierarchical Attention Network System for Building Segmentation","authors":"Liezhuo Zhang;Xianwei Lv;Chen Yu;Jiang Xiao;Kai Liu;Hai Jin","doi":"10.1109/TNSE.2024.3438846","DOIUrl":"10.1109/TNSE.2024.3438846","url":null,"abstract":"Satellite images are becoming increasingly high quality with the advancement of high-resolution remote sensing technology. Benefited from the development of deep learning, object segmentation for the high-resolution satellite images has achieved significant improvements in recent years. However, for buildings with multiple scales and almost straight edges in satellite images, the current segmentation methods usually struggle to achieve relatively good results. In this paper, we propose an Uncertainty map based Hierarchical Attention network system (UHA) for building segmentation. UHA aims to capture the information about low-confidence areas of the building's straight edges and improve the segmentation prediction step by step by a hierarchical structure network with three decoder branches. Specifically, we first generate uncertainty maps from the sigmoid prediction maps of the segmentation network. Based on the uncertainty maps, we design an attention module where a transform function transforms the uncertainty maps into attention maps. And then, we introduce the attention module to adjacent branches of the hierarchical network to guide the latter branch by the previous branch, which improves the prediction ability of the following branch smoothly. Finally, we conduct comprehensive experiments that show the proposed system can significantly improve the performance upon baselines by a large margin.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5695-5706"},"PeriodicalIF":6.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10623827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934724","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

Practical Privacy-Preserving Convolutional Neural Network Inference Framework With Edge Computing for Health Monitoring 用于健康监测的边缘计算的隐私保护卷积神经网络推理框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-08-02 DOI: 10.1109/TNSE.2024.3434643

Ruoli Zhao;Yong Xie;Debiao He;Kim-Kwang Raymond Choo;Zoe L. Jiang

{"title":"Practical Privacy-Preserving Convolutional Neural Network Inference Framework With Edge Computing for Health Monitoring","authors":"Ruoli Zhao;Yong Xie;Debiao He;Kim-Kwang Raymond Choo;Zoe L. Jiang","doi":"10.1109/TNSE.2024.3434643","DOIUrl":"10.1109/TNSE.2024.3434643","url":null,"abstract":"Using Convolutional Neural Network (CNN) model to analyze monitoring data in Body Area Network (BAN) has become an important way to solve health related issues in the current large sub-health population and aging population. However, the inference and analysis process of BAN data needs to ensure efficiency and security. At present, ensuring a balance of efficiency and security in the inference of CCN models is challenging. Therefore, an efficient and secure CNN inference scheme is proposed based on two Edge-Cloud-Servers (CS\u0000<inline-formula><tex-math>$_{0}$</tex-math></inline-formula>\u0000 and CS\u0000<inline-formula><tex-math>$_{1}$</tex-math></inline-formula>\u0000). By analyzing the CNN model and combining two secret sharing semantics, we optimize the communication overhead of inference. Specifically, a new non-interactive secure convolutional layer computation protocol is designed to significantly reduce the number of interactions between CS\u0000<inline-formula><tex-math>$_{0}$</tex-math></inline-formula>\u0000 and CS\u0000<inline-formula><tex-math>$_{1}$</tex-math></inline-formula>\u0000. For non-linear layers, we propose a simpler secure comparison computation functionality to reduce the communication overhead. Moreover, we also design some lightweight secure building blocks based on secret sharing to improve computing efficiency. We implement our proposed scheme on two standard datasets. Through the theoretical analysis and experimental comparison, our scheme improves the computational efficiency.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5995-6006"},"PeriodicalIF":6.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886914","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

State-Observer-Based Event-Trigger Bipartite Consensus Secure Control for NMASs With Deception Attacks 基于状态观测器的事件触发式双方共识安全控制，适用于存在欺骗攻击的 NMAS

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-31 DOI: 10.1109/TNSE.2024.3436085

