Pub Date : 2025-03-09DOI: 10.1109/JSYST.2025.3563693
Yun Chen;Qian Zhang;Xueyang Meng;Yunfei Guo
This article makes one of the first few attempts to investigate the multisensor distributed fusion filtering problem for a special type of time-varying nonlinear stochastic cyber-physical systems (CPSs) via encoding–decoding strategy (EDS) within the finite-horizon probability constraint framework. The random EDS is employed to orchestrate the data transmissions between sensors and remote local filters to enhance the resource-utilization efficiency and data security. A novel probability-constrained distributed fusion filtering (DFF) scheme is established such that the prescribed probabilistic ellipsoidal constraints and stochastic $H_{infty }$ disturbance attenuation index are satisfied for the resultant local and fusion filtering errors. Sufficient conditions are firstly presented to guarantee the existence of desired local filters by iteratively solving a sequence of matrix inequalities. Subsequently, the derived multisensor distributed fusion filter is designed by means of a certain optimization problem to maximize the ellipsoidal set constraint probability of the fused filtering error. Finally, a numerical example demonstrates the validity of the proposed distributed fusion filtering approach.
{"title":"Probability-Constrained Multisensor Distributed Fusion Filtering for Cyber-Physical Systems","authors":"Yun Chen;Qian Zhang;Xueyang Meng;Yunfei Guo","doi":"10.1109/JSYST.2025.3563693","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3563693","url":null,"abstract":"This article makes one of the first few attempts to investigate the multisensor distributed fusion filtering problem for a special type of time-varying nonlinear stochastic cyber-physical systems (CPSs) via encoding–decoding strategy (EDS) within the finite-horizon probability constraint framework. The random EDS is employed to orchestrate the data transmissions between sensors and remote local filters to enhance the resource-utilization efficiency and data security. A novel probability-constrained distributed fusion filtering (DFF) scheme is established such that the prescribed probabilistic ellipsoidal constraints and stochastic <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> disturbance attenuation index are satisfied for the resultant local and fusion filtering errors. Sufficient conditions are firstly presented to guarantee the existence of desired local filters by iteratively solving a sequence of matrix inequalities. Subsequently, the derived multisensor distributed fusion filter is designed by means of a certain optimization problem to maximize the ellipsoidal set constraint probability of the fused filtering error. Finally, a numerical example demonstrates the validity of the proposed distributed fusion filtering approach.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"589-599"},"PeriodicalIF":4.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339025","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 : 2025-03-09DOI: 10.1109/JSYST.2025.3565261
Mohamed Elsayed;Ahmed S. Ibrahim;Mahmoud H. Ismail;Ahmed Samir
Radar and communication coexistence (RCC) aims at improving the spectral efficiency of next generation wireless systems. One of the challenges facing RCC, however, is the mutual interference between the two coexisting subsystems. A reconfigurable intelligent surface (RIS) can thus be used to address such a challenge by optimizing its phase shifts along with the beamforming weights at the base station (BS). In this article, we aim to maximize the communication sum rate (SR), while limiting the interference toward the radar to a certain limit. Motivated by the unexplored fact that covariance matrices of RCC signals are Hermitian positive definite (HPD) and hence, can be represented over Riemannian manifolds (i.e., curved surfaces), the RCC SR maximization problem is reformulated as minimization of a Riemannian metric, which is the geodesic distance between the covariance matrices of the radar and the RIS-relayed signals. Such geometric reformulation paves the road for a low-complexity optimization approach over Riemannian manifolds, which simultaneously optimizes the beamforming weights and phase shifts at the BS and RIS, respectively. Simulation results demonstrate that the proposed solution significantly increases the communication SR, while meeting the constraint on the interference toward radar. Equally important, the proposed optimization approach over Riemannian manifolds exhibits a reduced complexity compared to state-of-the-art algorithms over Euclidean spaces.
