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

Fairness With Low Resentment in Distributed Sensor Systems to Detect Emitters 分布式传感器系统中检测发射器的低怨恨公平性

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-13 DOI: 10.1109/TSIPN.2024.3414146

Benedito J. B. Fonseca

{"title":"Fairness With Low Resentment in Distributed Sensor Systems to Detect Emitters","authors":"Benedito J. B. Fonseca","doi":"10.1109/TSIPN.2024.3414146","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3414146","url":null,"abstract":"Consider a single distributed sensor system to detect the occurrence of rare emitters in multiple regions, each representing a different community. Alarms are sent to a common dispatch center, which dispatches units to each alarmed community. We assume that all communities contribute equally to the cost of the system; however, the probability of detecting an emitter may vary among communities, raising the issue of fairness. We adopt in here the concept of envy-free fairness in which the goal is to equalize the worst-case probability of detection in each community. As shown in our previous work, envy-free fairness can be achieved by adjusting the probabilities of false alarm at each community. In here, we extend our results by addressing a concern that may arise from envy-free fairness: resentment. After precisely defining the concept of resentment, we show that it is possible to design an envy-free fair detection system while keeping the maximum resentment bounded by combining poorly-served communities with a high enough number of well-served communities. We also present algorithms to allocate sensors to communities to design envy-free fair systems with bounded resentment while considering different optimization goals and constraints. Our examples illustrate that our algorithms often produce close-to-optimum allocations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"552-564"},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448032","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

Tensor-Derived Large-Scale Multi-View Subspace Clustering With Faithful Semantics 具有忠实语义的张量推导大规模多视图子空间聚类技术

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-13 DOI: 10.1109/TSIPN.2024.3414134

Sujia Huang;Shide Du;Lele Fu;Zhihao Wu;Shiping Wang

{"title":"Tensor-Derived Large-Scale Multi-View Subspace Clustering With Faithful Semantics","authors":"Sujia Huang;Shide Du;Lele Fu;Zhihao Wu;Shiping Wang","doi":"10.1109/TSIPN.2024.3414134","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3414134","url":null,"abstract":"Multi-view subspace clustering is extensively investigated for its ability to extract essential information from multiple data. However, tensor-based methods often encounter several limitations: 1) They suffer from high computational complexity due to the construction of a global affinity matrix; 2) The sophisticated semantic information among samples remains under-explored. To address these issues, we propose a comprehensive framework called tensor-derived large-scale multi-view subspace clustering with faithful semantics, which replaces the original graph with a trustworthy anchor graph. In particular, a graph-optimization-based anchor selection strategy is designed to obtain salient points, and thus the anchor graph is computed to decrease the computational complexity of constructing the representation matrix. Subsequently, a refinement approach is designed to flexibly extract essential semantics between nodes by dividing the graph into significant components and undesired connections. These matrices preserving important information are fused into a tensor that is constrained by a nuclear norm to retain its low-rank property. Meanwhile, the undesired links should be eliminated to avoid confusing the clustering results. Finally, the spectral embedding is employed to directly guide the learning of anchors and graphs. The proposed model achieves a remarkable improvement of 3.3% and 13.1% of ACC on the NoisyMNIST and Prokaryotic datasets while reducing high computational complexity compared to other subspace-based clustering approaches.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"584-598"},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543990","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 Proximal Alternating Direction Method of Multipliers for Constrained Composite Optimization Over Directed Networks 用于有向网络上受限复合优化的分布式近端交替方向乘法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-06-03 DOI: 10.1109/TSIPN.2024.3407660

