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Collaborative Offloading Method for Digital Twin Empowered cloud edge computing on Internet of Vehicles

Tsinghua Science and Technology 2023年第3期28卷 433-451页

作者： Linjie Gu Mengmeng Cui Linkun Xu Xiaolong Xu Changwang School of Honors Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21044China School of Artificial Intelligence Nanjing University of Information and TechnologyNanjing 210044China

Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles applications.Most of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration,but the collaboration between small cell eNodeB(SCeNB)should not be ignored.Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes.The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller,thereby realizing user task offloading positions,sub-channel allocation,and computing resource allocation.Then an algorithm named AUC-AC is proposed,based on the dominant actor–critic network and the auction mechanism.In order to obtain a better command of global information,the convolutional block attention mechanism(CBAM)is used in the digital twin of each SCeNB node to observe its environment and learn strategies.Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.

关键词： digital twin cloud-edge computing reinforcement learning actor-critic network

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Smart additive manufacturing: Current artificial intelligenceenabled methods and future perspectives

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Science China(Technological Sciences) 2020年第9期63卷 1600-1611页

作者： WANG YuanBin ZHENG Pai PENG Tao YANG HuaYong ZOU Jun State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Departmen of Ilndustrial and Systems Engineering The Hong Kong Polyechnic UniversityKowloonHong Kong 999077China

Additive manufacturing(AM) has been increasingly used in production. Because of its rapid growth, the efficiency and robustness of AM-based product development processes should be improved. Artificial intelligence(AI) is a powerful tool that has outperformed humans in numerous complex tasks. AI-enabled intelligent agents can reduce the workforce required to scale up AM production and achieve higher resource utilization efficiency. This study provides an introduction of AI techniques. Then,the current development of AI-enabled AM product development is investigated. Existing intelligent agents are used for problems in product design, process design and production stages. Based on the review, current research gaps and future research directions are identified. To guide future development of more efficient and comprehensive intelligent agents, a smart AM framework based on cloud-edge computing is proposed. Global consideration can be realized in the cloud environment, and a fast response can be achieved at the edge nodes.

关键词： additive manufacturing artificial intelligence product development cloud-edge computing

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Hybrid Multiple Access and Service-Oriented Resource Allocation for Heterogeneous QoS Provisioning in Machine Type Communications

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Journal of Communications and Information Networks 2020年第2期5卷 225-236页

作者： Yanan Liu Xianbin Wang Jie Mei Electrical and Computer Engineering Western UniversityLondonON N6A 3K7Canada

Non-orthogonal multiple access(NOMA)has emerged as one important enabling technology for future wireless communications and services,including machine type communication(MTC).Unfortunately,supporting diverse MTC services and massive connectivity is still challenging due to the very different service requirements,scarce radio resources,limited battery capacity of MTC devices,as well as rapidly changing network conditions.In this paper,a hybrid-multipleaccess(HMA)scheme for service-oriented resource allocation scheme is proposed in supporting diverse MTC services for resource constrained devices and networks.In the proposed scheme,HMA allows MTC devices to choose a suitable type of multiple access technique according to their channel conditions,power constraints,and quality of service(QoS)requirements.To support service-oriented resource allocation,the physical network is firstly sliced into several virtualized networks based on QoS requirements and hardware conditions of MTC devices.A novel utility function integrating network performance and the power consumption in MTC devices is proposed.Furthermore,the resource allocation problem is formulated as an optimization problem to maximize the different utility functions under constraints of user QoS requirements and maximum transmitted power.To improve computational capacity as well as reduce the operational latency,a cloud-edge collaborative scheme is further designed to share the computation loads between the cloud and edge.Simulation results demonstrate the proposed service-oriented resource allocation scheme is effective and illustrate that the proposed hybrid multiple access method provides better performance than NOMA in terms of effective energy efficiency.

关键词： machine type communication network virtualization cloud-edge computing resource allocation hybrid multiple access

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