IEEE Transactions on Signal and Information Processing over Networks最新文献_第10页

Graph Signal Reconstruction Under Heterogeneous Noise via Adaptive Uncertainty-Aware Sampling and Soft Classification 通过自适应不确定性感知采样和软分类实现异质噪声下的图信号重构

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375593

Alessio Fascista;Angelo Coluccia;Chiara Ravazzi

{"title":"Graph Signal Reconstruction Under Heterogeneous Noise via Adaptive Uncertainty-Aware Sampling and Soft Classification","authors":"Alessio Fascista;Angelo Coluccia;Chiara Ravazzi","doi":"10.1109/TSIPN.2024.3375593","DOIUrl":"10.1109/TSIPN.2024.3375593","url":null,"abstract":"Reconstructing bandlimited graph signals from a subset of noisy measurements is a fundamental challenge within the realm of signal processing. Historically, this problem has been approached assuming uniform noise variance across the network. Nevertheless, practical scenarios often present heterogeneous noise landscapes, greatly complicating the signal reconstruction process. This study tackles reconstruction of graph signals across networks where measurements may be affected by heterogeneous noise. A Bayesian model tailored for graph signals is employed, considering the potential existence of node-specific variations in measurement variance, namely different (and unknown) levels of uncertainty. Moreover, a novel uncertainty-aware local graph coherence metric is introduced, capitalizing on estimated parameters to refine the sampling process. By accommodating uncertainty, signal reconstruction accuracy is enhanced, even in demanding noise conditions. The proposed approach revolves around a framework combining maximum likelihood and maximum a-posteriori principles. Specifically, each observation is weighted based on a soft classification of nodes, so incorporating measurements reliability into the reconstruction process. The latter is performed through a novel algorithm coupling re-weighted iterative least squares with expectation-maximization. Such an algorithm can effectively manage heterogeneous noise and features a non-local regularization term, which promotes sparsity in the reconstructed signal while preserving signal discontinuities, crucial for capturing the characteristics of the underlying graph signal. Extensive simulations demonstrate the effectiveness of the proposed approach for various graph topologies and anomalous conditions, revealing substantial enhancements in signal reconstruction compared to existing methods. An illustrative example on PM10 data from the European Copernicus Atmosphere Monitoring Service (CAMS) is also reported.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"277-293"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105541","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

Distributed Optimisation With Linear Equality and Inequality Constraints Using PDMM 利用 PDMM 实现线性相等和不相等约束的分布式优化

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375597

Richard Heusdens;Guoqiang Zhang

{"title":"Distributed Optimisation With Linear Equality and Inequality Constraints Using PDMM","authors":"Richard Heusdens;Guoqiang Zhang","doi":"10.1109/TSIPN.2024.3375597","DOIUrl":"10.1109/TSIPN.2024.3375597","url":null,"abstract":"In this article, we consider the problem of distributed optimisation of a separable convex cost function over a graph, where every edge and node in the graph could carry both linear equality and/or inequality constraints. We show how to modify the primal-dual method of multipliers (PDMM), originally designed for linear equality constraints, such that it can handle inequality constraints as well. The proposed algorithm does not need any slack variables, which is similar to the recent work (He et al., 2023) which extends the alternating direction method of multipliers (ADMM) for addressing decomposable optimisation with linear equality and inequality constraints. Using convex analysis, monotone operator theory and fixed-point theory, we show how to derive the update equations of the modified PDMM algorithm by applying Peaceman-Rachford splitting to the monotonic inclusion related to the lifted dual problem. To incorporate the inequality constraints, we impose a non-negativity constraint on the associated dual variables. This additional constraint results in the introduction of a reflection operator to model the data exchange in the network, instead of a permutation operator as derived for equality constraint PDMM. Convergence for both synchronous and stochastic update schemes of PDMM are provided. The latter includes asynchronous update schemes and update schemes with transmission losses. Experiments show that PDMM converges notably faster than extended ADMM of (He et al., 2023).","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"294-306"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105668","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

Unifying Epidemic Models With Mixtures 用混合物统一流行病模型

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375600

Arnab Sarker;Ali Jadbabaie;Devavrat Shah

{"title":"Unifying Epidemic Models With Mixtures","authors":"Arnab Sarker;Ali Jadbabaie;Devavrat Shah","doi":"10.1109/TSIPN.2024.3375600","DOIUrl":"10.1109/TSIPN.2024.3375600","url":null,"abstract":"The COVID-19 pandemic has emphasized the need for a robust understanding of epidemic models. Current models of epidemics are classified as either mechanistic or non-mechanistic: mechanistic models make explicit assumptions on the dynamics of disease, whereas non-mechanistic models make assumptions on the form of observed time series. Here, we introduce a simple mixture-based model which bridges the two approaches while retaining benefits of both. The model represents time series of cases as a mixture of Gaussian curves, providing a flexible function class to learn from data, and we show that it arises as the natural outcome of a stochastic process based on a networked SIR framework. This allows learned parameters to take on a more meaningful interpretation compared to similar non-mechanistic models, and we validate the interpretations using auxiliary mobility data collected during the COVID-19 pandemic. We provide a simple learning algorithm to identify model parameters and establish theoretical results which show the model can be efficiently learned from data. Empirically, we find the model to have low prediction error. Moreover, this allows us to systematically understand the impacts of interventions on COVID-19, which is critical in developing data-driven solutions for controlling epidemics.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"239-252"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105671","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 Asymmetric Bipartite Consensus for Multi-Agent Systems Using Data-Driven Iterative Learning Control 利用数据驱动的迭代学习控制实现多代理系统的有限时间非对称双方共识

