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automation 5.0: The Key to systems Intelligence and Industry 5.0

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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Online Scheduling in Virtualized TSN Networks via Joint Admission Control and VNF Embedding

Online Scheduling in Virtualized TSN Networks via Joint Admi...

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IEEE International Conference on Communications in China (ICCC)

作者： Yajing Zhang Cailian Chen Chaoqun You Mingyan Li Xinping Guan Tony Q. S. Quek Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China School of Computer Science Fudan University College of Computer Science Chongqing University Chongqing P. R. China Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Introducing virtualization enhances the flexibility of time-sensitive networking (TSN), wherein applications manifest as service function chains comprising a series of virtual network functions (VNFs). However, such virtualized TSN realizes determinacy through global configuration, being too complicated to serve dynamic applications in time. To address this issue, we innovatively propose to achieve TSN scheduling by distributively executing admission control (AC), whereby the scheduling complexity is radically reduced. Specifically, we first build a two-way AC model that captures TSN multi-queue characteristics. Then, we define admissible regions of nodes and links, working as metrics for AC decision-making and enabling feasible TSN scheduling. Built upon this, we propose a joint AC and VNF embedding mechanism, Rapid Admission Control (RapidAC), which consists of two algorithms. The first algorithm responds to dynamic applications rapidly and derives node-mapping solutions by judging nodes’ admissible regions. Based on this, the second algorithm augments the detailed VNF embedding solution according to admissible regions of links. Simulation results show that RapidAC reduces runtime by 90% compared with existing TSN scheduling algorithms.

关键词： Measurement Runtime Scheduling algorithms Service function chaining Heuristic algorithms Simulation Admission control

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Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse

Generative AI-empowered Effective Physical-Virtual Synchroni...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Xu, Minrui Niyato, Dusit Zhang, Hongliang Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Peking University School of Electronics Beijing100871 China Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States 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

ISBN: (纸本)9798350333336

Metaverse seamlessly blends the physical world and virtual spaces through ubiquitous communication and computing equipment and infrastructure. In intelligent transportation systems, the vehicular Metaverse can provide a fully immersive and hyperreal travel experience (e.g., via augmented reality head-up displays, AR-HUDs) to drivers and passengers in autonomous vehicles (AVs) through roadside units (RSUs). However, providing real-time and immersive services requires effective physical-virtual synchronization between AVs and virtual simulators. This paper proposes a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In physical-to-virtual synchronization, digital twin (DT) tasks generated by AVs are offloaded for execution in RSUs with future route generation. In virtual-to-physical synchronization, virtual simulators customize diverse and personalized AR content via generative AI models based on user preferences. Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and virtual simulators for RSUs to provide real-time and effective services. Finally, property analysis and experimental results demonstrate that the proposed mechanism is strategy-proof and adverse-selection free while increasing social surplus. © 2023 IEEE.

关键词： Augmented reality

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Reinforcement Learning with Large Language Models (LLMs) Interaction for Network Services

Reinforcement Learning with Large Language Models (LLMs) Int...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Hongyang Du Ruichen Zhang Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Artificial Intelligence-Generated Content (AIGC)-related network services, especially image generation-based services, have garnered notable attention due to their ability to cater to diverse user preferences, which significantly impacts the subjective Quality of Experience (QoE). Specifically, different users can perceive the same semantically informed image quite differently, leading to varying levels of satisfaction. To address this challenge and maximize network users' subjective QoE, we introduce a novel interactive artificial intelligence (IAI) approach using Reinforcement Learning With Large Language Models Interaction (RLLI). RLLI leverages Large Language Model (LLM)-empowered generative agents to simulate user interactions, thereby providing real-time feedback on QoE that encapsulates a range of user personalities. This feedback is instrumental in facilitating the selection of the most suitable AIGC network service provider for each user, ensuring an optimized, personalized experience.

关键词： Image synthesis Computational modeling Instruments Focusing Reinforcement learning Real-time systems Quality of experience

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Survey on Near-Space information Networks: Channel Modeling, Transmission, and Networking Perspectives

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IEEE Communications Surveys and Tutorials 2025年

作者： Cao, Xianbin Su, Xiaoning Yang, Peng Gao, Yue Wu, Dapeng Oliver Quek, Tony Q. S. Beihang University School of Electronic Information Engineering Beijing100191 China Beihang University School of Electronic Information Engineering China Pengcheng Laboratory China State Key Laboratory of CNS/ATM Beijing100191 China Fudan University School of Computer Science Shanghai200433 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Near-space information networks (NSINs) composed of high-altitude platforms (HAPs) and high-and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quick, robust, and cost-efficient sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control techniques, NSINs have been envisioned as an essential component of the emerging sixth-generation of mobile communication systems. This article reveals some critical issues needing to be tackled in NSINs through conducting experiments and discusses the latest advances in NSINs in the research areas of channel modeling, transmission, and networking from a forward-looking, comparative, and technical evolutionary perspective. In this article, we highlight the characteristics of NSINs and present the promising use cases of NSINs. The impact of airborne platforms’ unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advances in HAP and UAV channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. The promising techniques and communication protocols of the physical (PHY) layer, medium access control (MAC) layer, network layer, and transport layer of NSINs for achieving efficient transmission over NSINs are reviewed. A comprehensive review of the networking techniques of NSINs in network deployment, handoff management, and network management aspects is provided. Besides, experiments have been conducted using practical NSINs to verify the performance of some techniques. Finally, we outline some open issues and promising directions for NSINs deserved for future study and discuss the corresponding challenges. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The Department of Electrical and Computer Engineering University of Waterloo Canada

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS2A) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS2A on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated. © 2024, CC BY.

关键词： Blockchain

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Connection Performance Modeling and Analysis of a Radiosonde Network in a Typhoon

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IEEE Transactions on Communications 2024年

作者： Liu, Hanyi Cao, Xianbin Yang, Peng Xiong, Zehui Quek, Tony Q. S. Wu, Dapeng Oliver Beihang University School of Electronic and Information Engineering Beijing China Pengcheng Laboratory China State Key Laboratory of CNS/ATM Beijing China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore City University of Hong Kong Kowloon Department of Computer Science Hong Kong Hong Kong

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde's three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters is comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km³, the uplink CP approaches 26%, 39%, and 50% in three patterns. © 1972-2012 IEEE.

关键词： Distribution functions

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Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexity and human participation in Next-Generation Networking (NGN), defining appropriate reward functions become challenging. In this article, we explore the applications of Inverse Reinforcement Learning (IRL) in NGN. Particularly, if DRL aims to find optimal solutions to the problem, IRL finds a problem from the optimal solutions, where the optimal solutions are collected from experts, and the problem is defined by reward inference. Specifically, we first formally introduce the IRL technique, including its fundamentals, workflow, and difference from DRL. Afterward, we present the motivations of IRL applications in NGN and survey existing studies. Furthermore, to demonstrate the process of applying IRL in NGN, we perform a case study about human-centric prompt engineering in Generative AI-enabled networks. We demonstrate the effectiveness of using both DRL and IRL techniques and prove the superiority of IRL. © 2024, CC BY.

关键词： Reinforcement learning

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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