Pub Date : 2024-03-11DOI: 10.1109/TSIPN.2024.3375612
Jia Deng;Fuyong Wang;Zhongxin Liu;Zengqiang Chen
This article is concerned with the fully distributed consensus control problem of a class of disturbed general linear multi-agent systems under event-triggered communication. Different from existing works, the disturbances considered in this article are more practical and complex. Each agent is subject to disturbances generated by exosystems and each exosystem is considered to exist with possible modelling errors. First, a local disturbance observer is designed for each agent to compensate potentially unbounded external disturbances to a bounded situation, but the value of this bound is not accessible because the upper bound of the modelling error is unknown. Second, an adaptive consensus control law with complete disturbance rejection is further proposed, by which the consensus error converges to zero over time. Third, with limited communication resources, an event-triggered communication mechanism is designed for deciding when an agent broadcasts information, which effectively saves communication resources while ensuring that the original control goal is achieved. In addition, it is demonstrated that Zeno behaviour is excluded. Finally, the correctness of the theoretical results is verified by a simulation example.
{"title":"Fully Distributed Consensus Control for a Class of Disturbed Linear Multi-Agent Systems Over Event-Triggered Communication","authors":"Jia Deng;Fuyong Wang;Zhongxin Liu;Zengqiang Chen","doi":"10.1109/TSIPN.2024.3375612","DOIUrl":"10.1109/TSIPN.2024.3375612","url":null,"abstract":"This article is concerned with the fully distributed consensus control problem of a class of disturbed general linear multi-agent systems under event-triggered communication. Different from existing works, the disturbances considered in this article are more practical and complex. Each agent is subject to disturbances generated by exosystems and each exosystem is considered to exist with possible modelling errors. First, a local disturbance observer is designed for each agent to compensate potentially unbounded external disturbances to a bounded situation, but the value of this bound is not accessible because the upper bound of the modelling error is unknown. Second, an adaptive consensus control law with complete disturbance rejection is further proposed, by which the consensus error converges to zero over time. Third, with limited communication resources, an event-triggered communication mechanism is designed for deciding when an agent broadcasts information, which effectively saves communication resources while ensuring that the original control goal is achieved. In addition, it is demonstrated that Zeno behaviour is excluded. Finally, the correctness of the theoretical results is verified by a simulation example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"205-215"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.1109/TSIPN.2024.3375605
Xiaoxian Lao;Chunguang Li
Distributed estimation has attracted great attention in the last few decades. In the problem of distributed estimation, a set of nodes estimate some parameter from noisy measurements. To leverage joint effort, the nodes communicate with each other in the estimation process. The communications consume bandwidth and energy resources, and these resources are often limited in real-world applications. To cope with the resources constraints, the event-triggered mechanism is proposed and widely adopted. It only allows signals to be transmitted if they carry significant amount of information. Various criteria of determining whether the information is significant lead to different trigger rules. With these rules, the resources can be saved. However, in the meanwhile, some inter-event information, not that important but still of certain use, is unavailable to the neighbors. The absence of these inter-event information may affect the algorithm performance. Considering this, in this paper, we come up with an inter-event information retrieval scheme to recover certain untransmitted information, which is the first work doing so to the best of our knowledge. We design an approach for inter-event information retrieval, and formulate and solve an optimization problem which has a closed-form solution to acquire information. With more information at hand, the performance degeneration caused by the event-triggered mechanism can be alleviated. We derive sufficient conditions for convergence of the overall algorithm. We also demonstrate the advantages of the proposed scheme by simulation experiments.