{"title":"Sum Rate Maximization Over Riemannian Manifolds for RIS-Assisted Coexistence of Radar and Communication Systems","authors":"Mohamed Elsayed;Ahmed S. Ibrahim;Mahmoud H. Ismail;Ahmed Samir","doi":"10.1109/JSYST.2025.3565261","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3565261","url":null,"abstract":"Radar and communication coexistence (RCC) aims at improving the spectral efficiency of next generation wireless systems. One of the challenges facing RCC, however, is the mutual interference between the two coexisting subsystems. A reconfigurable intelligent surface (RIS) can thus be used to address such a challenge by optimizing its phase shifts along with the beamforming weights at the base station (BS). In this article, we aim to maximize the communication sum rate (SR), while limiting the interference toward the radar to a certain limit. Motivated by the unexplored fact that covariance matrices of RCC signals are Hermitian positive definite (HPD) and hence, can be represented over Riemannian manifolds (i.e., curved surfaces), the RCC SR maximization problem is reformulated as minimization of a Riemannian metric, which is the geodesic distance between the covariance matrices of the radar and the RIS-relayed signals. Such geometric reformulation paves the road for a <italic>low-complexity</i> optimization approach over Riemannian manifolds, which simultaneously optimizes the beamforming weights and phase shifts at the BS and RIS, respectively. Simulation results demonstrate that the proposed solution significantly increases the communication SR, while meeting the constraint on the interference toward radar. Equally important, the proposed optimization approach over Riemannian manifolds exhibits a reduced complexity compared to state-of-the-art algorithms over Euclidean spaces.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"495-506"},"PeriodicalIF":4.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308402","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}
Recent research has shown it is possible for groups of robots to automatically “bootstrap” their own collective motion behaviors, particularly movement in a group. However, existing work has primarily provided proof of concept in regular, open arenas without obstacles. For practical applications on real robots, multiple collective motion skills are required. This article proposes a novel, multitask deep reinforcement learning algorithm and domain transfer architecture permitting multiple collective motion skills to be bootstrapped automatically and applied to real robots. The proposed approach is tested for tuning two collective motion skills for grouped movement and obstacle avoidance, without requiring a map of the environment. We show that our approach can tune obstacle avoidance parameters while maintaining high-quality swarming collective behavior when an obstacle is detected. Furthermore, learned collective motion skills can be transferred from a point mass simulation onto real mobile robots using our domain transfer architecture, without loss of quality. Transferability is comparable to that of an evolutionary algorithm run in a high-fidelity simulator.
{"title":"A Dual-Task Deep Reinforcement Learning and Domain Transfer Architecture for Bootstrapping Swarming Collective Motion Skills","authors":"Shadi Abpeikar;Matt Garratt;Sreenatha Anavatti;Reda Ghanem;Kathryn Kasmarik","doi":"10.1109/JSYST.2025.3536783","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3536783","url":null,"abstract":"Recent research has shown it is possible for groups of robots to automatically “bootstrap” their own collective motion behaviors, particularly movement in a group. However, existing work has primarily provided proof of concept in regular, open arenas without obstacles. For practical applications on real robots, multiple collective motion skills are required. This article proposes a novel, multitask deep reinforcement learning algorithm and domain transfer architecture permitting multiple collective motion skills to be bootstrapped automatically and applied to real robots. The proposed approach is tested for tuning two collective motion skills for grouped movement and obstacle avoidance, without requiring a map of the environment. We show that our approach can tune obstacle avoidance parameters while maintaining high-quality swarming collective behavior when an obstacle is detected. Furthermore, learned collective motion skills can be transferred from a point mass simulation onto real mobile robots using our domain transfer architecture, without loss of quality. Transferability is comparable to that of an evolutionary algorithm run in a high-fidelity simulator.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"327-338"},"PeriodicalIF":4.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645180","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 : 2025-03-01DOI: 10.1109/JSYST.2025.3547395
Feisheng Yang;Jiaming Liu;Qian Ma;Xiangpeng Xie
This article concentrates on how to achieve fast distributed optimization for multiple Euler–Lagrangian (EL) systems with unknown parameters while economizing communication resources. First, to tackle the optimal consensus issue for multiple EL systems with unavailable model parameters, a novel auxiliary system is presented as a reference model that can reach the control objective as well as optimize global cost at the predefined time. Second, a predefined-time tracking controller adopting the time-base generator method is developed to track the auxiliary system. It is a new attempt to introduce a predefined-time convergence mechanism into multiple EL systems with unavailable parameters. Third, a dynamic event triggering mechanism is devised, with triggering thresholds governed by internal dynamic variables. In addition, it effectively eliminates the occurrence of Zeno behavior, thereby illustrating the feasibility of the designed triggering method. Finally, the simulation shows the validity and superiority of the proposed distributed scheme.
