IEEE Transactions on Signal and Information Processing over Networks最新文献_第5页

Event-Triggered Distributed Cubature Kalman Filtering Algorithm With Stealthy Attacks Over Sensor Networks 具有传感器网络隐身攻击的事件触发分布式Cubature卡尔曼滤波算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-06 DOI: 10.1109/TSIPN.2025.3525977

Yinping Ma;Zhoujian Ma;Yinya Li;Yuan Liang

{"title":"Event-Triggered Distributed Cubature Kalman Filtering Algorithm With Stealthy Attacks Over Sensor Networks","authors":"Yinping Ma;Zhoujian Ma;Yinya Li;Yuan Liang","doi":"10.1109/TSIPN.2025.3525977","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3525977","url":null,"abstract":"This article investigates the security problem of distributed state estimation for nonlinear systems subject to stealthy attacks and limited energy. First, a novel detection strategy for a nonlinear information consensus filter is designed to resist the stealthy adversary which can modify the data transmitted through the wireless network. Unlike existing attack detection strategies, the proposed defense strategy is capable of simultaneously verifying the authenticity of the received local estimate and error covariance. Afterward, considering the limited communication resources, an event-triggered distributed cubature Kalman filtering algorithm with the aforementioned detection strategy is presented to fuse the local information. This algorithm can reduce communication consumptions and guarantee good estimation precision for sensor networks with stealthy attacks and limited energy. Subsequently, the stability properties of the developed nonlinear filtering algorithm are presented. Finally, two examples are given to demonstrate the effectiveness of the proposed approach.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"124-135"},"PeriodicalIF":3.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465593","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 Fixed-Time Convergent Distributed Algorithm for Time-Varying Optimal Resource Allocation Problem 时变资源最优分配问题的固定时间收敛分布式算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-12-18 DOI: 10.1109/TSIPN.2024.3511258

Zeng-Di Zhou;Ge Guo;Renyongkang Zhang

引用次数: 0

Memory-Enhanced Distributed Accelerated Algorithms for Coordinated Linear Computation 协调线性计算的内存增强分布式加速算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-12-12 DOI: 10.1109/TSIPN.2024.3511265

Shufen Ding;Deyuan Meng;Mingjun Du;Kaiquan Cai

{"title":"Memory-Enhanced Distributed Accelerated Algorithms for Coordinated Linear Computation","authors":"Shufen Ding;Deyuan Meng;Mingjun Du;Kaiquan Cai","doi":"10.1109/TSIPN.2024.3511265","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3511265","url":null,"abstract":"In this paper, a memory-enhanced distributed accelerated algorithm is proposed for solving large-scale systems of linear equations within the context of multi-agent systems. By employing a local predictor consisting of a linear combination of the nodes' current and previous values, the inclusion of two memory taps can be characterized such that the convergence of the distributed solution algorithm for coordinated computation is accelerated. Moreover, consensus-based convergence results are established by leveraging an analysis of the spectral radius of an augmented iterative matrix associated with the error system that arises from solving the equation. In addition, the connection between the convergence rate and the tunable parameters is developed through an examination of the spectral radius of the iterative matrix, and the optimal mixing parameter is systematically derived to achieve the fastest convergence rate. It is shown that despite whether the linear equation of interest possesses a unique solution or multiple solutions, the proposed distributed algorithm exhibits exponential convergence to the solution, without dependence on the initial conditions. In particular, both the theoretical analysis and simulation examples demonstrate that the proposed distributed algorithm can achieve a faster convergence rate than conventional distributed algorithms for the coordinated linear computation, provided that adjustable parameters are appropriately selected.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"35-47"},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890382","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

Auto-Weighted Multi-View Deep Non-Negative Matrix Factorization With Multi-Kernel Learning 基于多核学习的自加权多视图深度非负矩阵分解

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-12-04 DOI: 10.1109/TSIPN.2024.3511262

Xuanhao Yang;Hangjun Che;Man-Fai Leung;Cheng Liu;Shiping Wen

{"title":"Auto-Weighted Multi-View Deep Non-Negative Matrix Factorization With Multi-Kernel Learning","authors":"Xuanhao Yang;Hangjun Che;Man-Fai Leung;Cheng Liu;Shiping Wen","doi":"10.1109/TSIPN.2024.3511262","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3511262","url":null,"abstract":"Deep matrix factorization (DMF) has the capability to discover hierarchical structures within raw data by factorizing matrices layer by layer, allowing it to utilize latent information for superior clustering performance. However, DMF-based approaches face limitations when dealing with complex and nonlinear raw data. To address this issue, Auto-weighted Multi-view Deep Nonnegative Matrix Factorization with Multi-kernel Learning (MvMKDNMF) is proposed by incorporating multi-kernel learning into deep nonnegative matrix factorization. Specifically, samples are mapped into the kernel space which is a convex combination of several predefined kernels, free from selecting kernels manually. Furthermore, to preserve the local manifold structure of samples, a graph regularization is embedded in each view and the weights are assigned adaptively to different views. An alternate iteration algorithm is designed to solve the proposed model, and the convergence and computational complexity are also analyzed. Comparative experiments are conducted across nine multi-view datasets against seven state-of-the-art clustering methods showing the superior performances of the proposed MvMKDNMF.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"23-34"},"PeriodicalIF":3.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858875","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

