IEEE Transactions on Signal and Information Processing over Networks最新文献_第8页

Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition 通过状态分解实现分散优化的隐私保护推拉法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-20 DOI: 10.1109/TSIPN.2024.3402430

Huqiang Cheng;Xiaofeng Liao;Huaqing Li;Qingguo Lü;You Zhao

{"title":"Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition","authors":"Huqiang Cheng;Xiaofeng Liao;Huaqing Li;Qingguo Lü;You Zhao","doi":"10.1109/TSIPN.2024.3402430","DOIUrl":"10.1109/TSIPN.2024.3402430","url":null,"abstract":"Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, resource allocation, etc. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this may either sacrifice optimization accuracy or incur a heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, named PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. When the objective function is smooth and satisfies the Polyak-Łojasiewicz (PL) condition, PPSD attains an \u0000<inline-formula><tex-math>$R$</tex-math></inline-formula>\u0000-linear convergence rate. Moreover, the algorithm can preserve the normal agents' private information from being leaked to honest-but-curious attackers. Simulations further confirm the results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"513-526"},"PeriodicalIF":3.2,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151780","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

Consensus Analysis for Cooperative-Competitive Multiagent Systems Under False Data Injection Attacks via Dynamic Event-Triggered Observers 通过动态事件触发观测器对虚假数据注入攻击下的合作竞争型多代理系统进行共识分析

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-19 DOI: 10.1109/TSIPN.2024.3375611

Sangli Shi;Zhengxin Wang;Min Xiao;Guo-Ping Jiang;Jinde Cao

引用次数: 0

Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs 带限信号在图上的片断恒定表示和采样

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-19 DOI: 10.1109/TSIPN.2024.3378122

Guangrui Yang;Qing Zhang;Lihua Yang

{"title":"Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs","authors":"Guangrui Yang;Qing Zhang;Lihua Yang","doi":"10.1109/TSIPN.2024.3378122","DOIUrl":"10.1109/TSIPN.2024.3378122","url":null,"abstract":"Signal representations on graphs are at the heart of most graph signal processing techniques, allowing for targeted signal models for tasks such as denoising, compression, sampling, reconstruction and detection. This paper studies the piecewise-constant representation of bandlimited graph signals, thereby establishing the relationship between the bandlimited graph signal and the piecewise-constant graph signal. For this purpose, we first introduce the concept of \u0000<inline-formula><tex-math>$epsilon$</tex-math></inline-formula>\u0000-level piecewise-constant representation for a general signal space. Then, using a distance matrix, a single-layer piecewise-constant representation algorithm is proposed to find an \u0000<inline-formula><tex-math>$epsilon$</tex-math></inline-formula>\u0000-level piecewise-constant representation for bandlimited graph signals. On this basis, we further propose a multi-layer piecewise-constant representation algorithm, which can find a node partition with as few pieces as possible to represent bandlimited graph signals piecewise within a preset error bound. Finally, as an application, we apply the node partition obtained by the multi-layer algorithm to establish a sampling theory for bandlimited signals, which does not need to compute the eigendecomposition of a variation operator in both sampling and signal reconstruction. Numerical experiments show that the proposed algorithms have good performance.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"332-346"},"PeriodicalIF":3.2,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140168763","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

A Gradient Tracking Protocol for Optimization Over Nabla Fractional Multi-Agent Systems 纳布拉分数多代理系统优化的梯度跟踪协议

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-17 DOI: 10.1109/TSIPN.2024.3402354

Shuaiyu Zhou;Yiheng Wei;Shu Liang;Jinde Cao

引用次数: 0

Remote State Estimation Under DoS Attacks in CPSs With Arbitrary Tree Topology: A Bayesian Stackelberg Game Approach 具有任意树状拓扑结构的 CPS 中 DoS 攻击下的远程状态估计：贝叶斯堆栈博弈方法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-17 DOI: 10.1109/TSIPN.2024.3394776

Yuhan Wang;Wei Xing;Junfeng Zhang;Le Liu;Xudong Zhao

{"title":"Remote State Estimation Under DoS Attacks in CPSs With Arbitrary Tree Topology: A Bayesian Stackelberg Game Approach","authors":"Yuhan Wang;Wei Xing;Junfeng Zhang;Le Liu;Xudong Zhao","doi":"10.1109/TSIPN.2024.3394776","DOIUrl":"10.1109/TSIPN.2024.3394776","url":null,"abstract":"In this paper, we consider remote state estimation for an arbitrary tree topology in cyber-physical systems (CPSs) subject to Denial-of-Service (DoS) attacks. A sensor transmits its local estimation to the root node of the tree, and the root node transmits the optimal estimation to its child nodes until the leaf nodes are reached. In the meanwhile, a malicious attacker can jam all communication channels strategically connected to the attacked node. With the energy constraints in mind, both the defender and attacker adopt strategies that involve allocating energy to determine which nodes to protect or attack at each time step. A Bayesian Stackelberg game (BSG) framework with incomplete information is implemented, where the defender has no access to the available energy of the attacker exactly except for its probability distribution. In addition, a Markov decision process (MDP) and a Stackelberg Q-learning algorithm are presented to obtain the Stackelberg equilibrium (SE) policy over a finite time horizon. Finally, a numerical example is provided to demonstrate our main results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"527-538"},"PeriodicalIF":3.2,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061136","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

Sensor-Fault Detection, Isolation and Accommodation for Natural-Gas Pipelines Under Transient Flow 瞬态流量下天然气管道的传感器故障检测、隔离和容纳

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-13 DOI: 10.1109/TSIPN.2024.3377134

Khadija Shaheen;Apoorva Chawla;Ferdinand Evert Uilhoorn;Pierluigi Salvo Rossi

{"title":"Sensor-Fault Detection, Isolation and Accommodation for Natural-Gas Pipelines Under Transient Flow","authors":"Khadija Shaheen;Apoorva Chawla;Ferdinand Evert Uilhoorn;Pierluigi Salvo Rossi","doi":"10.1109/TSIPN.2024.3377134","DOIUrl":"10.1109/TSIPN.2024.3377134","url":null,"abstract":"The monitoring of natural gas pipelines is highly dependent on the information provided by different types of sensors. However, sensors are prone to faults, which results in performance degradation and serious hazards such as leaks or explosions. To prevent catastrophic failures and ensure the safe and efficient operation of the pipelines, it is crucial to timely diagnose sensor faults in natural gas pipelines. This paper investigates model-based sensor fault diagnosis techniques in a natural-gas pipeline under transient flow. A fusing architecture based on distributed data fusion is used for implementing the sensor fault detection, isolation, and accommodation (SFDIA) mechanism. The fusing architecture consists of a set of local filters and an information mixer. The local filters estimate the state variables in parallel, which are subsequently transferred to the information mixer to evaluate the sensor faults and compute fault-free state estimates. In this paper, three different types of fusing filters, namely based on the ensemble Kalman filter (EnKF), fusing unscented Kalman filter (UKF), and fusing extended Kalman filter (EKF) are investigated for fault diagnosis. Results demonstrate that all three filters can successfully detect, isolate, and accommodate sensor faults.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"264-276"},"PeriodicalIF":3.2,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125622","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

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