{"title":"Event-Triggered Distributed Estimation With Inter-Event Information Retrieval","authors":"Xiaoxian Lao;Chunguang Li","doi":"10.1109/TSIPN.2024.3375605","DOIUrl":"10.1109/TSIPN.2024.3375605","url":null,"abstract":"Distributed estimation has attracted great attention in the last few decades. In the problem of distributed estimation, a set of nodes estimate some parameter from noisy measurements. To leverage joint effort, the nodes communicate with each other in the estimation process. The communications consume bandwidth and energy resources, and these resources are often limited in real-world applications. To cope with the resources constraints, the event-triggered mechanism is proposed and widely adopted. It only allows signals to be transmitted if they carry significant amount of information. Various criteria of determining whether the information is significant lead to different trigger rules. With these rules, the resources can be saved. However, in the meanwhile, some inter-event information, not that important but still of certain use, is unavailable to the neighbors. The absence of these inter-event information may affect the algorithm performance. Considering this, in this paper, we come up with an inter-event information retrieval scheme to recover certain untransmitted information, which is the first work doing so to the best of our knowledge. We design an approach for inter-event information retrieval, and formulate and solve an optimization problem which has a closed-form solution to acquire information. With more information at hand, the performance degeneration caused by the event-triggered mechanism can be alleviated. We derive sufficient conditions for convergence of the overall algorithm. We also demonstrate the advantages of the proposed scheme by simulation experiments.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"253-263"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-10DOI: 10.1109/TSIPN.2024.3399558
Mor Oren-Loberman;Vered Azar;Wasim Huleihel
Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society. In this paper, we consider the problem of online auditing of information flow/propagation with the goal of classifying news items as fake or genuine. Specifically, driven by experiential studies on real-world social media platforms, we propose a probabilistic Markovian information spread model over networks modeled by graphs. We then formulate our inference task as a certain sequential detection problem with the goal of minimizing the combination of the error probability and the time it takes to achieve the correct decision. For this model, we find the optimal detection algorithm minimizing the aforementioned risk and prove several statistical guarantees. We then test our algorithm over real-world datasets. To that end, we first construct an offline algorithm for learning the probabilistic information spreading model, and then apply our optimal detection algorithm. Experimental study show that our algorithm outperforms state-of-the-art misinformation detection algorithms in terms of accuracy and detection time.
{"title":"Online Auditing of Information Flow","authors":"Mor Oren-Loberman;Vered Azar;Wasim Huleihel","doi":"10.1109/TSIPN.2024.3399558","DOIUrl":"10.1109/TSIPN.2024.3399558","url":null,"abstract":"Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifiable facts on social media platforms propagate disharmony and affect society. In this paper, we consider the problem of online auditing of information flow/propagation with the goal of classifying news items as fake or genuine. Specifically, driven by experiential studies on real-world social media platforms, we propose a probabilistic Markovian information spread model over networks modeled by graphs. We then formulate our inference task as a certain sequential detection problem with the goal of minimizing the combination of the error probability and the time it takes to achieve the correct decision. For this model, we find the optimal detection algorithm minimizing the aforementioned risk and prove several statistical guarantees. We then test our algorithm over real-world datasets. To that end, we first construct an offline algorithm for learning the probabilistic information spreading model, and then apply our optimal detection algorithm. Experimental study show that our algorithm outperforms state-of-the-art misinformation detection algorithms in terms of accuracy and detection time.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"487-499"},"PeriodicalIF":3.2,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1109/TSIPN.2024.3364613
Liang Ran;Huaqing Li;Lifeng Zheng;Jun Li;Zhe Li;Jinhui Hu
This article investigates the generalized Nash equilibria (GNE) seeking problem for noncooperative games, where all players dedicate to selfishly minimizing their own cost functions subject to local constraints and coupled constraints. To tackle the considered problem, we initially form an explicit local equilibrium condition for its variational formulation. By employing proximal splitting operators, a novel distributed primal-dual splitting algorithm with full-decision information (Dist_PDS_FuDeIn) is designed, eliminating the need for global step-sizes. Furthermore, to address scenarios where players lack access to all other players' decisions, a local estimation is introduced to approximate the decision information of other players, and a fully distributed primal-dual splitting algorithm with partial-decision information (Dist_PDS_PaDeIn) is then proposed. Both algorithms enable the derivation of new distributed forward-backward-like extensions. Theoretically, a new analytical approach for convergence is presented, demonstrating that the proposed algorithms converge to the variational GNE of games, and their convergence rates are also proven, provided that uncoordinated step-sizes are positive and less than explicit upper bounds. Moreover, the approach not only generalizes the forward-backward splitting technique but also improves convergence rates of several well-known algorithms. Finally, the advantages of Dist_PDS_FuDeIn and Dist_PDS_PaDeIn are illustrated through comparative simulations.