{"title":"Dynamic Event-Triggered Predefined-Time Distributed Optimal Consensus for Multiple Euler–Lagrangian Systems With Unavailable Parameters","authors":"Feisheng Yang;Jiaming Liu;Qian Ma;Xiangpeng Xie","doi":"10.1109/JSYST.2025.3547395","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3547395","url":null,"abstract":"This article concentrates on how to achieve fast distributed optimization for multiple Euler–Lagrangian (EL) systems with unknown parameters while economizing communication resources. First, to tackle the optimal consensus issue for multiple EL systems with unavailable model parameters, a novel auxiliary system is presented as a reference model that can reach the control objective as well as optimize global cost at the predefined time. Second, a predefined-time tracking controller adopting the time-base generator method is developed to track the auxiliary system. It is a new attempt to introduce a predefined-time convergence mechanism into multiple EL systems with unavailable parameters. Third, a dynamic event triggering mechanism is devised, with triggering thresholds governed by internal dynamic variables. In addition, it effectively eliminates the occurrence of Zeno behavior, thereby illustrating the feasibility of the designed triggering method. Finally, the simulation shows the validity and superiority of the proposed distributed scheme.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"518-528"},"PeriodicalIF":4.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308382","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 : 2025-02-28DOI: 10.1109/JSYST.2025.3529705
Changyi Xu;Yuming Huo;Chunkun Shi;Ying Zhao
It is crucial to monitor the operational status of aeroengines by using the digital twin technology to realize virtual-real synchronization for the gas path system. The challenge is to accurately monitor deep parameters in real-time during synchronization, although existing digital twin technologies have made good progress in monitoring shallow parameters. This study proposes a virtual-real synchronization method for digital twins of an aeroengine gas path system (AGPS) based on deep reinforcement learning (RL). First, the parameters are divided into directly measurable parameters (DMP) and nondirectly measurable parameters (NDMP). Then, different algorithms are applied to different types of parameters. An unscented Kalman filtering algorithm is utilized to aid in the synchronization of the DMP. An RL approach is employed to train parameter inference models for the NDMP. By combining the two algorithms, synchronization between these two parameter classes is achieved. This method excels by integrating the NDMP into the virtual-real synchronization of the AGPS digital twin, concurrently reducing the inference time for this specific segment. Comparative experiments are conducted, and the results indicate an effective improvement in the accuracy of parameter inference with the proposed method. Simultaneously, it ensures real-time and robust parameter inference.