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375602

Jiaqi Liang;Xuhui Bu;Zhongsheng Hou

{"title":"Finite-Time Asymmetric Bipartite Consensus for Multi-Agent Systems Using Data-Driven Iterative Learning Control","authors":"Jiaqi Liang;Xuhui Bu;Zhongsheng Hou","doi":"10.1109/TSIPN.2024.3375602","DOIUrl":"10.1109/TSIPN.2024.3375602","url":null,"abstract":"A general finite-time bipartite consensus problem is studied for multi-agent systems with completely unknown nonlinearities. An asymmetric bipartite consensus task is defined by introducing a proportional-related coefficient and a relationship-related index, which arranges that the agents reach an agreement with proportional modulus and opposite signs. With the cooperative-antagonistic interactions, a model-free adaptive bipartite iterative learning consensus protocol is proposed for promoting the accuracy of the performance within a finite-time interval. By employing the matrix transformation and property of the nonnegative matrix, the iteratively asymptotic convergence of the error of the MAS is guaranteed under the structurally balanced digraph has an oriented spanning tree. This differs from MFAILC results that have been proven based on matrix norm and do not require strong connectivity of digraphs. Moreover, the bounds for elements in the estimation-related matrices are presented, followed by providing a graph correlated sufficient condition to guide selection of control parameters. The results further extend to the control of asymmetric bipartite consensus tracking. The simulation examples verify the effectiveness of the distributed learning control protocols.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"227-238"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105673","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

Probability-Guaranteed Distributed Estimation for Two-Dimensional Systems Under Stochastic Access Protocol 随机访问协议下二维系统的概率保证分布式估计

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375596

Meiyu Li;Jinling Liang

{"title":"Probability-Guaranteed Distributed Estimation for Two-Dimensional Systems Under Stochastic Access Protocol","authors":"Meiyu Li;Jinling Liang","doi":"10.1109/TSIPN.2024.3375596","DOIUrl":"10.1109/TSIPN.2024.3375596","url":null,"abstract":"This paper studies the probability-guaranteed distributed estimation problem for a kind of two-dimensional shift-varying sensor networks under the stochastic access protocol (SAP). The considered system is affected by unknown-but-bounded perturbations and sector bounded nonlinearity. The communication architecture of a multi-node network is expressed by a digraph. Due to the limited communication channel, each moment allows only one adjacent node to send its measurement data and schedules the signal transmission of the addressing system using the SAP, characterized by a series of independent random variables. For each smart sensor, we designed a distributed estimator based on the network topology as well as the SAP and derived sufficient conditions to ascertain the probability of the estimation error located in the desired ellipsoid being not less than the predetermined value. Collection of these probability ellipsoids acquired at each position is then minimized by solving a set of convex optimization problems in the meaning of matrix trace. Finally, efficiency of the estimator design strategy proposed is demonstrated using a numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"216-226"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105672","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

Fully Distributed Consensus Control for a Class of Disturbed Linear Multi-Agent Systems Over Event-Triggered Communication 通过事件触发通信实现一类受干扰线性多代理系统的完全分布式共识控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375612

Jia Deng;Fuyong Wang;Zhongxin Liu;Zengqiang Chen

{"title":"Fully Distributed Consensus Control for a Class of Disturbed Linear Multi-Agent Systems Over Event-Triggered Communication","authors":"Jia Deng;Fuyong Wang;Zhongxin Liu;Zengqiang Chen","doi":"10.1109/TSIPN.2024.3375612","DOIUrl":"10.1109/TSIPN.2024.3375612","url":null,"abstract":"This article is concerned with the fully distributed consensus control problem of a class of disturbed general linear multi-agent systems under event-triggered communication. Different from existing works, the disturbances considered in this article are more practical and complex. Each agent is subject to disturbances generated by exosystems and each exosystem is considered to exist with possible modelling errors. First, a local disturbance observer is designed for each agent to compensate potentially unbounded external disturbances to a bounded situation, but the value of this bound is not accessible because the upper bound of the modelling error is unknown. Second, an adaptive consensus control law with complete disturbance rejection is further proposed, by which the consensus error converges to zero over time. Third, with limited communication resources, an event-triggered communication mechanism is designed for deciding when an agent broadcasts information, which effectively saves communication resources while ensuring that the original control goal is achieved. In addition, it is demonstrated that Zeno behaviour is excluded. Finally, the correctness of the theoretical results is verified by a simulation example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"205-215"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105470","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

Event-Triggered Distributed Estimation With Inter-Event Information Retrieval 事件触发分布式估计与事件间信息检索

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-11 DOI: 10.1109/TSIPN.2024.3375605

