Pub Date : 2025-09-04DOI: 10.1109/TSIPN.2025.3604657
Jian Sun;Ruoqi Li;Lei Liu;Jianxin Zhang;Qihe Shan
In this paper, a novel boundary-dependent asynchronous intermittent control scheme is proposed to realize the distributed consensus of multi-agent systems with output delays. Different from most works on intermittent control, this intermittent mechanism allows each agent to asynchronously adjust the intermittent time according to their actual control needs. In this intermittent mechanism, the non-negative real area is divided into three sub-regions through two boundary lines (safety boundary and intermittence boundary) to detect the error states of each agent, and a new intermittent mode is presented to arrange work period and break period by the detected real-time error states. By developing the distributed cascade compensator, a novel intermittent distributed cascade consensus mechanism is designed to ensure that all the agents achieve leader-following consensus. Compared with the current time-dependent mechanisms, the proposed boundary-dependent intermittent control mechanism can adjust work and break periods of each agent asynchronously according to the application needs, under which the multi-agent systems can tolerate more break period and reduce the communication frequency. Finally, numerical simulations are performed to verify our results.
{"title":"A Novel Asynchronous Intermittent Control Approach for Distributed Consensus of Multi-Agent Systems With Output Delays","authors":"Jian Sun;Ruoqi Li;Lei Liu;Jianxin Zhang;Qihe Shan","doi":"10.1109/TSIPN.2025.3604657","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3604657","url":null,"abstract":"In this paper, a novel boundary-dependent asynchronous intermittent control scheme is proposed to realize the distributed consensus of multi-agent systems with output delays. Different from most works on intermittent control, this intermittent mechanism allows each agent to asynchronously adjust the intermittent time according to their actual control needs. In this intermittent mechanism, the non-negative real area is divided into three sub-regions through two boundary lines (safety boundary and intermittence boundary) to detect the error states of each agent, and a new intermittent mode is presented to arrange work period and break period by the detected real-time error states. By developing the distributed cascade compensator, a novel intermittent distributed cascade consensus mechanism is designed to ensure that all the agents achieve leader-following consensus. Compared with the current time-dependent mechanisms, the proposed boundary-dependent intermittent control mechanism can adjust work and break periods of each agent asynchronously according to the application needs, under which the multi-agent systems can tolerate more break period and reduce the communication frequency. Finally, numerical simulations are performed to verify our results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1302-1316"},"PeriodicalIF":3.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255881","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}
Sensor fault diagnosis is a critical issue in Sensor Networks (SNs) since sensor failures could lead to significant errors in data fusion and state estimation. To address this challenge, we propose a trust-enhanced distributed Kalman filter (TeDKF) designed to improve the state estimation performance of SNs under sensor faults. The TeDKF framework incorporates a novel incremental density-based (IDB) clustering mechanism into the distributed diffusion Kalman filter (DDKF) structure, which can support an intermediate-level feature (innovations) exchange and effectively fuses reliable sensor nodes. Unlike conventional clustering schemes, IDB clustering does not rely on majority voting, where more than half of the nodes must be reliable. Instead, it can effectively detect and eliminate faulty sensors even in scenarios where the majority of nodes are compromised. This dynamic clustering builds-up trust by selectively grouping the reliable nodes based on evolving normal system behavior, which is considered as a dynamic trust reference to detect anomalies and isolate faulty sensors irrespective of majority voting. The experimental results show that TeDKF significantly reduces estimation errors and enhances fault tolerance compared to the traditional Kalman filtering technique. It can handle different sensor faults, like bias, drift, noise, and stuck faults, especially in scenarios where most nodes are faulty.