{"title":"Digital Twin Virtual-Real Synchronization for Aeroengine Gas Path System Based on Deep Reinforcement Learning","authors":"Changyi Xu;Yuming Huo;Chunkun Shi;Ying Zhao","doi":"10.1109/JSYST.2025.3529705","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3529705","url":null,"abstract":"It is crucial to monitor the operational status of aeroengines by using the digital twin technology to realize virtual-real synchronization for the gas path system. The challenge is to accurately monitor deep parameters in real-time during synchronization, although existing digital twin technologies have made good progress in monitoring shallow parameters. This study proposes a virtual-real synchronization method for digital twins of an aeroengine gas path system (AGPS) based on deep reinforcement learning (RL). First, the parameters are divided into directly measurable parameters (DMP) and nondirectly measurable parameters (NDMP). Then, different algorithms are applied to different types of parameters. An unscented Kalman filtering algorithm is utilized to aid in the synchronization of the DMP. An RL approach is employed to train parameter inference models for the NDMP. By combining the two algorithms, synchronization between these two parameter classes is achieved. This method excels by integrating the NDMP into the virtual-real synchronization of the AGPS digital twin, concurrently reducing the inference time for this specific segment. Comparative experiments are conducted, and the results indicate an effective improvement in the accuracy of parameter inference with the proposed method. Simultaneously, it ensures real-time and robust parameter inference.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"75-86"},"PeriodicalIF":4.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637895","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 : 2025-02-26DOI: 10.1109/JSYST.2025.3532232
Bilel Ben Saoud;Leïla Nasraoui
Unmanned aerial vehicles (UAVs) play a pivotal role in 5G/6G wireless communication systems due to their deployment flexibility. This article explores optimal UAV positioning to maximize coverage in hybrid aerial–ground communication links. Exploiting a probabilistic line-of-sight (LOS) model, we examine coverage radius behavior in mixed urban and suburban environments to meet specific quality-of-service (QoS) targets. The analysis reveals that the coverage radius expands as the probability of LOS increases, which in turn increases with the UAV height. However, beyond a certain height, path loss becomes dominant, and further increases in altitude negatively impact the coverage radius. By studying the maximum coverage radius for minimum signal strength and spectral efficiency requirements, we numerically determine a configuration space of UAV altitudes and the corresponding maximum radius that satisfies the target QoS. The results illustrate a dual-regime behavior, where coverage increases with altitude up to a certain value, beyond which it declines, indicating the existence of an optimal altitude for reliability. In addition, the analysis of ground surface effects shows that flying over concrete surfaces significantly enhances coverage, offering a radius up to five times larger compared to rough, vegetated surfaces.
{"title":"Coverage Optimization for Reliable UAV-Assisted 5G/6G Communication Systems","authors":"Bilel Ben Saoud;Leïla Nasraoui","doi":"10.1109/JSYST.2025.3532232","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532232","url":null,"abstract":"Unmanned aerial vehicles (UAVs) play a pivotal role in 5G/6G wireless communication systems due to their deployment flexibility. This article explores optimal UAV positioning to maximize coverage in hybrid aerial–ground communication links. Exploiting a probabilistic line-of-sight (LOS) model, we examine coverage radius behavior in mixed urban and suburban environments to meet specific quality-of-service (QoS) targets. The analysis reveals that the coverage radius expands as the probability of LOS increases, which in turn increases with the UAV height. However, beyond a certain height, path loss becomes dominant, and further increases in altitude negatively impact the coverage radius. By studying the maximum coverage radius for minimum signal strength and spectral efficiency requirements, we numerically determine a configuration space of UAV altitudes and the corresponding maximum radius that satisfies the target QoS. The results illustrate a dual-regime behavior, where coverage increases with altitude up to a certain value, beyond which it declines, indicating the existence of an optimal altitude for reliability. In addition, the analysis of ground surface effects shows that flying over concrete surfaces significantly enhances coverage, offering a radius up to five times larger compared to rough, vegetated surfaces.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"65-74"},"PeriodicalIF":4.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637915","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 : 2025-02-24DOI: 10.1109/JSYST.2025.3532508
Antônio Sobrinho Campolina Martins;Leandro Ramos de Araujo;Débora Rosana Ribeiro Penido
This article proposes a new method to optimize the number of clusters (NoC) in the active distance-based clustering multiphase probabilistic power flow (MPPF). The objective is to determine a NoC that highly accurately promotes output variables without overloading the computational time. The method is based on intracluster and intercluster distance evaluations to achieve a good partition. A quasi-convex curve is formed to select the optimal NoC, ensuring an excellent computational time to converge. Tests are carried out using K-means, and simulations are conducted using IEEE unbalanced test feeders. Different input random variables are tested, including correlated and noncorrelated variables, with and without renewable distributed generators. The results prove that the input conditions significantly affect the optimal NoC. Comparisons are made with Monte Carlo simulation to justify the proposed application, showing that the computational time reduction provided by the clustering algorithm reaches up to ∼99% . Since the optimal NoC increases dramatically with the size of the input database, guidelines are proposed to reduce the MPPF dimensionality for more effective probabilistic procedures.
