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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

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控制理论与应用（英文版） 2013年第1期11卷 27-34页

作者： Xingbo WANG Huanshui ZHANG Minyue FU School of Control Science and Engineering Shandong University School of Electrical Engineering and Computer Science University of Newcastle

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.

关键词： Target tracking Wireless sensor network Maximum likelihood estimation Kalman filtering Fisher information matrix Sensor selection

Spreading dynamics of information on online social networks

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arXiv 2025年

作者： Meng, Fanhui Xie, Jiarong Sun, Jiachen Xu, Cong Zeng, Yutian Wang, Xiangrong Jia, Tao Huang, Shuhong Deng, Youjin Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510275 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Computer and Information Science Southwest University Chongqing400715 China Institute of Neuroscience Technical University of Munich Munich80802 Germany Department of Modern Physics University of Science and Technology of China Hefei230026 China MinJiang Collaborative Center for Theoretical Physics College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development. © 2025, CC BY.

关键词： Tweets

Effective Two-Stage Double Auction for Dynamic Resource Trading in Edge Networks via Overbooking

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arXiv 2025年

作者： Wu, Sicheng Liwang, Minghui Wang, Deqing Wang, Xianbin Wu, Chao Tang, Junyi Li, Li Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China The Department of Electrical and Computer Engineering Western University ON Canada The School of Public Affairs Zhejiang University Hangzhou310027 China School of Information Science and Engineering Lanzhou University Lanzhou China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

To facilitate responsive and cost-effective computing resource scheduling and service delivery over edge-assisted mobile networks, this paper investigates a novel two-stage double auction methodology via utilizing an interesting idea of resource overbooking to overcome dynamic and uncertain nature from edge servers (sellers) and demand from mobile devices (as buyers). The proposed auction integrates multiple essential factors such as social welfare maximization and decision-making latency (e.g., the time for determining winning seller-buyer pairs) reduction, by introducing a stagewise strategy: an overbooking-driven pre-double auction (OPDAuction) for determining long-term cooperations between sellers and buyers before practical resource transactions as Stage I, and a real-time backup double auction (RBDAuction) for handling residual resource demands during actual transactions. In particular, by applying a proper overbooking rate, OPDAuction helps with facilitating trading contracts between appropriate sellers and buyers as guidance for future transactions, by allowing the booked resources to exceed supply. Then, since pre-auctions may cause risks, our RBDAuction adjusts to real-time market changes, further enhancing the overall social welfare. More importantly, we offer an interesting view to show that our proposed two-stage auction can support significant design properties such as truthfulness, individual rationality, and budget balance. Through extensive experiments, we demonstrate good performance in social welfare, time efficiency, and computational scalability, outstripping conventional methods in dynamic edge computing settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

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arXiv 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin School of Informatics Xiamen University Fujian China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China Department of Electrical Engineering University at Buffalo-SUNY NY United States Department of Electrical and Computer Engineering Western University ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

Data Identification Optimal control for Quantized Linear Systems under DoS Attacks

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Xianming Li, Liwei Shen, Mouquan Zhao, Xudong Wang, Qing-Guo Zhu, Zheng Hong Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Beijing Normal University Institute of Ai and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory Irads Department of Computer Science Zhuhai519087 China York University Department of Mechanical Engineering TorontoONM3J 1P3 Canada

This paper is devoted to data identification optimal control for quantized linear systems under denial of service (DoS) attacks. A least-squares algorithm is provided to identify the unknown system matrices using a data-based representation. An output estimation algorithm utilizing identified matrices is supplied to offset DoS attacks. Consequently, an optimal controller is built on the quantized and attacked data by adaptive dynamic programming. Sufficient conditions are presented to guarantee that the resultant closed-loop system is ultimately uniformly bounded. An example is produced to verify the validity of the proposed approach. © 1965-2011 IEEE.

关键词： Optimal control systems

Event-aided Semantic Scene Completion

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arXiv 2025年

作者： Guo, Shangwei Shi, Hao Wang, Song Yin, Xiaoting Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based approaches, which struggle under motion blur, poor lighting, and adverse weather. Event cameras, offering high dynamic range and low latency, address these challenges by providing asynchronous data that complements RGB inputs. We present DSEC-SSC, the first real-world benchmark specifically designed for event-aided SSC, which includes a novel 4D labeling pipeline for generating dense, visibility-aware labels that adapt dynamically to object motion. Our proposed RGB-Event fusion framework, EvSSC, introduces an Event-aided Lifting Module (ELM) that effectively bridges 2D RGB-Event features to 3D space, enhancing view transformation and the robustness of 3D volume construction across SSC models. Extensive experiments on DSEC-SSC and simulated SemanticKITTI-E demonstrate that EvSSC is adaptable to both transformer-based and LSS-based SSC architectures. Notably, evaluations on SemanticKITTI-C demonstrate that EvSSC achieves consistently improved prediction accuracy across five degradation modes and both In-domain and Out-of-domain settings, achieving up to a 52.5% relative improvement in mIoU when the image sensor partially fails. Additionally, we quantitatively and qualitatively validate the superiority of EvSSC under motion blur and extreme weather conditions, where autonomous driving is challenged. The established datasets and our codebase will be made publicly at https://***/Pandapan01/EvSSC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

Adapting Human Mesh Recovery with Vision-Language Feedback

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arXiv 2025年

作者： Xu, Chongyang Huang, Buzhen Zhang, Chengfang Feng, Ziliang Wang, Yangang College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The School of Automation Southeast University Nanjing210096 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China

Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are prone to local minima and depth ambiguity. In this work, we leverage large vision-language models (VLMs) to generate interactive body part descriptions, which serve as implicit constraints to enhance 3D perception and limit the optimization space. Specifically, we formulate monocular human mesh recovery as a distribution adaptation task by integrating both 2D observations and language descriptions. To bridge the gap between text and 3D pose signals, we first train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space using contrastive learning. Subsequently, we employ a diffusion-based framework to refine the initial parameters guided by gradients derived from both 2D observations and text descriptions. Finally, the model can produce poses with accurate 3D perception and image consistency. Experimental results on multiple benchmarks validate its effectiveness. The code will be made publicly available. © 2025, CC BY.

关键词： Mesh generation