Shuai Sui;Dongyu Shen;Shaocheng Tong;C. L. Philip Chen

{"title":"State-Observer-Based Event-Trigger Bipartite Consensus Secure Control for NMASs With Deception Attacks","authors":"Shuai Sui;Dongyu Shen;Shaocheng Tong;C. L. Philip Chen","doi":"10.1109/TNSE.2024.3436085","DOIUrl":"10.1109/TNSE.2024.3436085","url":null,"abstract":"Deception attacks are a big danger to the security and durability of multi-agent systems (MASs). This article introduces a new technique to tackle the problem of deception attacks in nonlinear multi-agent systems (NMASs). The system can be attacked through sensors and actuators, which may lead to incomplete system data transmission and render the system state inaccessible for control design. To overcome this challenge, a fuzzy state observer is constructed to estimate the system state after the attacks, and the fuzzy logic system (FLS) is used to handle unfamiliar nonlinearities. The Nussbaum function is proposed to deal with unknown control directions and reduces system complexity. In addition, considering that there are both cooperative and competitive relationships between agents, event-triggered bipartite consensus fuzzy security adaptive control is given to mitigate the effects of deception attacks and alleviate communication resources. A based-state-observer event-triggered bipartite consensus NMASs adaptive fuzzy security control is given to achieve asymptotic output consistency after the attacks. Using Lyapunov theory, it has been demonstrated that all signals within the closed-loop system remain bounded. Finally, the test simulations show that the suggested approach can maintain the security and stability of the system during deception attacks with minimal data and resources.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5732-5743"},"PeriodicalIF":6.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866349","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

Optimal Moving-Target Circumnavigation Control of Multiple Wheeled Mobile Robots Based on Adaptive Dynamic Programming 基于自适应动态编程的多轮移动机器人最佳移动目标环行控制

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-31 DOI: 10.1109/TNSE.2024.3434633

Yanhong Luo;Yannan Li;Jinliang Ding;Huaguang Zhang

引用次数: 0

Quantum-Assisted Joint Caching and Power Allocation for Integrated Satellite-Terrestrial Networks 卫星-地面一体化网络的量子辅助联合缓存和功率分配

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-30 DOI: 10.1109/TNSE.2024.3435444

Yu Zhang;Yanmin Gong;Lei Fan;Yu Wang;Zhu Han;Yuanxiong Guo

{"title":"Quantum-Assisted Joint Caching and Power Allocation for Integrated Satellite-Terrestrial Networks","authors":"Yu Zhang;Yanmin Gong;Lei Fan;Yu Wang;Zhu Han;Yuanxiong Guo","doi":"10.1109/TNSE.2024.3435444","DOIUrl":"10.1109/TNSE.2024.3435444","url":null,"abstract":"LowEarth orbit (LEO) satellite network can complement terrestrial networks for achieving global wireless coverage and improving delay-sensitive Internet services. This paper proposes an integrated satellite-terrestrial network (ISTN) architecture to provide ground users with seamless and reliable content delivery services. For optimal service provisioning in this architecture, we formulate an optimization model to maximize the network throughput by jointly optimizing content delivery policy, cache placement, and transmission power allocation. The resulting optimization model is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computer solvers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithm to address this challenge. Specifically, we first exploit the generalized Benders' decomposition (GBD) to decompose the problem into a master problem and a subproblem and then leverage the state-of-the-art quantum annealer to solve the challenging master problem. Furthermore, a multi-cut strategy is designed in HQCGBD to accelerate the solution process by leveraging the quantum advantages in parallel computing. Simulation results demonstrate the superiority of the proposed HQCGBD algorithm and validate the effectiveness of the proposed cache-enabled ISTN architecture.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5163-5174"},"PeriodicalIF":6.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866353","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

Synchronization of Fractional Reaction-Diffusion Complex Networks With Unknown Couplings 具有未知耦合的分数反应-扩散复杂网络的同步化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-30 DOI: 10.1109/TNSE.2024.3432997

Mouquan Shen;Chen Wang;Qing-Guo Wang;Yonghui Sun;Guangdeng Zong

引用次数: 0

GAXG: A Global and Self-Adaptive Optimal Graph Topology Generation Framework for Explaining Graph Neural Networks GAXG：用于解释图神经网络的全局和自适应最优图拓扑生成框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-30 DOI: 10.1109/TNSE.2024.3435839