{"title":"Trust-Enhanced Distributed Kalman Filtering for Sensor Fault Diagnosis in Sensor Networks","authors":"Khadija Shaheen;Apoorva Chawla;Pierluigi Salvo Rossi","doi":"10.1109/TSIPN.2025.3606167","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3606167","url":null,"abstract":"Sensor fault diagnosis is a critical issue in Sensor Networks (SNs) since sensor failures could lead to significant errors in data fusion and state estimation. To address this challenge, we propose a trust-enhanced distributed Kalman filter (TeDKF) designed to improve the state estimation performance of SNs under sensor faults. The TeDKF framework incorporates a novel incremental density-based (IDB) clustering mechanism into the distributed diffusion Kalman filter (DDKF) structure, which can support an intermediate-level feature (innovations) exchange and effectively fuses reliable sensor nodes. Unlike conventional clustering schemes, IDB clustering does not rely on majority voting, where more than half of the nodes must be reliable. Instead, it can effectively detect and eliminate faulty sensors even in scenarios where the majority of nodes are compromised. This dynamic clustering builds-up trust by selectively grouping the reliable nodes based on evolving normal system behavior, which is considered as a dynamic trust reference to detect anomalies and isolate faulty sensors irrespective of majority voting. The experimental results show that TeDKF significantly reduces estimation errors and enhances fault tolerance compared to the traditional Kalman filtering technique. It can handle different sensor faults, like bias, drift, noise, and stuck faults, especially in scenarios where most nodes are faulty.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1178-1187"},"PeriodicalIF":3.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073268","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 : 2025-09-01DOI: 10.1109/TSIPN.2025.3604659
V. Sathish;Debraj Chakraborty;Siuli Mukhopadhyay
Conventional mathematical models of infectious diseases frequently overlook the spatial spread of the disease concentrating only on local transmission. However, spatial propagation of various diseases have been noted between geographical regions mainly due to the movement of infectious individuals from one region to another. In this work, we propose generalized linear models to study the graph of dependencies between multiple infection count time series from neighbouring regions. Due to the inherent theoretical and computational difficulties in inferring traditional partial correlation and causality graphs for such multiple count time series data, weakened concepts of correlation and causality of appropriate latent variables are introduced to simplify computation. In order to estimate these latent graphs with tunable sparsity, a novel Monte Carlo expectation and maximization algorithm is used to iteratively maximize an appropriate regularized likelihood function, and asymptotic convergence is established. In addition to simulated data, the algorithm is applied on observed weekly dengue disease counts from each region of an Indian city. The interdependence of various regions in the proliferation of the disease is characterized by the edges of the inferred latent graphs. It is observed that some regions act as epicentres of dengue spread even though their disease counts are relatively low.
{"title":"Latent Graphical Models of Multivariate Count Time Series","authors":"V. Sathish;Debraj Chakraborty;Siuli Mukhopadhyay","doi":"10.1109/TSIPN.2025.3604659","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3604659","url":null,"abstract":"Conventional mathematical models of infectious diseases frequently overlook the spatial spread of the disease concentrating only on local transmission. However, spatial propagation of various diseases have been noted between geographical regions mainly due to the movement of infectious individuals from one region to another. In this work, we propose generalized linear models to study the graph of dependencies between multiple infection count time series from neighbouring regions. Due to the inherent theoretical and computational difficulties in inferring traditional partial correlation and causality graphs for such multiple count time series data, weakened concepts of correlation and causality of appropriate latent variables are introduced to simplify computation. In order to estimate these latent graphs with tunable sparsity, a novel Monte Carlo expectation and maximization algorithm is used to iteratively maximize an appropriate regularized likelihood function, and asymptotic convergence is established. In addition to simulated data, the algorithm is applied on observed weekly dengue disease counts from each region of an Indian city. The interdependence of various regions in the proliferation of the disease is characterized by the edges of the inferred latent graphs. It is observed that some regions act as epicentres of dengue spread even though their disease counts are relatively low.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1163-1177"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073267","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 paper addresses the problem of secure state estimation in distributed sensor networks with communication constraints. We propose a reduced-dimensional coding scheme based on the PredVAR model, which extracts dynamics from high-dimensional measurements while enhancing communication efficiency and privacy. A distributed estimator is developed under the proposed coding framework, and the impact of dimensionality reduction on estimation performance is analyzed. To defend against adversarial inference, we explicitly model a subspace-based eavesdropper and introduce a lightweight, time-varying perturbation strategy using orthogonal transformations. Simulation results demonstrate the effectiveness of our framework in balancing estimation accuracy, communication efficiency, and resilience against eavesdropping attacks.
