Pub Date : 2024-01-10DOI: 10.1109/TSIPN.2024.3352267
Marzieh Rahimi;Mehdy Roayaei
Social networks (SN) have been one of the most important media for information diffusion in recent years. However, sometimes SN are used to spread rumors, which results in many social issues. Many researches have been done to detect rumors automatically. Previous works mostly exploit a single modality, especially the textual content, thus ignoring other modality such as the propagation structure and the interaction network of the rumor. However, the interaction network of users and tweets, and the propagation structure of a tweet, can provide important information to be used in rumor detection. In this paper, we propose a multi-view rumor detection framework (MV-RD) which captures multiple views of a tweet simultaneously including propagation structure, interaction network, and content. Previous works that considered propagation structure, mostly have used the final propagation structure at the end of information diffusion. Few related researchers have considered the dynamic evolution of propagation structures. In this paper, using partitioning of propagation structure over time, we have designed a propagation structure model that learns the evolution of the propagation structure of rumors over time. Besides, we take advantage of features of the rumor interaction network (modeling first-level interactions of tweets) for detecting rumors using the interaction network model. Also, a content model is learned to detect rumors using the tweet contents. Finally, these three models are fused into a unified framework. The results show the effectiveness of using multiple views in the rumor detection task. The proposed framework can detect rumors more effectively than other existing methods, even without using the tweet content. The proposed method achieved accuracies of 77.82%, 85.65%, and 88.26% by leveraging the propagation structure model alone, combining the propagation structure and interaction network models, and incorporating all three models, respectively. These results outperformed previous approaches and also demonstrated the method's capability to detect rumors earlier than existing methods.
{"title":"A Multi-View Rumor Detection Framework Using Dynamic Propagation Structure, Interaction Network, and Content","authors":"Marzieh Rahimi;Mehdy Roayaei","doi":"10.1109/TSIPN.2024.3352267","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352267","url":null,"abstract":"Social networks (SN) have been one of the most important media for information diffusion in recent years. However, sometimes SN are used to spread rumors, which results in many social issues. Many researches have been done to detect rumors automatically. Previous works mostly exploit a single modality, especially the textual content, thus ignoring other modality such as the propagation structure and the interaction network of the rumor. However, the interaction network of users and tweets, and the propagation structure of a tweet, can provide important information to be used in rumor detection. In this paper, we propose a multi-view rumor detection framework (MV-RD) which captures multiple views of a tweet simultaneously including propagation structure, interaction network, and content. Previous works that considered propagation structure, mostly have used the final propagation structure at the end of information diffusion. Few related researchers have considered the dynamic evolution of propagation structures. In this paper, using partitioning of propagation structure over time, we have designed a propagation structure model that learns the evolution of the propagation structure of rumors over time. Besides, we take advantage of features of the rumor interaction network (modeling first-level interactions of tweets) for detecting rumors using the interaction network model. Also, a content model is learned to detect rumors using the tweet contents. Finally, these three models are fused into a unified framework. The results show the effectiveness of using multiple views in the rumor detection task. The proposed framework can detect rumors more effectively than other existing methods, even without using the tweet content. The proposed method achieved accuracies of 77.82%, 85.65%, and 88.26% by leveraging the propagation structure model alone, combining the propagation structure and interaction network models, and incorporating all three models, respectively. These results outperformed previous approaches and also demonstrated the method's capability to detect rumors earlier than existing methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"48-58"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504532","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}
Pub Date : 2024-01-10DOI: 10.1109/TSIPN.2024.3352236
Xiang Zhang;Qiao Wang
In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting the underlying topological relationships between them. The difficulty lies in how to design a regularizer that accurately describes the intricate topological relationships, especially without prior knowledge. This problem becomes more challenging for the scenarios where data for different graphs are stored separately and prohibited from being transmitted to an unreliable central server due to privacy concerns. To address these issues, we propose a novel regularizer termed pattern graph to flexibly describe our priors on topological patterns. Theoretically, we provide the estimation error upper bound of the proposed graph estimator, which characterizes the impact of some factors on estimation errors. Furthermore, an approach that can automatically discover relationships among graphs is proposed to handle awkward situations without priors. On the algorithmic aspect, we develop a decentralized algorithm that updates each graph locally without sending the private data to a central server. Finally, extensive experiments on both synthetic and real data are carried out to validate the proposed method, and the results demonstrate that our framework outperforms the state-of-the-art methods.
