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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

IEEE Transactions on Vehicular Technology 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Xidian University State Key Laboratory of ISN Xian710071 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehiclesin multi-layer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. © 2025 IEEE.

关键词： Network function virtualization

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Accelerating Stable Matching between Workers and Spatial-Temporal Tasks for Dynamic MCS: A Stagewise Service Trading Approach

arXiv

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arXiv 2025年

作者： Qi, Houyi Liwang, Minghui Wang, Xianbin Fu, Liqun Hong, Yiguang Li, Li Cheng, Zhipeng School of Informatics Xiamen University Fujian China Department of Control Science and Engineering Canada Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Jiangsu China

Designing proper incentives in mobile crowdsensing (MCS) networks represents a critical mechanism in engaging distributed mobile users (workers) to contribute heterogeneous data for diverse applications (tasks). We develop a novel stagewise trading framework to reach efficient and stable matching between tasks and workers, upon considering the diversity of tasks and the dynamism of MCS networks. This framework integrates futures and spot trading stages, where in the former, we propose futures trading-driven stable matching and pre-path-planning (FT-SMP3) for long-term task-worker assignment and pre-planning of workers’ paths based on historical statistics and risk analysis. While in the latter, we investigate spot trading-driven DQN path planning and onsite worker recruitment (ST-DP2WR) mechanism to enhance workers’ and tasks’ practical utilities by facilitating temporary worker recruitment. We prove that our proposed mechanisms support crucial properties such as stability, individual rationality, competitive equilibrium, and weak Pareto optimality theoretically. Also, comprehensive evaluations confirm the satisfaction of these properties in practical network settings, demonstrating our commendable performance in terms of service quality, running time, and decision-making overheads. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk analysis

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Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming

Enhanced cyber-physical system with semantic technologies an...

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International Conference on control Systems and computer science (CSCS)

作者： Sorin N. Ciolofan Monica Drăgoicea Daniel-Cătălin Popeangă Department of Computer Science Politehnica University of Bucharest Bucharest Romania Department of Automatic Control and Systems Engineering Politehnica University of Bucharest Bucharest Romania

An important aspect related to the effects of agricultural activities on the environment is represented by the nutrient loss in water and air (specifically nitrogen). The interactions between catchments hydrological processes, management of farm activities, climate changes and nitrogen losses constitute a complex phenomenon yet not well understood, being an important concern from the sustainable agriculture perspective. Nitrogen can be lost with water as leaching or runoff, or as gas as ammonia volatilization. Nitrous oxide (N2O) is particularly problematic because it is also a powerful greenhouse gas. The goal of the current article is to present innovative digital techniques to advance in understanding of this phenomena through an Information System that integrates Artificial Intelligence techniques such as Semantic Technologies and Machine Learning (ML) into Cyber-Physical Systems (CPS) to support smart farming and sustainable agriculture.

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Tight Collision Probability for UAV Motion Planning in Uncertain Environment

Tight Collision Probability for UAV Motion Planning in Uncer...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tianyu Liu Fu Zhang Fei Gao Jia Pan Department of Mechanical Engineering The University of Hong Kong Institute of Cyber-Systems and Control Zhejiang University Department of Computer Science The University of Hong Kong

Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate these challenges. Recent research on UAV motion planning in static environments has been unable to cope with the rapidly changing surroundings, resulting in trajectories that may not be feasible. Moreover, previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of such environments. This paper proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot's Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, we propose a tight upper bound of the collision probability and evaluate it both exactly and approximately. The approximated solution is used to generate motion primitives as a reference trajectory, while the exact solution is leveraged to iteratively optimize the trajectory for better results. Our method is thoroughly tested in simulation and real-world experiments, verifying its reliability and effectiveness in uncertain environments.

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Universal Recurrent Traffic Flow Model for Urban Traffic Simulation

Universal Recurrent Traffic Flow Model for Urban Traffic Sim...

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2021 International Conference of Technology, science and Administration, ICTSA 2021

作者： Sofronova, Elena Alnowaini, Ghazi Russian Academy of Sciences Department of Robotics Control Federal Research Center Computer Science and Control of Moscow Russia Taiz University Department of Mechatronic Engineering Taiz Yemen

ISBN: (纸本)9781665428620

This paper presents a universal recurrent traffic flow model designed to simulate traffic flows and solve the problem of finding the optimal durations of traffic light phases. The model belongs to the class of microscopic and is based on the theory of controlled networks. The road network is presented as a configurable graph. The nodes of the graph are the road sections, and the edges are the maneuvers between the sections. The graph configuration is changed by turning the graph edges on or off, which corresponds to the permission or prohibition of certain maneuvers. Traffic flows are controlled by traffic lights at regulated intersections. To apply this model it is assumed that the information about the parameters of flows and the road network is available. The description of the model, its properties, and a methodological example of calculation are given. © 2021 IEEE.

关键词： Roads and streets

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Graph-based semantic planning for adaptive human-robot-collaboration in assemble-to-order scenarios

Graph-based semantic planning for adaptive human-robot-colla...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ruidong Ma Jingyu Chen John Oyekan Department of Automatic Control and System Engineering University of Sheffield Sheffield United Kingdom Department of Computer Science University of York York United Kingdom

Assemble-to-Order (ATO) has become a popular production strategy for the increasing demand for mass-customized manufacturing. In order to facilitate a flexible and automated Human-Robot-Collaboration system for ATO, we propose a Learning from Demonstration (LfD) framework based on 2D videos in this paper. We initially combine temporal hand motions with spatial hand-object interactions to detect assembly actions. Therefore, an assembly graph can be constructed using classified action sequences. Compared to previous studies on task planning for robots, our graph-based semantic planner can directly learn the demonstrated task structure and thus produce more detailed assistive robot actions for more effective collaboration. We validate our approach by applying it to a real-world ATO problem. The results demonstrated that our proposed system can produce actions adaptively in response to varying human action sequences, as well as guide human assembly when the robot is not involved. Our approach also shows generalizability to unseen human action sequences.

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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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SSIE-Diffusion: Personalized Generative Model for Subject-Specific Image Editing

SSIE-Diffusion: Personalized Generative Model for Subject-Sp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lin Guo Meiling Liu Kaiqun Fu Jiyun Zhou College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Electrical Engineering and Computer Science South Dakota State University South Dakota USA d Lieber Institute Johns Hopkins University Baltimore USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Large-scale Text-to-Image (T2I) models can generate high-quality images by controlling the synthesis based on textual prompts. However, concepts related to specific subjects are often challenging to accurately describe through text, leading to the model's inability to generate diverse images containing these specific concepts. Current methods exhibit shortcomings, including stringent requirements for input images, poor model generalization, uncontrollable image generation quality, lack of editing flexibility and unbalanced semantic consistency. In this paper, we propose a personalized generation method for targeted subject image editing, referred to as SSIE-Diffusion. We address these challenges by training an image encoder to invert original images into text embeddings, ensuring the controllability and accuracy of concept generation. To mitigate overfitting issues during training, we devise a mask loss. Additionally, building upon the T2I model, we propose fine-tuning the U-Net architecture in the model using a regularized dataset. This involves improving the projection matrices mapping to key and value features in the cross-attention layers, enhancing the visual fidelity of personalized images of the subject, and simultaneously addressing language drift issues to balance semantic consistency. Experimental results demonstrate the effectiveness of our proposed method in editing specific subjects within individual images, surpassing current SOTA methods. Across various scenarios, the generated images exhibit superior visual fidelity and editing flexibility, while maintaining high computational efficiency.

关键词： Training Visualization Image synthesis Computational modeling Semantics Neural networks Text to image

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