Jing Yan;Xinli Shi;Luyao Guo;Ying Wan;Guanghui Wen

{"title":"Distributed Proximal Alternating Direction Method of Multipliers for Constrained Composite Optimization Over Directed Networks","authors":"Jing Yan;Xinli Shi;Luyao Guo;Ying Wan;Guanghui Wen","doi":"10.1109/TSIPN.2024.3407660","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3407660","url":null,"abstract":"In this article, we investigate a constrained composition optimization problem in a directed communication network. Each agent is equipped with a local objective function composed of both smooth and nonsmooth terms, as well as linear equality constraints. The optimization objective is to minimize the sum of all local functions, subject to linear equality constraints, through local computations and information exchange with neighboring agents. Based on the alternating direction method of multipliers (ADMM), a novel distributed optimization algorithm is proposed to address the composite optimization problem. We leverage the composite structure of the objective function, by introducing a linear approximation for the smooth term and a proximal mapping for the nonsmooth term, which simplifies the process of solving the ADMM subproblem. Furthermore, in contrast to the existing algorithms that eliminate the imbalance resulting from directed graphs using a column-stochastic matrix, the proposed algorithm only employs a row-stochastic matrix, thereby avoiding the need for agents to know their outdegree. Moreover, the step sizes of agents are uncoordinated and can be independent of the network topology. Furthermore, we prove that the proposed algorithm achieves a sublinear convergence rate when the local objective functions are convex. Finally, the effectiveness of the proposed algorithm is verified through numerical simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"539-551"},"PeriodicalIF":3.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308640","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

Natural Gradient Primal-Dual Method for Decentralized Learning 用于分散学习的自然梯度原始双法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-26 DOI: 10.1109/TSIPN.2024.3388948

Kenta Niwa;Hiro Ishii;Hiroshi Sawada;Akinori Fujino;Noboru Harada;Rio Yokota

{"title":"Natural Gradient Primal-Dual Method for Decentralized Learning","authors":"Kenta Niwa;Hiro Ishii;Hiroshi Sawada;Akinori Fujino;Noboru Harada;Rio Yokota","doi":"10.1109/TSIPN.2024.3388948","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3388948","url":null,"abstract":"We propose the Natural Gradient Primal-Dual (NGPD) method for decentralized learning of parameters in Deep Neural Networks (DNNs). Conventional approaches, such as the primal-dual method, constrain the local parameters to be similar between connected nodes. However, since most of them follow a first-order optimization method and the loss functions of DNNs may have ill-conditioned curvatures, many local parameter updates and communication among local nodes are needed. For fast convergence, we integrate the second-order natural gradient method into the primal-dual method (NGPD). Since additional constraint minimizes the amount of output change before and after the parameter updates, robustness towards ill-conditioned curvatures is expected. We theoretically demonstrate the convergence rate for the averaged parameter (the average of the local parameters) under certain assumptions. As a practical implementation of NGPD without a significant increase in computational overheads, we introduce Kronecker Factored Approximate Curvature (K-FAC). Our experimental results confirmed that NGPD achieved the highest test accuracy through image classification tasks using DNNs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"417-433"},"PeriodicalIF":3.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807296","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

Mixed Static-Dynamic Protocol-Based Tobit Recursive Filtering for Stochastic Nonlinear Systems Against Random False Data Injection Attacks 基于静态-动态混合协议的随机非线性系统托比特递归过滤，对抗随机虚假数据注入攻击

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-22 DOI: 10.1109/TSIPN.2024.3388953

Jun Hu;Shuo Yang;Raquel Caballero-Águila;Hongli Dong;Boying Wu

{"title":"Mixed Static-Dynamic Protocol-Based Tobit Recursive Filtering for Stochastic Nonlinear Systems Against Random False Data Injection Attacks","authors":"Jun Hu;Shuo Yang;Raquel Caballero-Águila;Hongli Dong;Boying Wu","doi":"10.1109/TSIPN.2024.3388953","DOIUrl":"10.1109/TSIPN.2024.3388953","url":null,"abstract":"In this paper, the Tobit recursive filtering (TRF) issue is discussed for a class of time-varying stochastic nonlinear systems (SNSs) with censored measurements and random false data injection attacks (FDIAs) under the mixed static-dynamic protocol. The censored measurements considered are depicted by the Tobit Type I model and the phenomenon of the random FDIAs involved is governed by a set of Bernoulli random variables. Additionally, in order to reduce the communication burden and improve the data utilization efficiency, the mixed static-dynamic protocol is elaborately adopted to schedule the signal transmission, which is managed by the time-triggered and event-triggered rules to further increase the flexibility of the data scheduling. The main goal of this paper is to present a new TRF approach such that, in the presence of censored measurements, mixed static-dynamic protocol and random FDIAs, a minimized upper bound of the filtering error covariance (FEC) can be obtained. Moreover, a sufficient criterion from the theoretical analysis perspective is established to guarantee the desired uniform boundedness of the filtering error in the mean-square sense (MSS). Finally, some experiments with comparisons applicable for three-wheeled Ackerman turning model are conducted to show the applicability and advantages of newly proposed TRF scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"445-459"},"PeriodicalIF":3.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140634204","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