{"title":"A Graph-Assisted Framework for Multiple Graph Learning","authors":"Xiang Zhang;Qiao Wang","doi":"10.1109/TSIPN.2024.3352236","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352236","url":null,"abstract":"In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting the underlying topological relationships between them. The difficulty lies in how to design a regularizer that accurately describes the intricate topological relationships, especially without prior knowledge. This problem becomes more challenging for the scenarios where data for different graphs are stored separately and prohibited from being transmitted to an unreliable central server due to privacy concerns. To address these issues, we propose a novel regularizer termed pattern graph to flexibly describe our priors on topological patterns. Theoretically, we provide the estimation error upper bound of the proposed graph estimator, which characterizes the impact of some factors on estimation errors. Furthermore, an approach that can automatically discover relationships among graphs is proposed to handle awkward situations without priors. On the algorithmic aspect, we develop a decentralized algorithm that updates each graph locally without sending the private data to a central server. Finally, extensive experiments on both synthetic and real data are carried out to validate the proposed method, and the results demonstrate that our framework outperforms the state-of-the-art methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"162-178"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738933","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}
Pub Date : 2024-01-10DOI: 10.1109/TSIPN.2024.3352271
Rui Tang;Wen Yang;Zhihai Rong;Chao Yang;Yang Tang
Since communication quality between sensors can directly affect distributed estimation, we consider the communication channel pricing and sensor scheduling problem for distributed estimation over a wireless sensor network with limited resources. Each sensor's choice of channels depends on its estimation performance and the channel communication cost which sets by a communication network server. Thus, there exists a tradeoff between the estimation accuracy and the channel communication cost. To solve this decision-making process, a Stackelberg game framework is builded, where the server firstly sets pricing strategy, then sensors schedule communication channels under limited resources. In this scenario, the existence of the optimal stationary decision-making process of sensors is provided after observing the server's stationary and deterministic pricing policy. Firstly, we analyze the impact of channel pricing on the convergence of the system. Then, the server's optimal pricing strategy is proposed after observing the sensors' channel scheduling policy under a Stackelberg game framework. The property of the equilibrium pair in the Stackelberg game framework is investigated and finally an optimal channel pricing and scheduling schemes based on the equilibrium pair is proposed. Finally, simulation results verify the optimality of the channel pricing and scheduling mechanisms.
{"title":"Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework","authors":"Rui Tang;Wen Yang;Zhihai Rong;Chao Yang;Yang Tang","doi":"10.1109/TSIPN.2024.3352271","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352271","url":null,"abstract":"Since communication quality between sensors can directly affect distributed estimation, we consider the communication channel pricing and sensor scheduling problem for distributed estimation over a wireless sensor network with limited resources. Each sensor's choice of channels depends on its estimation performance and the channel communication cost which sets by a communication network server. Thus, there exists a tradeoff between the estimation accuracy and the channel communication cost. To solve this decision-making process, a Stackelberg game framework is builded, where the server firstly sets pricing strategy, then sensors schedule communication channels under limited resources. In this scenario, the existence of the optimal stationary decision-making process of sensors is provided after observing the server's stationary and deterministic pricing policy. Firstly, we analyze the impact of channel pricing on the convergence of the system. Then, the server's optimal pricing strategy is proposed after observing the sensors' channel scheduling policy under a Stackelberg game framework. The property of the equilibrium pair in the Stackelberg game framework is investigated and finally an optimal channel pricing and scheduling schemes based on the equilibrium pair is proposed. Finally, simulation results verify the optimality of the channel pricing and scheduling mechanisms.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"59-68"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139573462","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}
Pub Date : 2024-01-10DOI: 10.1109/TSIPN.2024.3349792
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/TSIPN.2024.3349792","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3349792","url":null,"abstract":"","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"C2-C2"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10387487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419533","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}
Pub Date : 2024-01-01DOI: 10.1109/TSIPN.2023.3346994
Zhenzhen Pan;Ronghu Chi;Zhongsheng Hou
This work explores the challenging problems of nonlinear dynamics, nonaffine structures, heterogeneous properties, and deception attack together and proposes a novel distributed model-free adaptive predictive control (DMFAPC) for multiple-input-multiple-output (MIMO) multi-agent systems (MASs). A dynamic linearization method is introduced to address the nonlinear heterogeneous dynamics which is transformed as the unknown parameters in the obtained linear data model. A radial basis function neural network is designed to detect the deception attack and to estimate the polluted output that is further used in the controller design to compensate for the effect. Then, the DMFAPC is designed by defining a new expanded distributed output with a stochastic factor introduced. The bounded convergence is proved by using the contraction mapping method and the effectiveness of the proposed DMFAPC is verified by simulation examples.