{"title":"Secure Reduced-Dimensional Coding Scheme for Distributed Estimation With Communication Constraints","authors":"Longyu Li;Wen Yang;Yanfang Mo;Wenjie Ding;Jie Wang;Yang Tang","doi":"10.1109/TSIPN.2025.3603723","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3603723","url":null,"abstract":"This paper addresses the problem of secure state estimation in distributed sensor networks with communication constraints. We propose a reduced-dimensional coding scheme based on the PredVAR model, which extracts dynamics from high-dimensional measurements while enhancing communication efficiency and privacy. A distributed estimator is developed under the proposed coding framework, and the impact of dimensionality reduction on estimation performance is analyzed. To defend against adversarial inference, we explicitly model a subspace-based eavesdropper and introduce a lightweight, time-varying perturbation strategy using orthogonal transformations. Simulation results demonstrate the effectiveness of our framework in balancing estimation accuracy, communication efficiency, and resilience against eavesdropping attacks.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1058-1071"},"PeriodicalIF":3.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021241","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 : 2025-08-28DOI: 10.1109/TSIPN.2025.3603740
Peizhe Li;Cailian Chen;Shanying Zhu;Xinping Guan
In the Industrial Internet of Things (IIoT), multiple sensors are deployed in production sites to perform state estimation of large-scale physical systems, which is important to ensure the stable operation of the production process. Sensors can only transmit quantized local information composed of a finite number of bits, where more quantization bits improve estimation accuracy. However, ensuring the necessary data rate for such data transmission under bandwidth limitations requires larger transmission power, increasing the energy consumption of sensors. To address this trade-off, this paper considers the co-design of estimation and communication, where the data rate and transmission power are jointly allocated to minimize a weighted estimation-communication cost while satisfying the minimum data rate constraint. An $ell _{p}$-box alternating direction method of multipliers (ADMM) based distributed optimization method is designed to solve this mixed-integer nonlinear programming (MINLP) problem, and the global convergence of the proposed method is proved. Moreover, a distributed estimation algorithm is proposed to ensure the convergence of estimation errors with minimum data rates, and the balance of the ultimate bound and convergence rate of estimation errors can be achieved by tuning the estimation gain. A numerical case study in the hot rolling process shows the superiority of the proposed distributed optimization and estimation methods.
在工业物联网（IIoT）中，在生产现场部署多个传感器，对大规模物理系统进行状态估计，这对于保证生产过程的稳定运行至关重要。传感器只能传输由有限位组成的量化局部信息，其中更多的量化位可以提高估计精度。但是，在带宽限制的情况下，要保证这种数据传输所需的数据速率，需要更大的传输功率，增加了传感器的能耗。为了解决这种权衡，本文考虑了估计和通信的协同设计，其中数据速率和传输功率共同分配，以最小化加权估计-通信成本，同时满足最小数据速率约束。针对混合整数非线性规划问题，设计了一种基于$ well _{p}$-box交替方向乘法器（ADMM）的分布式优化方法，并证明了该方法的全局收敛性。此外，为了保证估计误差在最小数据速率下收敛，提出了一种分布式估计算法，并通过调整估计增益来平衡估计误差的最终界和收敛率。热轧过程的数值实例研究表明了所提出的分布式优化和估计方法的优越性。
{"title":"Distributed Optimization for Estimation and Communication Co-Design Under Bandwidth Constraints","authors":"Peizhe Li;Cailian Chen;Shanying Zhu;Xinping Guan","doi":"10.1109/TSIPN.2025.3603740","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3603740","url":null,"abstract":"In the Industrial Internet of Things (IIoT), multiple sensors are deployed in production sites to perform state estimation of large-scale physical systems, which is important to ensure the stable operation of the production process. Sensors can only transmit quantized local information composed of a finite number of bits, where more quantization bits improve estimation accuracy. However, ensuring the necessary data rate for such data transmission under bandwidth limitations requires larger transmission power, increasing the energy consumption of sensors. To address this trade-off, this paper considers the co-design of estimation and communication, where the data rate and transmission power are jointly allocated to minimize a weighted estimation-communication cost while satisfying the minimum data rate constraint. An <inline-formula><tex-math>$ell _{p}$</tex-math></inline-formula>-box alternating direction method of multipliers (ADMM) based distributed optimization method is designed to solve this mixed-integer nonlinear programming (MINLP) problem, and the global convergence of the proposed method is proved. Moreover, a distributed estimation algorithm is proposed to ensure the convergence of estimation errors with minimum data rates, and the balance of the ultimate bound and convergence rate of estimation errors can be achieved by tuning the estimation gain. A numerical case study in the hot rolling process shows the superiority of the proposed distributed optimization and estimation methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1112-1126"},"PeriodicalIF":3.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051008","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 : 2025-08-22DOI: 10.1109/TSIPN.2025.3600831
