IEEE Transactions on Signal and Information Processing over Networks最新文献

Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems 基于强化学习的受扰多代理系统事件触发约束遏制控制

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-08 DOI: 10.1109/TSIPN.2024.3487422

Daocheng Tang;Ning Pang;Xin Wang

{"title":"Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems","authors":"Daocheng Tang;Ning Pang;Xin Wang","doi":"10.1109/TSIPN.2024.3487422","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3487422","url":null,"abstract":"This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"820-832"},"PeriodicalIF":3.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Finite-Time Performance Mask Function-Based Distributed Privacy-Preserving Consensus: Case Study on Optimal Dispatch of Energy System 基于有限时间性能掩码函数的分布式隐私保护共识：能源系统优化调度案例研究

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-06 DOI: 10.1109/TSIPN.2024.3485480

Minxue Kong;Feifei Shen;Zhi Li;Xin Peng;Weimin Zhong

{"title":"Finite-Time Performance Mask Function-Based Distributed Privacy-Preserving Consensus: Case Study on Optimal Dispatch of Energy System","authors":"Minxue Kong;Feifei Shen;Zhi Li;Xin Peng;Weimin Zhong","doi":"10.1109/TSIPN.2024.3485480","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485480","url":null,"abstract":"Privacy-preserving consensus can address the information being leaked during distributed computing, encouraging its application in various scenarios. This paper investigates the finite-time privacy-preserving distributed optimal dispatch for energy systems (ESs). Firstly, a dynamic output mask function is designed to ensure that each node's internal state cannot be identified while accomplishing a distributed task. Second, two finite-time privacy-preserving consensus algorithms are presented, including leader–follower and average consensus algorithms. Under the proposed dynamic mask function, the proposed algorithms are local, allowing each node to protect its privacy by adopting the proposed dynamic output mask. The superiority of the proposed algorithm lies in its ability to achieve precise convergence while ensuring privacy protection. Third, the accurate value of the target state can be obtained after finite steps when processing and transmitting information. In addition, several conditions are presented for ensuring the convergence of the algorithms, which is not limited by special topologies such as undirected graphs and balanced graphs. Finally, an application that achieves the distributed optimal dispatch for the CCHP-based (Combined Cooling, Heating, and Power) ESs, and two examples illustrate that the algorithms can be effective access to economic optimization and excellent privacy performance.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"776-787"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Discrete-Time Controllability of Cartesian Product Networks 笛卡尔产品网络的离散时间可控性

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-06 DOI: 10.1109/TSIPN.2024.3487411

Bo Liu;Mengjie Hu;Junjie Huang;Qiang Zhang;Yin Chen;Housheng Su

引用次数: 0

Generalized Simplicial Attention Neural Networks 广义简约注意神经网络

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-25 DOI: 10.1109/TSIPN.2024.3485473

Claudio Battiloro;Lucia Testa;Lorenzo Giusti;Stefania Sardellitti;Paolo Di Lorenzo;Sergio Barbarossa

{"title":"Generalized Simplicial Attention Neural Networks","authors":"Claudio Battiloro;Lucia Testa;Lorenzo Giusti;Stefania Sardellitti;Paolo Di Lorenzo;Sergio Barbarossa","doi":"10.1109/TSIPN.2024.3485473","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485473","url":null,"abstract":"Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes. For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers. Hinging on topological signal processing principles, we devise a series of principled self-attention mechanisms able to process data associated with simplices of various order, such as nodes, edges, triangles, and beyond. These schemes learn how to combine data associated with neighbor simplices of consecutive order in a task-oriented fashion, leveraging on the simplicial Dirac operator and its Dirac decomposition. We also prove that GSAN satisfies two fundamental properties: permutation equivariance and simplicial-awareness. Finally, we illustrate how our approach compares favorably with other simplicial and graph models when applied to several (inductive and transductive) tasks, such as trajectory prediction, missing data imputation, graph classification, and simplex prediction.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"833-850"},"PeriodicalIF":3.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Dual-Domain Defenses for Byzantine-Resilient Decentralized Resource Allocation 拜占庭弹性分散资源分配的双域防御系统

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-23 DOI: 10.1109/TSIPN.2024.3485508

Runhua Wang;Qing Ling;Zhi Tian

引用次数: 0

Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model 概率误差模型下的图卷积神经网络灵敏度

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-23 DOI: 10.1109/TSIPN.2024.3485532