Multi-Bit Distributed Detection of Sparse Stochastic Signals Over Error-Prone Reporting Channels 在易出错报告信道上对稀疏随机信号进行多比特分布式检测

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-20 DOI: 10.1109/TSIPN.2024.3496253

Linlin Mao;Shefeng Yan;Zeping Sui;Hongbin Li

{"title":"Multi-Bit Distributed Detection of Sparse Stochastic Signals Over Error-Prone Reporting Channels","authors":"Linlin Mao;Shefeng Yan;Zeping Sui;Hongbin Li","doi":"10.1109/TSIPN.2024.3496253","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3496253","url":null,"abstract":"We consider a distributed detection problem within a wireless sensor network (WSN), where a substantial number of sensors cooperate to detect the existence of sparse stochastic signals. To achieve a trade-off between detection performance and system constraints, multi-bit quantizers are employed at local sensors. Then, two quantization strategies, namely raw quantization (RQ) and likelihood ratio quantization (LQ), are examined. The multi-bit quantized signals undergo encoding into binary codewords and are subsequently transmitted to the fusion center via error-prone reporting channels. Upon exploiting the locally most powerful test (LMPT) strategy, we devise two multi-bit LMPT detectors in which quantized raw observations and local likelihood ratios are fused respectively. Moreover, the asymptotic detection performance of the proposed quantized detectors is analyzed, and closed-form expressions for the detection and false alarm probabilities are derived. Furthermore, the multi-bit quantizer design criterion, considering both RQ and LQ, is then proposed to achieve near-optimal asymptotic performance for our proposed detectors. The normalized Fisher information and asymptotic relative efficiency are derived, serving as tools to analyze and compensate for the loss of information introduced by the quantization. Simulation results validate the effectiveness of the proposed detectors, especially in scenarios with low signal-to-noise ratios and poor channel conditions.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"881-893"},"PeriodicalIF":3.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713876","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

Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks 高阶GNNs满足效率：稀疏Sobolev图神经网络

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-20 DOI: 10.1109/TSIPN.2024.3503416

Jhony H. Giraldo;Aref Einizade;Andjela Todorovic;Jhon A. Castro-Correa;Mohsen Badiey;Thierry Bouwmans;Fragkiskos D. Malliaros

{"title":"Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks","authors":"Jhony H. Giraldo;Aref Einizade;Andjela Todorovic;Jhon A. Castro-Correa;Mohsen Badiey;Thierry Bouwmans;Fragkiskos D. Malliaros","doi":"10.1109/TSIPN.2024.3503416","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3503416","url":null,"abstract":"Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the information from higher-order neighbors in the graph, involving the incorporation of powers of the shift operator, such as the graph Laplacian or adjacency matrix. This approach comes with a trade-off in terms of increased computational and memory demands. Relying on graph spectral theory, we make a fundamental observation: \u0000<italic>the regular and the Hadamard power of the Laplacian matrix behave similarly in the spectrum</i>\u0000. This observation has significant implications for capturing higher-order information in GNNs for various tasks such as node classification and semi-supervised learning. Consequently, we propose a novel graph convolutional operator based on the sparse Sobolev norm of graph signals. Our approach, known as Sparse Sobolev GNN (S2-GNN), employs Hadamard products between matrices to maintain the sparsity level in graph representations. S2-GNN utilizes a cascade of filters with increasing Hadamard powers to generate a diverse set of functions. We theoretically analyze the stability of S2-GNN to show the robustness of the model against possible graph perturbations. We also conduct a comprehensive evaluation of S2-GNN across various graph mining, semi-supervised node classification, and computer vision tasks. In particular use cases, our algorithm demonstrates competitive performance compared to state-of-the-art GNNs in terms of performance and running time.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"11-22"},"PeriodicalIF":3.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844519","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 Filtering for Nonlinear Systems on Basis of Nonuniform Samplings Subject to Envelope Constraints 基于包络约束的非均匀采样的非线性系统概率保证分布式滤波

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-15 DOI: 10.1109/TSIPN.2024.3496254