Xiaofeng Liu;Chenqi Guo;Mingjun Zhao;Yinglong Ma

{"title":"GAXG: A Global and Self-Adaptive Optimal Graph Topology Generation Framework for Explaining Graph Neural Networks","authors":"Xiaofeng Liu;Chenqi Guo;Mingjun Zhao;Yinglong Ma","doi":"10.1109/TNSE.2024.3435839","DOIUrl":"10.1109/TNSE.2024.3435839","url":null,"abstract":"Numerous explainability techniques have been developed to reveal the prediction principles of Graph Neural Networks (GNNs) across diverse domains. However, many existing approaches, particularly those concentrating on model-level explanations, tend to grapple with the tunnel vision problem, leading to less-than-optimal outcomes and constraining users' comprehensive understanding of GNNs. Furthermore, these methods typically require hyperparameters to mold the explanations, introducing unintended human biases. In response, we present GAXG, a global and self-adaptive optimal graph topology generation framework for explaining GNNs' prediction principles at model-level. GAXG addresses the challenges of tunnel vision and hyperparameter reliance by integrating a strategically tailored Monte Carlo Tree Search (MCTS) algorithm. Notably, our tailored MCTS algorithm is modified to incorporate an Edge Mask Learning and Simulated Annealing-based subgraph screening strategy during the expansion phase, resolving the inherent time-consuming challenges of the tailored MCTS and enhancing the quality of the generated explanatory graph topologies. Experimental results underscore GAXG's effectiveness in discovering global explanations for GNNs, outperforming leading explainers on most evaluation metrics.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6007-6023"},"PeriodicalIF":6.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866352","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

Leader-Following Consensus Control of Unknown Nonlinear MASs Under False Data Injection Attacks 虚假数据注入攻击下未知非线性 MAS 的领导者追随共识控制

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-29 DOI: 10.1109/TNSE.2024.3433392

Meirong Wang;Jianqiang Hu;Ahmed Alsaedi;Jinde Cao

{"title":"Leader-Following Consensus Control of Unknown Nonlinear MASs Under False Data Injection Attacks","authors":"Meirong Wang;Jianqiang Hu;Ahmed Alsaedi;Jinde Cao","doi":"10.1109/TNSE.2024.3433392","DOIUrl":"10.1109/TNSE.2024.3433392","url":null,"abstract":"This paper studies the distributed leader-following consensus problem of unknown nonlinear multi-agent systems (MASs) under false data injection attacks (FDIAs), where the followers connected to the leader may receive the injected false data from the leader's communication channels. Due to the existence of FDIAs, the real and broken leader state value is not available to the followers and cannot be used by followers' controllers, thus an attack compensator based on the errors between the predictive value and the actual measured value is added to the controller to mitigate the adverse effects of attacks. Fuzzy logic systems (FLSs) and Neural Network (NN) techniques are applied to approximate the unknown nonlinear dynamic by estimating the weight matrix. The proposed controller combines attack compensation with unknown nonlinear function compensation, and finally obtains sufficient conditions for the MASs to be ultimately uniformly bounded (UUB). Two algorithms are presented for undirected and directed communication topologies respectively and the simulation results verify the feasibility of the proposed consensus algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 5","pages":"4513-4524"},"PeriodicalIF":6.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866357","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

Scale-Free Collaborative Protocol Design for Exact Output Synchronization of Multi-Agent Systems in the Presence of Disturbances and Measurement Noise With Known Frequencies 在已知频率的干扰和测量噪声条件下实现多代理系统精确输出同步的无规模协作协议设计

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-29 DOI: 10.1109/TNSE.2024.3433604

Zhenwei Liu;Meirong Zhang;Ali Saberi;Anton A. Stoorvogel

{"title":"Scale-Free Collaborative Protocol Design for Exact Output Synchronization of Multi-Agent Systems in the Presence of Disturbances and Measurement Noise With Known Frequencies","authors":"Zhenwei Liu;Meirong Zhang;Ali Saberi;Anton A. Stoorvogel","doi":"10.1109/TNSE.2024.3433604","DOIUrl":"10.1109/TNSE.2024.3433604","url":null,"abstract":"Cooperative control is concerned with engineering systems that can be characterized as a collection of decision making components called agents with locally sensed information and limited inter-component communication, all seeking to achieve a collective objective. Examples include mobile sensor networks, autonomous vehicle systems, distributed computation, and power systems. Areas of research that are related to cooperative control include multi-agent control, distributed systems, networked control, etc. Most of the results in cooperative control (such as synchronization) of MAS require some prior information of the communication topology, which means that control or synchronization might not be achieved when there is a change in the communication topology. In particular, scale fragility has shown that instability of the network often occurs when the number of agents grows large. In this paper, we focus on scalable synchronization of MAS in the presence of disturbances and measurement noise with known frequencies. We develop a \u0000<italic>scale-free</i>\u0000 collaborative protocol design for this class of MAS. The scalable exact output and regulated output synchronizations are developed for homogeneous MAS with non-introspective agents and heterogeneous MAS with introspective agents, respectively. The proposed protocol design methodology is solely based on agent models and does not need any information about the communication network or the number of agents. The results are illustrated in numerical examples with the random number of agents.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5720-5731"},"PeriodicalIF":6.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866360","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