Median-Based Resilient Multi-Object Fusion With Application to LMB Densities 基于中值的弹性多目标融合技术在 LMB 密度中的应用

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-17 DOI: 10.1109/TSIPN.2024.3388951

Yao Zhou;Giorgio Battistelli;Luigi Chisci;Lin Gao;Gaiyou Li;Ping Wei

引用次数: 0

Causal Inference From Slowly Varying Nonstationary Processes 从缓慢变化的非平稳过程中进行因果推理

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-12 DOI: 10.1109/TSIPN.2024.3375594

Kang Du;Yu Xiang

引用次数: 0

Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications 具有异步通信功能的合作-竞争网络上的多代理双方成群控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-03 DOI: 10.1109/TSIPN.2024.3384817

Zhuangzhuang Ma;Lei Shi;Kai Chen;Jinliang Shao;Yuhua Cheng

{"title":"Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications","authors":"Zhuangzhuang Ma;Lei Shi;Kai Chen;Jinliang Shao;Yuhua Cheng","doi":"10.1109/TSIPN.2024.3384817","DOIUrl":"10.1109/TSIPN.2024.3384817","url":null,"abstract":"In this contribution, the bipartite flocking control problem of a set of autonomous mobile agents over cooperation-competition networks is investigated. Two kinds of asynchronous communication scenarios are considered, where each agent communicates with the neighbors only at certain time instants determined by its own clock, but not at other time instants. In addition, each agent adjusts the control input at all time instants in the first asynchronous scenario, and adjusts the control input only at its communication time instants in the second asynchronous scenario. Nonlinear positive and negative weight functions are designed to describe the effect of the distance between agents on the cooperation/competition degree in real interaction scenarios, where the farther (closer) the distance, the weaker (stronger) the cooperation/competition degree. With the help of signed graph theory and sub-stochastic matrix, the dynamic models under different asynchronous scenarios are analyzed, and the algebraic conditions for achieving bipartite flocking control are established separately. At last, the effectiveness of algebraic conditions is verified through numerical simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"460-472"},"PeriodicalIF":3.2,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590750","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 Event-Triggered Fault-Tolerant Consensus Control of Multi-Agent Systems Under DoS Attacks 多代理系统在 DoS 攻击下的分布式事件触发容错共识控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-03 DOI: 10.1109/TSIPN.2024.3384814

Chun Liu;Bin Jiang;Yang Li;Ron J. Patton

引用次数: 0

Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances 具有异质不匹配干扰的一般线性多代理系统的复合输出共识控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-27 DOI: 10.1109/TSIPN.2024.3382427

Pan Yu;Yifan Ding;Kang-Zhi Liu;Xiaoli Li

{"title":"Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances","authors":"Pan Yu;Yifan Ding;Kang-Zhi Liu;Xiaoli Li","doi":"10.1109/TSIPN.2024.3382427","DOIUrl":"10.1109/TSIPN.2024.3382427","url":null,"abstract":"This paper develops a composite output consensus control protocol for a general linear multiagent system subject to mismatched disturbances, which incorporates active disturbance-rejection control and fully distributed adaptive consensus control. To estimate and then cancel out the effect of mismatched disturbances on the outputs of the agents, heterogeneous generalized equivalent-input-disturbance estimators are constructed in the inner loop. Then a fully distributed adaptive feedback controller is designed to achieve consensus control based on the states of the designed heterogeneous observers for the agents. The restriction on the disturbances is lowered, the requirement for the global information of the communication topology is removed, and the exchanging information among agents is only relative estimated states. Further, the output consensus performance is analyzed for the closed-loop multiagent system. Our results complement and improve the results of the existing literature. Lastly, the effectiveness and superiority of the developed method are demonstrated through a numerical simulation and a comparison with the distributed extended-state-observer-based method.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"434-444"},"PeriodicalIF":3.2,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313490","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