Wei Wang;Chen Hu;Lifeng Ma;Xiaojian Yi

{"title":"Probability-Guaranteed Distributed Filtering for Nonlinear Systems on Basis of Nonuniform Samplings Subject to Envelope Constraints","authors":"Wei Wang;Chen Hu;Lifeng Ma;Xiaojian Yi","doi":"10.1109/TSIPN.2024.3496254","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3496254","url":null,"abstract":"This paper investigates the probability-guaranteed distributed \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 filtering problem for stochastic time-varying systems over sensor networks. The measurements from sensing nodes are sampled nonuniformly before being received by filters and the sampling processes are modeled by a set of Markov chains. The purpose of the addressed problem is to design a distributed filter algorithm which meets the finite-horizon average \u0000<inline-formula><tex-math>$H_infty$</tex-math></inline-formula>\u0000 performance, meanwhile guaranteeing all filtering errors bounded within a prespecified envelope with a certain probability. Sufficient conditions for the feasibility of the mentioned filtering technique are established using convex optimization techniques. The desired filtering gains are subsequently determined by resolving the relevant matrix inequalities at each time step. Finally, the effectiveness of the proposed filtering algorithm is shown via an illustrative numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"905-915"},"PeriodicalIF":3.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736339","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

Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning With a Use-Case in Resource Allocation in Communication Networks 基于异步消息传递和零阶优化的分布式学习在通信网络资源分配中的应用

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-15 DOI: 10.1109/TSIPN.2024.3487421

Pourya Behmandpoor;Marc Moonen;Panagiotis Patrinos

{"title":"Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning With a Use-Case in Resource Allocation in Communication Networks","authors":"Pourya Behmandpoor;Marc Moonen;Panagiotis Patrinos","doi":"10.1109/TSIPN.2024.3487421","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3487421","url":null,"abstract":"Distributed learning and adaptation have received significant interest and found wide-ranging applications in machine learning and signal processing. While various approaches, such as shared-memory optimization, multi-task learning, and consensus-based learning (e.g., federated learning and learning over graphs), focus on optimizing either local costs or a global cost, there remains a need for further exploration of their interconnections. This paper specifically focuses on a scenario where agents collaborate towards a common task (i.e., optimizing a global cost equal to aggregated local costs) while effectively having distinct individual tasks (i.e., optimizing individual local parameters in a local cost). Each agent's actions can potentially impact other agents' performance through interactions. Notably, each agent has access to only its local zeroth-order oracle (i.e., cost function value) and shares scalar values, rather than gradient vectors, with other agents, leading to communication bandwidth efficiency and agent privacy. Agents employ zeroth-order optimization to update their parameters, and the asynchronous message-passing between them is subject to bounded but possibly random communication delays. This paper presents theoretical convergence analyses and establishes a convergence rate for nonconvex problems. Furthermore, it addresses the relevant use-case of deep learning-based resource allocation in communication networks and conducts numerical experiments in which agents, acting as transmitters, collaboratively train their individual policies to maximize a global reward, e.g., a sum of data rates.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"916-931"},"PeriodicalIF":3.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777758","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 Sequential State Estimation Over Binary Sensor Networks With Inaccurate Process Noise Covariance: A Variational Bayesian Framework 具有不准确过程噪声协方差的二值传感器网络上的分布式顺序状态估计：一个变分贝叶斯框架

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-13 DOI: 10.1109/TSIPN.2024.3497773

Jiayi Zhang;Guoliang Wei;Derui Ding;Yamei Ju

{"title":"Distributed Sequential State Estimation Over Binary Sensor Networks With Inaccurate Process Noise Covariance: A Variational Bayesian Framework","authors":"Jiayi Zhang;Guoliang Wei;Derui Ding;Yamei Ju","doi":"10.1109/TSIPN.2024.3497773","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3497773","url":null,"abstract":"In this paper, the distributed sequential state estimation problem is addressed for a class of discrete time-varying systems with inaccurate process noise covariance over binary sensor networks. First, with the purpose of reducing communication costs, a special class of sensors called binary sensors, which output only one bit of data, is adopted. The Gaussian tail function is then used to describe the likelihood of the binary measurements. Subsequently, the process noise covariance matrix is modeled as a inverse Wishart distribution. By employing a variational Bayesian approach combined with diffusion filtering strategies, the parameters (i.e., mean and variance) of the prior and posterior probability density functions are formalized for the sequential estimator and the sequential predictor. Then, the fixed-point iteration is utilized to receive the approximate optimal distributions of both system states and estimated covariance matrices. Finally, a simulation example of target tracking demonstrates that our algorithm performs effectively using binary measurement outputs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1-10"},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810383","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

Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks 网络上的可变步长扩散偏差补偿 APV 算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-11 DOI: 10.1109/TSIPN.2024.3496255

Fuyi Huang;Shuting Yang;Sheng Zhang;Haiqiang Chen;Pengwei Wen

引用次数: 0