Yinghao Hong;Yun Chen;Xueyang Meng;Yunfei Guo
This article concentrates on the variance-constrained distributed filtering problem with the constraint of limited bit rates and imperfect measurements for nonlinear time-varying systems. The measurement outputs undergo the phenomena of sensor saturations and nonlinearities occurring in a random way. An encoding-decoding mechanism (EDM) is implemented to regulate the transmission procedures over shared communication network. The main purpose of this article is to formulate a suitable distributed filtering algorithm to enable the fulfillment of both stochastic $H_{infty }$ performance and variance constraint for the resultant filtering error system over a finite horizon. The sufficient conditions are initially established to satisfy the prescribed performance constraints, following which the proper filter parameters are derived by means of the solutions to a sequence of iterative matrix inequalities. Furthermore, based on the variance constraint analysis for filtering error, the genetic algorithm (GA) is utilized to optimize the bit rate allocation among every node by minimizing the value of triggered decoding error. Finally, the validity of the proposed distributed filtering scheme is testified by a numerical example.
{"title":"Variance-Constrained Distributed Filtering Under Limited Bit Rates for Time-Varying Systems","authors":"Yinghao Hong;Yun Chen;Xueyang Meng;Yunfei Guo","doi":"10.1109/TSIPN.2025.3600831","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600831","url":null,"abstract":"This article concentrates on the variance-constrained distributed filtering problem with the constraint of limited bit rates and imperfect measurements for nonlinear time-varying systems. The measurement outputs undergo the phenomena of sensor saturations and nonlinearities occurring in a random way. An encoding-decoding mechanism (EDM) is implemented to regulate the transmission procedures over shared communication network. The main purpose of this article is to formulate a suitable distributed filtering algorithm to enable the fulfillment of both stochastic <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> performance and variance constraint for the resultant filtering error system over a finite horizon. The sufficient conditions are initially established to satisfy the prescribed performance constraints, following which the proper filter parameters are derived by means of the solutions to a sequence of iterative matrix inequalities. Furthermore, based on the variance constraint analysis for filtering error, the genetic algorithm (GA) is utilized to optimize the bit rate allocation among every node by minimizing the value of triggered decoding error. Finally, the validity of the proposed distributed filtering scheme is testified by a numerical example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1100-1111"},"PeriodicalIF":3.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050808","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 : 2025-08-20DOI: 10.1109/TSIPN.2025.3600760
Jiacheng Wu;Zhengchun Zhou;Sheng Zhang;Hongyu Han
In this paper, we develop a noise-resistant primal-dual stochastic gradient-based diffusion algorithm (named NPD-SG) designed to operate effectively in scenarios with link noise. The mean-square analysis indicates that, with enough small step-size $mu$ and forgetting factor $gamma$ in (0, 1), the strategy is stable in terms of mean-square error; by reducing the value of $gamma$, it is possible to maintain a low level of estimation error. Then, we modify the update step for dual variables to address the numerical accumulation problem, resulting in an improved NPD-SG (INPD-SG) algorithm. The theoretical analysis also reveals the impact of this modification on algorithm performance. Finally, several simulations demonstrate the theoretical findings and the effectiveness of the proposed approaches.