{"title":"Quasi-Convex NoC Optimization in the Active Multiphase Probabilistic Power Flow","authors":"Antônio Sobrinho Campolina Martins;Leandro Ramos de Araujo;Débora Rosana Ribeiro Penido","doi":"10.1109/JSYST.2025.3532508","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532508","url":null,"abstract":"This article proposes a new method to optimize the number of clusters (NoC) in the active distance-based clustering multiphase probabilistic power flow (MPPF). The objective is to determine a NoC that highly accurately promotes output variables without overloading the computational time. The method is based on intracluster and intercluster distance evaluations to achieve a good partition. A quasi-convex curve is formed to select the optimal NoC, ensuring an excellent computational time to converge. Tests are carried out using K-means, and simulations are conducted using IEEE unbalanced test feeders. Different input random variables are tested, including correlated and noncorrelated variables, with and without renewable distributed generators. The results prove that the input conditions significantly affect the optimal NoC. Comparisons are made with Monte Carlo simulation to justify the proposed application, showing that the computational time reduction provided by the clustering algorithm reaches up to ∼99% . Since the optimal NoC increases dramatically with the size of the input database, guidelines are proposed to reduce the MPPF dimensionality for more effective probabilistic procedures.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"294-304"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645228","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 : 2025-02-21DOI: 10.1109/JSYST.2025.3531837
Jianli Qiu;Zhufang Kuang;Zhenqi Huang;Siyu Lin
Mobile edge computing, a prospective wireless communication framework, can contribute to offload a large number of tasks to unmanned aerial vehicle (UAV) mobile edge servers. Besides, the demand for server computational resources increasingly ascends as the volume of processing tasks grows. However, in reality, many devices have similar computing tasks and require the same computing data. Therefore, servers can effectively reduce server computing latency and bandwidth costs by caching task data. This investigation explores task security offloading and data caching optimization strategies in scenarios with multiple interfering devices. With the goal of minimizing the total energy consumption, the UAV trajectories, transmission power, task offloading scheduling strategies, and caching decisions is jointly optimized. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To make this problem solved, the original problem is decomposed into three tiers, and an iterative algorithm named CDSFS which is based on the coordinate descent, successive convex approximation, and flow shop scheduling is proposed. Simulation results demonstrate the stability and superiority of the proposed algorithm.
{"title":"Security Offloading Scheduling and Caching Optimization Algorithm in UAV Edge Computing","authors":"Jianli Qiu;Zhufang Kuang;Zhenqi Huang;Siyu Lin","doi":"10.1109/JSYST.2025.3531837","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3531837","url":null,"abstract":"Mobile edge computing, a prospective wireless communication framework, can contribute to offload a large number of tasks to unmanned aerial vehicle (UAV) mobile edge servers. Besides, the demand for server computational resources increasingly ascends as the volume of processing tasks grows. However, in reality, many devices have similar computing tasks and require the same computing data. Therefore, servers can effectively reduce server computing latency and bandwidth costs by caching task data. This investigation explores task security offloading and data caching optimization strategies in scenarios with multiple interfering devices. With the goal of minimizing the total energy consumption, the UAV trajectories, transmission power, task offloading scheduling strategies, and caching decisions is jointly optimized. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To make this problem solved, the original problem is decomposed into three tiers, and an iterative algorithm named CDSFS which is based on the coordinate descent, successive convex approximation, and flow shop scheduling is proposed. Simulation results demonstrate the stability and superiority of the proposed algorithm.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"96-106"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637913","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 : 2025-02-21DOI: 10.1109/JSYST.2025.3540722
Yuan Wang;Zhenbin Du
This article investigates the model-free fault-tolerant bipartite consensus of multiagent systems under directed topology. The radial basis function neural network (RBFNN)-based fault estimation technique is constructed for acquiring unknown actuator faults information directly, in which the topology structure and the information interaction among agents are adequately considered. Compared with the existing method, updating weights using RBFNN estimation is avoided. By utilizing the obtained fault estimation, a distributed model-free adaptive fault-tolerant control (FTC) strategy is developed to achieve bipartite consensus. Unlike other bipartite consensus control techniques, the constructed FTC mechanism does not require accurate system model and structure information, and uses solely the agents' input/output data. Finally, a simulation is performed to verify the proposed mechanism's efficacy.