Xinjue Wang;Esa Ollila;Sergiy A. Vorobyov

{"title":"Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model","authors":"Xinjue Wang;Esa Ollila;Sergiy A. Vorobyov","doi":"10.1109/TSIPN.2024.3485532","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485532","url":null,"abstract":"Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data. This paper proposes an analysis framework to investigate the sensitivity of GCNNs to probabilistic graph perturbations, directly impacting the graph shift operator (GSO). Our study establishes tight expected GSO error bounds, which are explicitly linked to the error model parameters, and reveals a linear relationship between GSO perturbations and the resulting output differences at each layer of GCNNs. This linearity demonstrates that a single-layer GCNN maintains stability under graph edge perturbations, provided that the GSO errors remain bounded, regardless of the perturbation scale. For multilayer GCNNs, the dependency of system's output difference on GSO perturbations is shown to be a recursion of linearity. Finally, we exemplify the framework with the Graph Isomorphism Network (GIN) and Simple Graph Convolution Network (SGCN). Experiments validate our theoretical derivations and the effectiveness of our approach.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"788-803"},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A Continuous-Time Algorithm for Distributed Optimization With Nonuniform Time-Delay Under Switching and Unbalanced Digraphs 切换和不平衡图谱下非均匀时延分布式优化的连续时间算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-23 DOI: 10.1109/TSIPN.2024.3485549

Wenbo Zhu;Wenqiang Wu;Qingling Wang

引用次数: 0

Gotta Match 'Em All: Solution Diversification in Graph Matching Matched Filters 必须全部匹配：图形匹配匹配过滤器中的解决方案多样化

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-07 DOI: 10.1109/TSIPN.2024.3467921

Zhirui Li;Ben K Johnson;Daniel L. Sussman;Carey E. Priebe;Vince Lyzinski

引用次数: 0

Edge-Triggered Leader–Follower Consensus of Multiple Spacecraft Systems With Unknown Disturbances 具有未知干扰的多个航天器系统的边缘触发式领导者-追随者共识

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-09-25 DOI: 10.1109/TSIPN.2024.3467916

Dong Liang;Shimin Wang;Engang Tian

{"title":"Edge-Triggered Leader–Follower Consensus of Multiple Spacecraft Systems With Unknown Disturbances","authors":"Dong Liang;Shimin Wang;Engang Tian","doi":"10.1109/TSIPN.2024.3467916","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3467916","url":null,"abstract":"Multiple rigid bodies can model various practical industrial systems. However, periodic sampled-data communication can have a load over the network subject to limited bandwidth. The research on the leader-follower attitude consensus issue for a group of rigid-body dynamics is conducted in this technical paper. The plant of each follower is subject to unknown external disturbances. To reduce the burden of the communication network, an edge-triggered nonlinear distributed observer with dynamic triggering mechanisms is presented. The proposed observer has the ability to evaluate the leader system's state regardless of implementing the continuous-time exchange of the neighborhood information. The proposed edge-based triggering mechanism is asynchronous while eliminating the Zeno phenomenon. Based on the nonlinear observer, a distributed control protocol together with an adaptive law is put forward in order to realize the leader-follower attitude consensus while attenuating the unknown external disturbances. In the end, an illustrative example of a collection of spacecraft systems is provided to verify the feasibility of our methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"740-751"},"PeriodicalIF":3.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Compressed Regression Over Adaptive Networks 自适应网络上的压缩回归

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-09-20 DOI: 10.1109/TSIPN.2024.3464350

Marco Carpentiero;Vincenzo Matta;Ali H. Sayed

{"title":"Compressed Regression Over Adaptive Networks","authors":"Marco Carpentiero;Vincenzo Matta;Ali H. Sayed","doi":"10.1109/TSIPN.2024.3464350","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3464350","url":null,"abstract":"In this work we derive the performance achievable by a network of distributed agents that solve, \u0000<italic>adaptively</i>\u0000 and in the presence of \u0000<italic>communication constraints</i>\u0000, a regression problem. Agents employ the recently proposed ACTC (adapt-compress-then-combine) diffusion strategy, where the signals exchanged locally by neighboring agents are encoded with \u0000<italic>randomized differential compression</i>\u0000 operators. We provide a detailed characterization of the mean-square estimation error, which is shown to comprise a term related to the error that agents would achieve without communication constraints, plus a term arising from compression. The analysis reveals quantitative relationships between the compression loss and fundamental attributes of the distributed regression problem, in particular, the stochastic approximation error caused by the gradient noise and the network topology (through the Perron eigenvector). We show that knowledge of such relationships is critical to allocate optimally the communication resources across the agents, taking into account their individual attributes, such as the quality of their data or their degree of centrality in the network topology. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned \u0000<italic>online</i>\u0000 by the agents. Illustrative examples show that a significant performance improvement, as compared to a blind (i.e., uniform) resource allocation, can be achieved by optimizing the allocation by means of the provided mean-square-error formulas.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"851-867"},"PeriodicalIF":3.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10685148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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