{"title":"NPD-SG: A Noise-Resistant Primal-Dual Stochastic Gradient Diffusion Algorithm Over Networks","authors":"Jiacheng Wu;Zhengchun Zhou;Sheng Zhang;Hongyu Han","doi":"10.1109/TSIPN.2025.3600760","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600760","url":null,"abstract":"In this paper, we develop a noise-resistant primal-dual stochastic gradient-based diffusion algorithm (named NPD-SG) designed to operate effectively in scenarios with link noise. The mean-square analysis indicates that, with enough small step-size <inline-formula><tex-math>$mu$</tex-math></inline-formula> and forgetting factor <inline-formula><tex-math>$gamma$</tex-math></inline-formula> in (0, 1), the strategy is stable in terms of mean-square error; by reducing the value of <inline-formula><tex-math>$gamma$</tex-math></inline-formula>, it is possible to maintain a low level of estimation error. Then, we modify the update step for dual variables to address the numerical accumulation problem, resulting in an improved NPD-SG (INPD-SG) algorithm. The theoretical analysis also reveals the impact of this modification on algorithm performance. Finally, several simulations demonstrate the theoretical findings and the effectiveness of the proposed approaches.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1087-1099"},"PeriodicalIF":3.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051007","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 : 2025-08-20DOI: 10.1109/TSIPN.2025.3600766
Luyao Guo;Luqing Wang;Xinli Shi;Jinde Cao
Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations between the communication rounds. Nevertheless, this capability is effective only when the network is sufficiently well-connected and the loss function is smooth. In this paper, we propose a communication-efficient method $textsc {MG-Skip}$ with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology. For any undirected and connected networks, $textsc {MG-Skip}$ allows for the multi-gossip communications to be skipped in most iterations in the strongly convex setting, while its computation complexity is $mathcal {O}(kappa log frac {1}{epsilon })$ and communication complexity is only $mathcal {O}(sqrt{frac {kappa }{(1-rho)}} log frac {1}{epsilon })$, where $kappa$ is the condition number of the loss function, $rho$ reflects the connectivity of the network topology, and $epsilon$ is the target accuracy. The theoretical results indicate that $textsc {MG-Skip}$ achieves provable communication acceleration, thereby validating the advantages of local updates in the nonsmooth setting.
{"title":"A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization","authors":"Luyao Guo;Luqing Wang;Xinli Shi;Jinde Cao","doi":"10.1109/TSIPN.2025.3600766","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600766","url":null,"abstract":"Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations between the communication rounds. Nevertheless, this capability is effective only when the network is sufficiently well-connected and the loss function is smooth. In this paper, we propose a communication-efficient method <inline-formula><tex-math>$textsc {MG-Skip}$</tex-math></inline-formula> with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology. For any undirected and connected networks, <inline-formula><tex-math>$textsc {MG-Skip}$</tex-math></inline-formula> allows for the multi-gossip communications to be skipped in most iterations in the strongly convex setting, while its computation complexity is <inline-formula><tex-math>$mathcal {O}(kappa log frac {1}{epsilon })$</tex-math></inline-formula> and communication complexity is only <inline-formula><tex-math>$mathcal {O}(sqrt{frac {kappa }{(1-rho)}} log frac {1}{epsilon })$</tex-math></inline-formula>, where <inline-formula><tex-math>$kappa$</tex-math></inline-formula> is the condition number of the loss function, <inline-formula><tex-math>$rho$</tex-math></inline-formula> reflects the connectivity of the network topology, and <inline-formula><tex-math>$epsilon$</tex-math></inline-formula> is the target accuracy. The theoretical results indicate that <inline-formula><tex-math>$textsc {MG-Skip}$</tex-math></inline-formula> achieves provable communication acceleration, thereby validating the advantages of local updates in the nonsmooth setting.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1044-1057"},"PeriodicalIF":3.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990041","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 : 2025-08-20DOI: 10.1109/TSIPN.2025.3600826
Yifan Liang;Hongbin Li
This paper considers passive target localization using multiple spatially distributed sensors, each transmitting distinct waveforms to measure line-of-sight (LOS) and non-line-of-sight (NLOS) delays from the passive targets. Since LOS and NLOS measurements are not directly distinguishable, the problem is to identify the LOS measurements when certain sensors are blocked from some targets—without prior knowledge of which sensors or targets are affected—and the total number of targets present in the scene is unknown a priori. Leveraging the fact that targets can be categorized into different levels according to the number of sensors obstructed from them, we propose a hierarchical type-based clustering algorithm (HiTCA), which employs a multi-level search strategy, with each designed to identify one specific level of targets. These searches can be performed in parallel across levels to efficiently identify targets with different extents of LOS blockage. Moreover, we exploit a spread difference among the multi-level search results, which enables us to obtain a reliable inference of the total target number. Extensive computer simulations show that the proposed technique obtains superior performance compared to existing methods in multi-target multipath environments with blockage.