{"title":"Distributed Generalized Nash Equilibria Computation of Noncooperative Games Via Novel Primal-Dual Splitting Algorithms","authors":"Liang Ran;Huaqing Li;Lifeng Zheng;Jun Li;Zhe Li;Jinhui Hu","doi":"10.1109/TSIPN.2024.3364613","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3364613","url":null,"abstract":"This article investigates the generalized Nash equilibria (GNE) seeking problem for noncooperative games, where all players dedicate to selfishly minimizing their own cost functions subject to local constraints and coupled constraints. To tackle the considered problem, we initially form an explicit local equilibrium condition for its variational formulation. By employing proximal splitting operators, a novel distributed primal-dual splitting algorithm with full-decision information (Dist_PDS_FuDeIn) is designed, eliminating the need for global step-sizes. Furthermore, to address scenarios where players lack access to all other players' decisions, a local estimation is introduced to approximate the decision information of other players, and a fully distributed primal-dual splitting algorithm with partial-decision information (Dist_PDS_PaDeIn) is then proposed. Both algorithms enable the derivation of new distributed forward-backward-like extensions. Theoretically, a new analytical approach for convergence is presented, demonstrating that the proposed algorithms converge to the variational GNE of games, and their convergence rates are also proven, provided that uncoordinated step-sizes are positive and less than explicit upper bounds. Moreover, the approach not only generalizes the forward-backward splitting technique but also improves convergence rates of several well-known algorithms. Finally, the advantages of Dist_PDS_FuDeIn and Dist_PDS_PaDeIn are illustrated through comparative simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"179-194"},"PeriodicalIF":3.2,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1109/TSIPN.2024.3361373
Olle Abrahamsson;Danyo Danev;Erik G. Larsson
We study an opinion dynamics model in which each agent takes a random Bernoulli distributed action whose probability is updated at each discrete time step, and we prove that this model converges almost surely to consensus. We also provide a detailed critique of a claimed proof of this result in the literature. We generalize the result by proving that the assumption of irreducibility in the original model is not necessary. Furthermore, we prove as a corollary of the generalized result that the almost sure convergence to consensus holds also in the presence of a stubborn agent which never changes its opinion. In addition, we show that the model, in both the original and generalized cases, converges to consensus also in $r$ th mean.
{"title":"Strong Convergence of a Random Actions Model in Opinion Dynamics","authors":"Olle Abrahamsson;Danyo Danev;Erik G. Larsson","doi":"10.1109/TSIPN.2024.3361373","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3361373","url":null,"abstract":"We study an opinion dynamics model in which each agent takes a random Bernoulli distributed action whose probability is updated at each discrete time step, and we prove that this model converges almost surely to consensus. We also provide a detailed critique of a claimed proof of this result in the literature. We generalize the result by proving that the assumption of irreducibility in the original model is not necessary. Furthermore, we prove as a corollary of the generalized result that the almost sure convergence to consensus holds also in the presence of a stubborn agent which never changes its opinion. In addition, we show that the model, in both the original and generalized cases, converges to consensus also in \u0000<inline-formula><tex-math>$r$</tex-math></inline-formula>\u0000th mean.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"147-161"},"PeriodicalIF":3.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139739018","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 : 2024-01-22DOI: 10.1109/TSIPN.2024.3356794
Wenwei Liu;Hui Feng;Feng Ji;Bo Hu
The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as “like” or “dislike” for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated. The online signed sampling problem of a graph signal can be formulated as a Markov decision process in a finite horizon. Unfortunately, it is intractable for large size graphs. We propose a low-complexity greedy signed sampling algorithm (GSS) as well as a stopping criterion. Meanwhile, we prove that the objective function is adaptive monotonic and adaptive submodular, so that the performance is close enough to the global optimum with a lower bound. Finally, we demonstrate the effectiveness of the GSS algorithm by both synthesis and realworld data.