{"title":"Distributed Model-Free Adaptive Predictive Control for MIMO Multi-Agent Systems With Deception Attack","authors":"Zhenzhen Pan;Ronghu Chi;Zhongsheng Hou","doi":"10.1109/TSIPN.2023.3346994","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3346994","url":null,"abstract":"This work explores the challenging problems of nonlinear dynamics, nonaffine structures, heterogeneous properties, and deception attack together and proposes a novel distributed model-free adaptive predictive control (DMFAPC) for multiple-input-multiple-output (MIMO) multi-agent systems (MASs). A dynamic linearization method is introduced to address the nonlinear heterogeneous dynamics which is transformed as the unknown parameters in the obtained linear data model. A radial basis function neural network is designed to detect the deception attack and to estimate the polluted output that is further used in the controller design to compensate for the effect. Then, the DMFAPC is designed by defining a new expanded distributed output with a stochastic factor introduced. The bounded convergence is proved by using the contraction mapping method and the effectiveness of the proposed DMFAPC is verified by simulation examples.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"32-47"},"PeriodicalIF":3.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406690","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}
Generalized Edge-Consensus Learning (G-ECL) is a primal-dual method to solve loss-sum minimization problems. We propose Local Generalized Edge-Consensus Learning (Local G-ECL) as an extension of previous G-ECL, aiming to be a decentralized/centralized FL algorithm robust to heterogeneous data sets with a large number of local updates. Our contributions are as follows: (C1) success in theoretical gradient norm convergence analysis nearly independently of data heterogeneity, and (C2) equivalency proof between our primal-dual Local G-ECL and a pure primal Stochastic Controlled Averaging (SCAFFOLD) algorithm in centralized settings, where the difference in the initial local model for each round is ignored. Numerical experiments using image classification tests validated that Local G-ECL is robust to heterogeneous data with a large number of local updates.
{"title":"A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity","authors":"Iifan Tyou;Tomoya Murata;Takumi Fukami;Yuki Takezawa;Kenta Niwa","doi":"10.1109/TSIPN.2023.3343616","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3343616","url":null,"abstract":"Generalized Edge-Consensus Learning (G-ECL) is a primal-dual method to solve loss-sum minimization problems. We propose Local Generalized Edge-Consensus Learning (Local G-ECL) as an extension of previous G-ECL, aiming to be a decentralized/centralized FL algorithm robust to heterogeneous data sets with a large number of local updates. Our contributions are as follows: (C1) success in theoretical gradient norm convergence analysis nearly independently of data heterogeneity, and (C2) equivalency proof between our primal-dual Local G-ECL and a pure primal Stochastic Controlled Averaging (SCAFFOLD) algorithm in centralized settings, where the difference in the initial local model for each round is ignored. Numerical experiments using image classification tests validated that Local G-ECL is robust to heterogeneous data with a large number of local updates.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"94-107"},"PeriodicalIF":3.2,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10373878","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654438","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}
Pub Date : 2023-12-20DOI: 10.1109/TSIPN.2023.3345142
Karelia Pena-Pena;Lucas Taipe;Fuli Wang;Daniel L. Lau;Gonzalo R. Arce
Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing. However, a meaningful hypergraph may only be available in specific cases. This paper addresses the challenge of learning the underlying hypergraph topology from the data itself. As in graph signal processing applications, we consider the case in which the data possesses certain regularity or smoothness on the hypergraph. To this end, our method builds on the novel tensor-based hypergraph signal processing framework (t-HGSP) that has recently emerged as a powerful tool for preserving the intrinsic high-order structure of data on hypergraphs. Given the hypergraph spectrum and frequency coefficient definitions within the t-HGSP framework, we propose a method to learn the hypergraph Laplacian from data by minimizing the total variation on the hypergraph (TVL-HGSP). Additionally, we introduce an alternative approach (PDL-HGSP) that improves the connectivity of the learned hypergraph without compromising sparsity and use primal-dual-based algorithms to reduce the computational complexity. Finally, we combine the proposed learning algorithms with novel tensor-based hypergraph convolutional neural networks to propose hypergraph learning-convolutional neural networks (t-HyperGLNN).