{"title":"Robust LOS Identification for Passive Multi-Target Localization in Multipath Obstructed Environments","authors":"Yifan Liang;Hongbin Li","doi":"10.1109/TSIPN.2025.3600826","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3600826","url":null,"abstract":"This paper considers passive target localization using multiple spatially distributed sensors, each transmitting distinct waveforms to measure line-of-sight (LOS) and non-line-of-sight (NLOS) delays from the passive targets. Since LOS and NLOS measurements are not directly distinguishable, the problem is to identify the LOS measurements when certain sensors are blocked from some targets—without prior knowledge of which sensors or targets are affected—and the total number of targets present in the scene is unknown a priori. Leveraging the fact that targets can be categorized into different <italic>levels</i> according to the number of sensors obstructed from them, we propose a hierarchical type-based clustering algorithm (HiTCA), which employs a multi-level search strategy, with each designed to identify one specific level of targets. These searches can be performed in parallel across levels to efficiently identify targets with different extents of LOS blockage. Moreover, we exploit a <italic>spread</i> difference among the multi-level search results, which enables us to obtain a reliable inference of the total target number. Extensive computer simulations show that the proposed technique obtains superior performance compared to existing methods in multi-target multipath environments with blockage.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1030-1043"},"PeriodicalIF":3.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990042","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 : 2025-08-18DOI: 10.1109/TSIPN.2025.3599777
Xiaoqi Liu;Xiangyu Zuo;Tianrui Chen;Ju H. Park
This paper addresses the prescribed-time synchronization (PTS) problem of complex networks (CNs) under asynchronously aperiodic intermittent dynamic event-triggered control (AAIDE-TC). A novel asynchronous controller is designed by integrating the intermittent control (IC) scheme with the event-triggered control (E-TC) mechanism. By introducing a time-varying function into the controller, the networks’ convergence time can be constrained within any prescribed time. Furthermore, in the E-TC strategy, dynamic and exponential terms are introduced to extend the intervals between triggering events and numerical simulation verifies its effects in reducing control cost. The IC approach adopts an average control rate rather than the conventional minimal control rate, making the synchronization conditions of the networks met more easily. Additionally, a global Lyapunov function is established by adopting Kirchhoff’s Matrix Tree Theorem, thereby relaxing the requirements for coupling matrix. Consequently, a synchronization criterion of the CNs under AAIDE-TC is derived, and its accuracy and validity are verified through a numerical simulation of coupled single-link manipulators.
{"title":"Prescribed-Time Asynchronously Aperiodic Intermittent Dynamic Event-Triggered Control for Synchronization of Complex Networks","authors":"Xiaoqi Liu;Xiangyu Zuo;Tianrui Chen;Ju H. Park","doi":"10.1109/TSIPN.2025.3599777","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3599777","url":null,"abstract":"This paper addresses the prescribed-time synchronization (PTS) problem of complex networks (CNs) under asynchronously aperiodic intermittent dynamic event-triggered control (AAIDE-TC). A novel asynchronous controller is designed by integrating the intermittent control (IC) scheme with the event-triggered control (E-TC) mechanism. By introducing a time-varying function into the controller, the networks’ convergence time can be constrained within any prescribed time. Furthermore, in the E-TC strategy, dynamic and exponential terms are introduced to extend the intervals between triggering events and numerical simulation verifies its effects in reducing control cost. The IC approach adopts an average control rate rather than the conventional minimal control rate, making the synchronization conditions of the networks met more easily. Additionally, a global Lyapunov function is established by adopting Kirchhoff’s Matrix Tree Theorem, thereby relaxing the requirements for coupling matrix. Consequently, a synchronization criterion of the CNs under AAIDE-TC is derived, and its accuracy and validity are verified through a numerical simulation of coupled single-link manipulators.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1151-1162"},"PeriodicalIF":3.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061870","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}
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