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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 800-802页

作者： Chao Ren Chunran Zou zehui xiong Han Yu Zhao-Yang Dong Niyato Dusit Nanyang Technological University Singapore 639798Singapore Nanjing University of Aeronautics and Astronautics Nanjing 211106China Singapore University of Technology and Design Singapore 487372Singapore

Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart gri... 详细信息

关键词： Smart smart Dynamic

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu zehui xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

恶意干扰下的无人机辅助边缘计算加权能耗与时延智能优化

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电子与信息学报 2024年第7期46卷 2879-2887页

作者：杨和林郑梦婷刘帅肖亮谢显中熊泽辉厦门大学信息学院厦门361005 重庆邮电大学重庆市计算机网络与通信技术重点实验室重庆400065 新加坡科技设计大学信息系统技术与设计学院新加坡487372

近年来,将移动边缘计算(MEC)服务器搭载在无人机(UAV)上为地面移动用户提供服务备受学术界和工业界广泛的关注。但在恶意干扰环境下,如何有效调度资源降低系统时延和能耗成为关键问题。为此,针对干扰机影响下无人机辅助边缘计算的问题,... 详细信息

近年来,将移动边缘计算(MEC)服务器搭载在无人机(UAV)上为地面移动用户提供服务备受学术界和工业界广泛的关注。但在恶意干扰环境下,如何有效调度资源降低系统时延和能耗成为关键问题。为此,针对干扰机影响下无人机辅助边缘计算的问题,该文建立一个以最小化加权能耗与时延为目标的模型,联合优化无人机飞行轨迹、资源调度和任务分配来提升无人机辅助移动边缘计算系统性能。鉴于优化问题难求解以及恶意干扰行为动态多变,该文提出了一种基于双延迟深度确定性策略梯度(TD3)的资源调度算法,同时结合优先经验回放(PER)机制提高算法收敛速度和稳定性,高效对抗恶意干扰攻击。仿真结果表明所提算法较其他算法,能够有效降低系统的时延和能耗,并具有很好的收敛性与稳定性。

关键词：无人机通信移动边缘计算资源调度抗干扰深度强化学习

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面向多子元宇宙矿工分配的多背包问题优化方案

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电子与信息学报 2024年第5期46卷 2177-2186页

作者：康嘉文吴天昊文锦柏陈俊龙熊泽辉黄旭民刘雷广东工业大学自动化学院广州510006 南京航空航天大学计算机科学与技术学院南京210016 新加坡科技与设计大学信息系统技术与设计学院新加坡487372 西安电子科技大学通信工程学院西安710071

元宇宙是一种新型互联网社会生态,旨在促进用户交流、提供虚拟服务和数字资产交易。区块链作为元宇宙的底层技术,支持非同质化通证(NFT)等数字资产在元宇宙内流通。然而,随着共识节点的增加,数字资产的交易共识效率会降低。因此,该文设... 详细信息

元宇宙是一种新型互联网社会生态,旨在促进用户交流、提供虚拟服务和数字资产交易。区块链作为元宇宙的底层技术,支持非同质化通证(NFT)等数字资产在元宇宙内流通。然而,随着共识节点的增加,数字资产的交易共识效率会降低。因此,该文设计了基于边缘计算和跨链技术的多子元宇宙数字资产交易管理框架,首先,利用跨链技术将多个子元宇宙连接成多子元宇宙系统;其次,将边缘设备以矿工的身份分配到各个子元宇宙中,并利用其空闲的计算资源来提高数字资产交易的效率;此外,将边缘设备分配问题建模为一个多背包问题,并设计了一套矿工选择方案。针对环境动态变化的分配问题,采用深度强化学习中的近端策略优化(DRLPPO)算法,有效解决多子元宇宙中子元宇宙的矿工分配问题。仿真结果验证了所提方法的有效性,能够以安全、高效和灵活的方式实现跨链NFT交易和子元宇宙管理。

关键词：元宇宙区块链多背包问题深度强化学习

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A trustless architecture of blockchain-enabled metaverse

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高置信计算(英文) 2023年第1期3卷 1-7页

作者： Minghui Xu Yihao Guo Qin Hu zehui xiong Dongxiao Yu Xiuzhen Cheng School Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer and Information Science Indiana University-Purdue University IndianapolisUSA Information Systems Technology and Design Pillar Singapore University of Technology and DesignSingapore