{"title":"Online Signed Sampling of Bandlimited Graph Signals","authors":"Wenwei Liu;Hui Feng;Feng Ji;Bo Hu","doi":"10.1109/TSIPN.2024.3356794","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3356794","url":null,"abstract":"The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as “like” or “dislike” for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated. The online signed sampling problem of a graph signal can be formulated as a Markov decision process in a finite horizon. Unfortunately, it is intractable for large size graphs. We propose a low-complexity greedy signed sampling algorithm (GSS) as well as a stopping criterion. Meanwhile, we prove that the objective function is adaptive monotonic and adaptive submodular, so that the performance is close enough to the global optimum with a lower bound. Finally, we demonstrate the effectiveness of the GSS algorithm by both synthesis and realworld data.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"131-146"},"PeriodicalIF":3.2,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732034","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}
This article studies the distributed fusion estimation (DFE) issue for networked multi-sensor systems (NMSSs) with stochastic uncertainties, bandwidth-constrained network and energy-constrained denial-of-service (DoS) attacks. The stochastic uncertainties reflected in both the state and measurement models are characterized by multiplicative noises. For reducing the communication burden, local estimation signals are subject to dimensionality reduction processing. And the improved Round-Robin (RR) protocol is used on the channels from local estimators to the fusion estimator. To reflect the actual situation, the dimensionality reduction strategy is designed from the defender's point of view in the sense of minimum fusion error covariance (FEC). And the attack strategy is designed from the attacker's point of view in the sense of maximum FEC. Then, based on a compensation model, a recursive distributed Kalman fusion estimation algorithm (DKFEA) is proposed. The stability conditions making the mean square error (MSE) for DFE bounded are derived. In the end, the validity of the presented DKFEA is verified by an illustrative example.
{"title":"Protocol-Based Distributed Security Fusion Estimation for Time-Varying Uncertain Systems Over Sensor Networks: Tackling DoS Attacks","authors":"Lijuan Zha;Yaping Guo;Jinliang Liu;Xiangpeng Xie;Engang Tian","doi":"10.1109/TSIPN.2024.3356789","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3356789","url":null,"abstract":"This article studies the distributed fusion estimation (DFE) issue for networked multi-sensor systems (NMSSs) with stochastic uncertainties, bandwidth-constrained network and energy-constrained denial-of-service (DoS) attacks. The stochastic uncertainties reflected in both the state and measurement models are characterized by multiplicative noises. For reducing the communication burden, local estimation signals are subject to dimensionality reduction processing. And the improved Round-Robin (RR) protocol is used on the channels from local estimators to the fusion estimator. To reflect the actual situation, the dimensionality reduction strategy is designed from the defender's point of view in the sense of minimum fusion error covariance (FEC). And the attack strategy is designed from the attacker's point of view in the sense of maximum FEC. Then, based on a compensation model, a recursive distributed Kalman fusion estimation algorithm (DKFEA) is proposed. The stability conditions making the mean square error (MSE) for DFE bounded are derived. In the end, the validity of the presented DKFEA is verified by an illustrative example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"119-130"},"PeriodicalIF":3.2,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695114","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 : 2024-01-19DOI: 10.1109/TSIPN.2024.3355748
Ruyun Tian;Hongyan Xing;Yihan Cao;Huaizhou Zhang
The microtremor survey method (MSM) holds great potential for obtaining subsurface shear wave velocity structures in exploration geophysics. However, the lack of an instant imaging mechanism with local fast computation and processing has become a significant bottleneck hindering the development of MSM. In instant imaging tasks, the computational resources of ordinary nodes employed for imaging are often limited. In this article, we consider a single-point microtremor array network with time-varying wireless channels and stochastic imaging task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability and optimize service quality subject to the long-term data queue stability and average power constraints. We formulate the problem as a the minimum delay problem that jointly determines the binary offloading and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework named LyECCO that combines the Lyapunov optimization and energy consumption optimization, solve the binary offloading problems with very low computational complexity. Simulation results show the feasibility of the LyECCO, which achieves optimal computation performance while stabilizing all queues in the system.
{"title":"Lyapunov-Optimized and Energy-Constrained Stable Online Computation Offloading in Wireless Microtremor Sensor Networks","authors":"Ruyun Tian;Hongyan Xing;Yihan Cao;Huaizhou Zhang","doi":"10.1109/TSIPN.2024.3355748","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3355748","url":null,"abstract":"The microtremor survey method (MSM) holds great potential for obtaining subsurface shear wave velocity structures in exploration geophysics. However, the lack of an instant imaging mechanism with local fast computation and processing has become a significant bottleneck hindering the development of MSM. In instant imaging tasks, the computational resources of ordinary nodes employed for imaging are often limited. In this article, we consider a single-point microtremor array network with time-varying wireless channels and stochastic imaging task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability and optimize service quality subject to the long-term data queue stability and average power constraints. We formulate the problem as a the minimum delay problem that jointly determines the binary offloading and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework named LyECCO that combines the Lyapunov optimization and energy consumption optimization, solve the binary offloading problems with very low computational complexity. Simulation results show the feasibility of the LyECCO, which achieves optimal computation performance while stabilizing all queues in the system.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"83-93"},"PeriodicalIF":3.2,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654439","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 : 2024-01-18DOI: 10.1109/TSIPN.2024.3355747
Arumugam Parivallal;Sangwoon Yun;Yoon Mo Jung
In this paper, we investigate the output feedback containment control problem for multi-agent systems with missing measurements. The primary objective is to design a hybrid-triggered controller that not only reduces the unwanted data transmission but also ensures the required control performance. The proposed hybrid-triggered controller is developed by combining the time-triggered and event-triggered schemes using a Bernoulli random variable. Utilizing Lyapunov stability theory, we derive sufficient conditions to ensure the containment control of the considered multi-agent system. Finally, we verify the derived theoretical results through two numerical examples.