{"title":"Data-Driven Fault-Tolerant Bipartite Consensus for Multiagent Systems With Directed Topology","authors":"Yuan Wang;Zhenbin Du","doi":"10.1109/JSYST.2025.3540722","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3540722","url":null,"abstract":"This article investigates the model-free fault-tolerant bipartite consensus of multiagent systems under directed topology. The radial basis function neural network (RBFNN)-based fault estimation technique is constructed for acquiring unknown actuator faults information directly, in which the topology structure and the information interaction among agents are adequately considered. Compared with the existing method, updating weights using RBFNN estimation is avoided. By utilizing the obtained fault estimation, a distributed model-free adaptive fault-tolerant control (FTC) strategy is developed to achieve bipartite consensus. Unlike other bipartite consensus control techniques, the constructed FTC mechanism does not require accurate system model and structure information, and uses solely the agents' input/output data. Finally, a simulation is performed to verify the proposed mechanism's efficacy.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"425-434"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308421","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 : 2025-02-18DOI: 10.1109/JSYST.2025.3532698
Jhonathan Prieto Rojas;Rayan Almazyad;Abdulaziz Al Hayyah;Ahmed Alruhaiman;Mohammed Almusharraf;Suhail Al-Dharrab;Hussein Attia
The underground layers of the Earth contain immense resources that require geophysical surveys. This article presents an end-to-end, self-powered wireless sensor network (WSN) for geophysical surveys. The WSN conducts geophysical surveys in an energy-efficient, portable manner. It includes a sensing element, advanced electronics, data processing and digitization, and wireless transmission with networking capabilities between sensing nodes. The system is equipped with a power management module with solar-powered charging capabilities, allowing for at least six days of effective operation on a few hours' worth of charge. The electronic circuitry performing amplification and filtering provides cut-off frequencies of 8.2–108 Hz, and the sensor node exhibits a sampling frequency of 600 SPS. Furthermore, the system implements power modes (active/sleep) to reduce power consumption, with a nominal power usage of only 650 mW at its maximum. The WSN comprises a multihop implementation with smart routing to ensure power-efficient and reliable data transmission. In addition, message encryption is implemented for enhanced wireless security. A field test was conducted to validate the proposed geophysical data acquisition system. Geophysical signals were detected and wirelessly transmitted over a 200 m2 area employing a network of six nodes to a storage unit, where they were successfully reconstructed and remained stored for later processing and analysis.
{"title":"Self-Powered End-to-End Wireless Sensor Network for Geophysical Explorations","authors":"Jhonathan Prieto Rojas;Rayan Almazyad;Abdulaziz Al Hayyah;Ahmed Alruhaiman;Mohammed Almusharraf;Suhail Al-Dharrab;Hussein Attia","doi":"10.1109/JSYST.2025.3532698","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532698","url":null,"abstract":"The underground layers of the Earth contain immense resources that require geophysical surveys. This article presents an end-to-end, self-powered wireless sensor network (WSN) for geophysical surveys. The WSN conducts geophysical surveys in an energy-efficient, portable manner. It includes a sensing element, advanced electronics, data processing and digitization, and wireless transmission with networking capabilities between sensing nodes. The system is equipped with a power management module with solar-powered charging capabilities, allowing for at least six days of effective operation on a few hours' worth of charge. The electronic circuitry performing amplification and filtering provides cut-off frequencies of 8.2–108 Hz, and the sensor node exhibits a sampling frequency of 600 SPS. Furthermore, the system implements power modes (active/sleep) to reduce power consumption, with a nominal power usage of only 650 mW at its maximum. The WSN comprises a multihop implementation with smart routing to ensure power-efficient and reliable data transmission. In addition, message encryption is implemented for enhanced wireless security. A field test was conducted to validate the proposed geophysical data acquisition system. Geophysical signals were detected and wirelessly transmitted over a 200 m<sup>2</sup> area employing a network of six nodes to a storage unit, where they were successfully reconstructed and remained stored for later processing and analysis.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"107-118"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637883","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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