Metaverse has rekindled human beings’desire to further break space-time barriers by fusing the virtual and real ***,security and privacy threats hinder us from building a utopia.A metaverse em-braces various techniques,while at the same time inheriting their pitfalls and thus exposing large attack ***,proposed in 2008,was regarded as a key building block of *** enables transparent and trusted computing environments using tamper-resistant decentralized ***,blockchain supports Decentralized Finance(DeFi)and Non-fungible Tokens(NFT)for ***-ever,the power of a blockchain has not been sufficiently *** this article,we propose a novel trustless architecture of blockchain-enabled metaverse,aiming to provide efficient resource integration and allocation by consolidating hardware and software *** realize our design objectives,we provide an On-Demand Trusted Computing Environment(OTCE)technique based on local trust ***,the architecture adopts a hypergraph to represent a metaverse,in which each hyper-edge links a group of users with certain *** the trust level of each user group can be evaluated based on graph analytics *** on the trust value,each group can determine its security plan on demand,free from interference by irrelevant ***,OTCEs enable large-scale and flexible application environments(sandboxes)while preserving a strong security guarantee.

关键词： Metaverse Blockchain Edge computing Trust

6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and Information Networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen zehui xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

Achieving 500X Acceleration for Adversarial Robustness Verification of Tree-Based Smart Grid Dynamic Security Assessment

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

自动化学报（英文版） 2024年第3期

作者： Chao Ren Chunran Zou zehui xiong Han Yu Zhao-Yang Dong Niyato Dusit

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Federated Learning for 6G Communications:Challenges,Methods,and Future Directions

自动化学报（英文版） 2024年第2期

作者： Jiawen Kang Junlong Chen Minrui Xu zehui xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie

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China Communications 2020年第9期17卷 105-118页

作者： Yi Liu Xingliang Yuan zehui xiong Jiawen Kang Xiaofei Wang Dusit Niyato School of Data Science of Technology Heilongjiang UniversityHarbinChina Faculty of Information Technology Monash UniversityAustralia Alibaba-NTU Joint Research Institute and also School of Computer Science and Engineering NTUSingapore Energy Research Institute Nanyang Technological UniversitySingapore College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Engineering NTUSingapore

As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G *** is generally believed that 6G will be established on ubiquitous Artificial Intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale ***,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy *** learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in *** this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for *** then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.

关键词： 6G communication federated learning security and privacy protection

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基于区块链和贝叶斯博弈的联邦学习激励机制

中国科学：信息科学 2022年第6期52卷 971-991页

作者：张沁楠朱建明高胜熊泽辉丁庆洋朴桂荣中央财经大学信息学院北京100081 Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore 北京联合大学管理学院北京100020

联邦学习通过聚合多方本地模型成为数据共享的新模式.现有的联邦学习激励机制有效缓解了完全信息下的数据供给不足问题,但仍面临搭便车、不公平、不可信等挑战.为此,本文提出了一种基于区块链和贝叶斯博弈(Bayesian game)的不完全信息... 详细信息

联邦学习通过聚合多方本地模型成为数据共享的新模式.现有的联邦学习激励机制有效缓解了完全信息下的数据供给不足问题,但仍面临搭便车、不公平、不可信等挑战.为此,本文提出了一种基于区块链和贝叶斯博弈(Bayesian game)的不完全信息联邦学习激励机制,通过量化数据供给方的成本效用与数据需求方的支付报酬对数据交易过程建模,采用沙普利值(Shapley value)实现了数据供给方报酬分配的公平性.在交易模型中考虑到参与个体的异质性与隐私保护,将数据供给方的资源配置策略构建为不完全信息的贝叶斯博弈模型,通过优化本地模型训练策略实现对数据供给方的激励作用.本文进一步分析了激励机制的有效性与行动策略的可信性,提出一种隐私保护的贝叶斯博弈行动策略共识算法(privacy-preserving Bayesian game action strategy consensus algorithm,PPBG-AC),该算法使数据供给方在基于区块链的数据交易平台下实现了贝叶斯纳什均衡.方案对比与理论分析表明本文提出的不完全信息联邦学习激励机制保障了数据供给方利益分配的公平性与资源配置的可信性,基于实际公开数据集的仿真实验与性能评估验证了激励机制的有效性.

关键词：联邦学习激励机制区块链贝叶斯博弈沙普利值不完全信息隐私保护