{"title":"Learning Hypergraphs Tensor Representations From Data via t-HGSP","authors":"Karelia Pena-Pena;Lucas Taipe;Fuli Wang;Daniel L. Lau;Gonzalo R. Arce","doi":"10.1109/TSIPN.2023.3345142","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3345142","url":null,"abstract":"Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing. However, a meaningful hypergraph may only be available in specific cases. This paper addresses the challenge of learning the underlying hypergraph topology from the data itself. As in graph signal processing applications, we consider the case in which the data possesses certain regularity or smoothness on the hypergraph. To this end, our method builds on the novel tensor-based hypergraph signal processing framework (t-HGSP) that has recently emerged as a powerful tool for preserving the intrinsic high-order structure of data on hypergraphs. Given the hypergraph spectrum and frequency coefficient definitions within the t-HGSP framework, we propose a method to learn the hypergraph Laplacian from data by minimizing the total variation on the hypergraph (TVL-HGSP). Additionally, we introduce an alternative approach (PDL-HGSP) that improves the connectivity of the learned hypergraph without compromising sparsity and use primal-dual-based algorithms to reduce the computational complexity. Finally, we combine the proposed learning algorithms with novel tensor-based hypergraph convolutional neural networks to propose hypergraph learning-convolutional neural networks (t-HyperGLNN).","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"17-31"},"PeriodicalIF":3.2,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399599","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}
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the embedding task of estimating these vectors from an observed graph is typically posed as a low-rank matrix factorization problem. The workhorse Adjacency Spectral Embedding (ASE) enjoys solid statistical properties, but it is formally solving a surrogate problem and can be computationally intensive. In this paper, we bring to bear recent advances in non-convex optimization and demonstrate their impact to RDPG inference. We advocate first-order gradient descent methods to better solve the embedding problem, and to organically accommodate broader network embedding applications of practical relevance. Notably, we argue that RDPG embeddings of directed graphs loose interpretability unless the factor matrices are constrained to have orthogonal columns. We thus develop a novel feasible optimization method in the resulting manifold. The effectiveness of the graph representation learning framework is demonstrated on reproducible experiments with both synthetic and real network data. Our open-source algorithm implementations are scalable, and unlike the ASE they are robust to missing edge data and can track slowly-varying latent positions from streaming graphs.
随机点积图（RDPG）是一种关系数据生成模型，节点通过低维欧几里得空间中的潜在向量表示。RDPG 的关键假设是，边缘形成概率由相应的潜在位置的点积给出。因此，从观测图中估算这些向量的嵌入任务通常被视为低阶矩阵因式分解问题。主要的邻接谱嵌入（ASE）具有可靠的统计特性，但它在形式上解决的是一个代理问题，而且计算量很大。在本文中，我们将介绍非凸优化的最新进展，并展示它们对 RDPG 推断的影响。我们提倡一阶梯度下降方法，以更好地解决嵌入问题，并有机地适应更广泛的实际网络嵌入应用。值得注意的是，我们认为除非约束因子矩阵具有正交列，否则有向图的 RDPG 嵌入会降低可解释性。因此，我们在由此产生的流形中开发了一种新的可行优化方法。我们利用合成和真实网络数据进行了可重复实验，证明了图表示学习框架的有效性。我们的开源算法实现具有可扩展性，而且与 ASE 不同，它们对缺失的边缘数据具有鲁棒性，并能跟踪流图中缓慢变化的潜在位置。
{"title":"Gradient-Based Spectral Embeddings of Random Dot Product Graphs","authors":"Marcelo Fiori;Bernardo Marenco;Federico Larroca;Paola Bermolen;Gonzalo Mateos","doi":"10.1109/TSIPN.2023.3343607","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3343607","url":null,"abstract":"The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the \u0000<italic>embedding</i>\u0000 task of estimating these vectors from an observed graph is typically posed as a low-rank matrix factorization problem. The workhorse Adjacency Spectral Embedding (ASE) enjoys solid statistical properties, but it is formally solving a surrogate problem and can be computationally intensive. In this paper, we bring to bear recent advances in non-convex optimization and demonstrate their impact to RDPG inference. We advocate first-order gradient descent methods to better solve the embedding problem, and to organically accommodate broader network embedding applications of practical relevance. Notably, we argue that RDPG embeddings of directed graphs loose interpretability unless the factor matrices are constrained to have orthogonal columns. We thus develop a novel feasible optimization method in the resulting manifold. The effectiveness of the graph representation learning framework is demonstrated on reproducible experiments with both synthetic and real network data. Our open-source algorithm implementations are scalable, and unlike the ASE they are robust to missing edge data and can track slowly-varying latent positions from streaming graphs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"1-16"},"PeriodicalIF":3.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109661","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}
Pub Date : 2023-12-13DOI: 10.1109/TSIPN.2023.3341406
Bo Meng;Ke Zhang;Bin Jiang
This article aims to propose a novel fixed-time distributed optimized formation control scheme for fixed-wing unmanned aerial vehicles with uncertainties, communication link and actuator faults, and performance constraint. Firstly, the prescribed performance function is introduced to improve the steady-state and transient performances of fixed-wing UAVs system. Communication link faults are tolerated by utilizing the distributed leader state observer. Subsequently, with the objective of establishing optimal controllers for velocity and altitude subsystems, the reinforcement learning control method is employed. Simultaneously, an intermediate controller is constructed to tackle the difficulties in applying reinforcement learning to the fault-tolerant control scheme. In addition, new adaptive laws of fault factor parameters are proposed, which can make the fault-tolerant scheme align better with the concept of fixed-time convergence. Finally, fixed-time prescribed performance controllers for velocity and altitude subsystems are developed. The designed control algorithm can ensure that the velocity and altitude tracking errors converge to the prescribed region, and the simulation results further demonstrate that the proposed control strategy is effective.