{"title":"Hybrid-Triggered Output Feedback Containment Control for Multi-Agent Systems With Missing Measurements","authors":"Arumugam Parivallal;Sangwoon Yun;Yoon Mo Jung","doi":"10.1109/TSIPN.2024.3355747","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3355747","url":null,"abstract":"In this paper, we investigate the output feedback containment control problem for multi-agent systems with missing measurements. The primary objective is to design a hybrid-triggered controller that not only reduces the unwanted data transmission but also ensures the required control performance. The proposed hybrid-triggered controller is developed by combining the time-triggered and event-triggered schemes using a Bernoulli random variable. Utilizing Lyapunov stability theory, we derive sufficient conditions to ensure the containment control of the considered multi-agent system. Finally, we verify the derived theoretical results through two numerical examples.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"108-118"},"PeriodicalIF":3.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694911","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.2024.3352264
Marom Dadon;Wasim Huleihel;Tamir Bendory
In this paper, we study the problems of detection and recovery of hidden submatrices with elevated means inside a large Gaussian random matrix. We consider two different structures for the planted submatrices. In the first model, the planted matrices are disjoint, and their row and column indices can be arbitrary. Inspired by scientific applications, the second model restricts the row and column indices to be consecutive. In the detection problem, under the null hypothesis, the observed matrix is a realization of independent and identically distributed standard normal entries. Under the alternative, there exists a set of hidden submatrices with elevated means inside the same standard normal matrix. Recovery refers to the task of locating the hidden submatrices. For both problems, and for both models, we characterize the statistical and computational barriers by deriving information-theoretic lower bounds, designing and analyzing algorithms matching those bounds, and proving computational lower bounds based on the low-degree polynomials conjecture. In particular, we show that the space of the model parameters (i.e., number of planted submatrices, their dimensions, and elevated mean) can be partitioned into three regions: the impossible regime, where all algorithms fail; the hard regime, where while detection or recovery are statistically possible, we give some evidence that polynomial-time algorithm do not exist; and finally the easy regime, where polynomial-time algorithms exist.
{"title":"Detection and Recovery of Hidden Submatrices","authors":"Marom Dadon;Wasim Huleihel;Tamir Bendory","doi":"10.1109/TSIPN.2024.3352264","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3352264","url":null,"abstract":"In this paper, we study the problems of detection and recovery of hidden submatrices with elevated means inside a large Gaussian random matrix. We consider two different structures for the planted submatrices. In the first model, the planted matrices are disjoint, and their row and column indices can be arbitrary. Inspired by scientific applications, the second model restricts the row and column indices to be consecutive. In the detection problem, under the null hypothesis, the observed matrix is a realization of independent and identically distributed standard normal entries. Under the alternative, there exists a set of hidden submatrices with elevated means inside the same standard normal matrix. Recovery refers to the task of locating the hidden submatrices. For both problems, and for both models, we characterize the statistical and computational barriers by deriving information-theoretic lower bounds, designing and analyzing algorithms matching those bounds, and proving computational lower bounds based on the low-degree polynomials conjecture. In particular, we show that the space of the model parameters (i.e., number of planted submatrices, their dimensions, and elevated mean) can be partitioned into three regions: the \u0000<italic>impossible</i>\u0000 regime, where all algorithms fail; the \u0000<italic>hard</i>\u0000 regime, where while detection or recovery are statistically possible, we give some evidence that polynomial-time algorithm do not exist; and finally the \u0000<italic>easy</i>\u0000 regime, where polynomial-time algorithms exist.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"69-82"},"PeriodicalIF":3.2,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572632","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：