Xiaoxian Lao;Chunguang Li

{"title":"Event-Triggered Distributed Estimation With Inter-Event Information Retrieval","authors":"Xiaoxian Lao;Chunguang Li","doi":"10.1109/TSIPN.2024.3375605","DOIUrl":"10.1109/TSIPN.2024.3375605","url":null,"abstract":"Distributed estimation has attracted great attention in the last few decades. In the problem of distributed estimation, a set of nodes estimate some parameter from noisy measurements. To leverage joint effort, the nodes communicate with each other in the estimation process. The communications consume bandwidth and energy resources, and these resources are often limited in real-world applications. To cope with the resources constraints, the event-triggered mechanism is proposed and widely adopted. It only allows signals to be transmitted if they carry significant amount of information. Various criteria of determining whether the information is significant lead to different trigger rules. With these rules, the resources can be saved. However, in the meanwhile, some inter-event information, not that important but still of certain use, is unavailable to the neighbors. The absence of these inter-event information may affect the algorithm performance. Considering this, in this paper, we come up with an inter-event information retrieval scheme to recover certain untransmitted information, which is the first work doing so to the best of our knowledge. We design an approach for inter-event information retrieval, and formulate and solve an optimization problem which has a closed-form solution to acquire information. With more information at hand, the performance degeneration caused by the event-triggered mechanism can be alleviated. We derive sufficient conditions for convergence of the overall algorithm. We also demonstrate the advantages of the proposed scheme by simulation experiments.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"253-263"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105479","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

Online Auditing of Information Flow 信息流在线审计

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-10 DOI: 10.1109/TSIPN.2024.3399558

Mor Oren-Loberman;Vered Azar;Wasim Huleihel

{"title":"Online Auditing of Information Flow","authors":"Mor Oren-Loberman;Vered Azar;Wasim Huleihel","doi":"10.1109/TSIPN.2024.3399558","DOIUrl":"10.1109/TSIPN.2024.3399558","url":null,"abstract":"Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society. In this paper, we consider the problem of online auditing of information flow/propagation with the goal of classifying news items as fake or genuine. Specifically, driven by experiential studies on real-world social media platforms, we propose a probabilistic Markovian information spread model over networks modeled by graphs. We then formulate our inference task as a certain sequential detection problem with the goal of minimizing the combination of the error probability and the time it takes to achieve the correct decision. For this model, we find the optimal detection algorithm minimizing the aforementioned risk and prove several statistical guarantees. We then test our algorithm over real-world datasets. To that end, we first construct an offline algorithm for learning the probabilistic information spreading model, and then apply our optimal detection algorithm. Experimental study show that our algorithm outperforms state-of-the-art misinformation detection algorithms in terms of accuracy and detection time.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"487-499"},"PeriodicalIF":3.2,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937171","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

Distributed Generalized Nash Equilibria Computation of Noncooperative Games Via Novel Primal-Dual Splitting Algorithms 通过新颖的原始-双重分割算法计算非合作博弈的分布式广义纳什均衡点

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-02-09 DOI: 10.1109/TSIPN.2024.3364613

Liang Ran;Huaqing Li;Lifeng Zheng;Jun Li;Zhe Li;Jinhui Hu

{"title":"Distributed Generalized Nash Equilibria Computation of Noncooperative Games Via Novel Primal-Dual Splitting Algorithms","authors":"Liang Ran;Huaqing Li;Lifeng Zheng;Jun Li;Zhe Li;Jinhui Hu","doi":"10.1109/TSIPN.2024.3364613","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3364613","url":null,"abstract":"This article investigates the generalized Nash equilibria (GNE) seeking problem for noncooperative games, where all players dedicate to selfishly minimizing their own cost functions subject to local constraints and coupled constraints. To tackle the considered problem, we initially form an explicit local equilibrium condition for its variational formulation. By employing proximal splitting operators, a novel distributed primal-dual splitting algorithm with full-decision information (Dist_PDS_FuDeIn) is designed, eliminating the need for global step-sizes. Furthermore, to address scenarios where players lack access to all other players' decisions, a local estimation is introduced to approximate the decision information of other players, and a fully distributed primal-dual splitting algorithm with partial-decision information (Dist_PDS_PaDeIn) is then proposed. Both algorithms enable the derivation of new distributed forward-backward-like extensions. Theoretically, a new analytical approach for convergence is presented, demonstrating that the proposed algorithms converge to the variational GNE of games, and their convergence rates are also proven, provided that uncoordinated step-sizes are positive and less than explicit upper bounds. Moreover, the approach not only generalizes the forward-backward splitting technique but also improves convergence rates of several well-known algorithms. Finally, the advantages of Dist_PDS_FuDeIn and Dist_PDS_PaDeIn are illustrated through comparative simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"179-194"},"PeriodicalIF":3.2,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942779","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

Strong Convergence of a Random Actions Model in Opinion Dynamics 舆论动力学中随机行动模型的强收敛性

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-02-01 DOI: 10.1109/TSIPN.2024.3361373

Olle Abrahamsson;Danyo Danev;Erik G. Larsson

引用次数: 0