{"title":"Fixed-Time Optimal Fault-Tolerant Formation Control With Prescribed Performance for Fixed-Wing UAVs Under Dual Faults","authors":"Bo Meng;Ke Zhang;Bin Jiang","doi":"10.1109/TSIPN.2023.3341406","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3341406","url":null,"abstract":"This article aims to propose a novel fixed-time distributed optimized formation control scheme for fixed-wing unmanned aerial vehicles with uncertainties, communication link and actuator faults, and performance constraint. Firstly, the prescribed performance function is introduced to improve the steady-state and transient performances of fixed-wing UAVs system. Communication link faults are tolerated by utilizing the distributed leader state observer. Subsequently, with the objective of establishing optimal controllers for velocity and altitude subsystems, the reinforcement learning control method is employed. Simultaneously, an intermediate controller is constructed to tackle the difficulties in applying reinforcement learning to the fault-tolerant control scheme. In addition, new adaptive laws of fault factor parameters are proposed, which can make the fault-tolerant scheme align better with the concept of fixed-time convergence. Finally, fixed-time prescribed performance controllers for velocity and altitude subsystems are developed. The designed control algorithm can ensure that the velocity and altitude tracking errors converge to the prescribed region, and the simulation results further demonstrate that the proposed control strategy is effective.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"875-887"},"PeriodicalIF":3.2,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050589","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}
Pub Date : 2023-12-12DOI: 10.1109/TSIPN.2023.3341407
Juan Augusto Maya;Leonardo Rey Vega;Andrea M. Tonello
In this article, we tackle the problem of distributed detection of a radio source emitting a signal. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively a statistic to decide if the source is present or absent. We model the radio source as a stochastic signal and work with spatially statistically dependent measurements. We consider the Generalized Likelihood Ratio Test (GLRT) approach to deal with an unknown multidimensional parameter from the model. We analytically characterize the asymptotic distribution of the statistic when the amount of sensor measurements tends to infinity. Moreover, as the GLRT is not amenable for distributed settings because of the spatial statistical dependence of the measurements, we study a GLRT-like test where the statistical dependence is completely discarded for building this test. Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistical dependence of the measurements has no impact on the detection performance in the asymptotic scenario. Furthermore, the GLRT-like algorithm has a low computational complexity and demands low communication resources, as compared to the GLRT.
{"title":"An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks","authors":"Juan Augusto Maya;Leonardo Rey Vega;Andrea M. Tonello","doi":"10.1109/TSIPN.2023.3341407","DOIUrl":"https://doi.org/10.1109/TSIPN.2023.3341407","url":null,"abstract":"In this article, we tackle the problem of distributed detection of a radio source emitting a signal. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively a statistic to decide if the source is present or absent. We model the radio source as a stochastic signal and work with spatially statistically dependent measurements. We consider the Generalized Likelihood Ratio Test (GLRT) approach to deal with an unknown multidimensional parameter from the model. We analytically characterize the asymptotic distribution of the statistic when the amount of sensor measurements tends to infinity. Moreover, as the GLRT is not amenable for distributed settings because of the spatial statistical dependence of the measurements, we study a GLRT-like test where the statistical dependence is completely discarded for building this test. Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistical dependence of the measurements has no impact on the detection performance in the asymptotic scenario. Furthermore, the GLRT-like algorithm has a low computational complexity and demands low communication resources, as compared to the GLRT.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"888-900"},"PeriodicalIF":3.2,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10354397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050588","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}
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